The beauty of machine learning is that in almost any area, you should be able to find a problem where it would be interesting to try to apply machine learning. Recent years's course projects from Andrew Ng's CS229 class at Stanford are a good example of this. There is a lot of breadth:

• http://cs229.stanford.edu/projects2010.html
• http://cs229.stanford.edu/projects2011.html
  

So it really depends on your interests, and the best thing would be for you to design a problem that you're interested in, then start trying different approaches for solving it. Having said that, here are some places I would think about starting if I were in your shoes. (Note: if you add comments or clarifications, I will update this answer).

Cool data sets (this is just a tiny subset):

• A huge database of pretty well-labeled images: http://www.image-net.org/
• Data from perhaps the most popular object classification/detection/segmentation competition: http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2011/index.html#introduction
• Try something a bit different -- predict the results of the college basketball "March Madness" tournament: http://blog.smellthedata.com/2011/03/official-2011-march-madness-predictive.html

Tools:

• I strongly recommend downloading and playing with Theano; it will allow you to experiment with more variations of a model with much less pain: http://deeplearning.net/software/theano/

Other:

• I've written about a related topic here, and a few people have added to the discussion in the comments: http://blog.smellthedata.com/2010/07/choosing-first-machine-learning-project.html

I will assume that you are not talking about building software to implement machine learning algorithms from scratch but rather packaging ML libraries into a data pipeline (R, python scikit-learn, Spark...).

Some patterns :

Async processing using queues

Machine learning systems are complex and then can be quite unpredicatable in terms of latency. Depending on your data, optimums can be found more or less easily. You also want to build a system that is able to A/B test different algorithms, so latency may vary depending on its complexity (logistic reg VS random forest is an example). Finally, the more you get into very complex algorithms (NN, stacking...), the less your workload will look synchronous. Using queues such as kafka also helps you to build distributed systems to add more workers if your data is big enough.

Hidden Feedback Loop & data channels

This is for systems where your prediction has an influence on its own verification.

The hidden feeback loop is a well known phenomenon explained by the fact that if you influence an experience you can't learn from it at the same time. A classic example is the police patrol recommendation system. If you always predict that something is going to happen somewhere and send police there, they will only be able to arrest people there and not in other zones. Your predictions will then be confirmed, your algorithm will learn from that and will be biased more and more. A solution to this phenomenon is to split your data in two or more channels : the data you learn from and the data you predict on. For the data you learn from you make a random prediction, so you don't get biased, and see its result later to learn from it. For the other set of the data, you make your best prediction to maximize ROI/you name it. You then need a system able to split your data to various algorithms (random, algorithm A, algorithm B...), and to split it on some conditions (10% of the input, balanced sampling between targets or variables...). A good way to implement that is... queues.

Hashing trick and handling novelty

The hashing trick is a VERY handy solution for machine learning architectures (it's even better than queues...imagine). Basically, when you train a machine learning algorithms and want to get predictions from it, it will wait for a defined number of variables. This number is precisely the number of variables that it has seen on the training set. What if a new variable comes in your system after an upgrade? Or a new value for a categorical variables? Hashing trick is a very robust solution to that. The idea is that you define a number of variables that will come in your system (200 000 for example), you cast every single data to an int (for example you create a string age_22 if the next line is someone 22 and hash it to an int) then you compute the modulo of that int upon 200 000. The number you get is the number of the column for your value, you put 1 in this column. On the other side you get sparse data, which may not be the best for some algorithms like Decision trees...

This is very handy, also because anyway you will have to transform your categorical variables into a matrix of N columns with N the number of possible values. For example :

N is often changing, the order of the variables may also change and so on...you don't want to manage that. A great ML framework using this trick is vowpal wabbit, which is focused on online learning : JohnLangford/vowpal_wabbit

(Fake) Lambda architecture

Except if you go for online learning (update the model after every example), you will have to re-train your model regularly and cross-validate its performance. On the other hand you need your model to be able to predict over real-time or simply incoming data. That means you need two layers. One where the data goes for predictions, one where it is historicized and used for regular trainings. There will be a lot of code in common, such as your processes for feature creation, data cleaning, configuration for the machine learning libraries and so on... There is great litterature on the lambda architecture, but the general idea here is to write code that is both able to run in a streaming and a batch manner.

The "fake" disclaimer is because you don't have a serving layer (and most lambda architectures people talk about are not lambda architectures... but that's for another day!)

Kappa architecture

I like kappa. It's an architecture proposed to handle the needs i just described above, but without the complexity of a real lambda architecture (writing everything twice in a streaming and a batch framework, building a serving layer that glues the time windows..). The idea is that you only build a streaming architecture and use it both for real time predictions and to replay big batches.

Questioning the Lambda Architecture

Caching joins for incoming data

A common task in machine learning is to add variables to the example you are trying to predict against. Sometimes it can be quite long or complex to calculate them, or it can be well above your latency prerequisites. Preparing these features in advance is a good pattern. Let's say you have a client action coming into your streaming system and you want to add information about his previous purchases (2 month average, maximum purchase of his city...). You can compute them in batch and upload them in a key value cache (redis, in-memory hashmap...). You simply join it with the client id when the client is coming in the streaming system.

Also, generally speaking :

• Keep things simple, use well understood frameworks
• Don't try to scale if you don't need to
• Think about the functional perimeter of your application before thinking about a framework (Spark for example)
• I prefer a well written bunch of python lines rather than a mess of big data technologies put together (kafka spark cassandra elastic search wombocombo)
• Think about how easily your system is allowing your R&D team to put their model in production. If they code in R and you build a custom scala code for every algorithm they suggest you won't be much agile. Try to use the same frameworks for R&D and production, and if you don't, try to use standard tools to translate the models from one language to the another (PMML for example).

I don't know of any, although that doesn't mean they don't exist.

There are workflows guidelines in solving a problem through the analysis of data (let's use this as the basics of solving a data science problem - there's often more that we'll ignore to keep it simple), but, since there are so many problems and so many different kinds of solutions (e.g. ML algorithms, statistical models, etc.) and even more kinds of data... they have to be taken with a grain of salt. In any given situation, things change, and the plan has to be adapted. Of course, you could have a workflow for all problems, but it would be so general it wouldn't tell you much.

Since much of the use of machine learning is solving data science (and related) problems, let's call "machine learning" my personal favourite part of machine learning: research and development. And there are ways to do this, generically speaking, methods in which to approach this. But no design patterns.

You may have hear about the Cross Industry Standard Process for Data-mining.

It's really a simple way of thinking things, but it helps you to identify tasks and resources necessary for data mining projects.

I really think that obtaining a well expressed question from business expert is the key to a good data-mining project. There are plenty of methods and good statisticians who may be able to answer a question, once it's well explained and, of course, assuming you have the data.

Lots of good answers already - however the question is such that I think perhaps a business rather than technical description might be warranted.

First things first, doing stuff with data, whatever you want to call it is going to require some investment - fortunately the entry price has come right down and you can do pretty much all of this at home with a reasonably priced machine and online access to a host of free or purchased resources. Commercial organizations have realized that there is huge value hiding in the data and are employing the techniques you ask about to realize that value. Ultimately what all of this work produces is insights, things that you may not have known otherwise. Insights are the items of information that cause a change in behavior.

Let's begin with a real world example, looking at a farm that is growing strawberries (here's a simple backgrounder The Secret Life Of California's World-Class Strawberries, this High-Tech Greenhouse Yields Winter Strawberries , and this Growing Strawberry Plants Commercially)

What would a farmer need to consider if they are growing strawberries? The farmer will be selecting the types of plants, fertilizers, pesticides. Also looking at machinery, transportation, storage and labor. Weather, water supply and pestlience are also likely concerns. Ultimately the farmer is also investigating the market price so supply and demand and timing of the harvest (which will determine the dates to prepare the soil, to plant, to thin out the crop, to nurture and to harvest) are also concerns.

So the objective of all the data work is to create insights that will help the farmer make a set of decisions that will optimize their commercial growing operation.

Let's think about the data available to the farmer, here's a simplified breakdown:

1. Historic weather patterns

2. Plant breeding data and productivity for each strain

3. Fertilizer specifications

4. Pesticide specifications

5. Soil productivity data

6. Pest cycle data

7. Machinery cost, reliability, fault and cost data

8. Water supply data

9. Historic supply and demand data

10. Market spot price and futures data

Now to explain the definitions in context (with some made-up insights, so if you're a strawberry farmer, this might not be the best set of examples):

Big Data: Using all of the data available to provide new insights to a problem. Traditionally the farmer may have made their decisions based on only a few of the available data points, for example selecting the breeds of strawberries that had the highest yield for their soil and water table. The Big Data approach may show that the market price slightly earlier in the season is a lot higher and local weather patterns are such that a new breed variation of strawberry would do well. So the insight would be switching to a new breed would allow the farmer to take advantage of a higher prices earlier in the season, and the cost of labor, storage and transportation at that time would be slightly lower. There's another thing you might hear in the Big Data marketing hype: Volume, Velocity, Variety, Veracity - so there is a huge amount of data here, a lot of data is being generated each minute (so weather patterns, stock prices and machine sensors), and the data is liable to change at any time (e.g. a new source of social media data that is a great predictor for consumer demand),

Data Analysis: Analysis is really a heuristic activity, where scanning through all the data the analyst gains some insight. Looking at a single data set - say the one on machine reliability, I might be able to say that certain machines are expensive to purchase but have fewer general operational faults leading to less downtime and lower maintenance costs. There are other cheaper machines that are more costly in the long run. The farmer might not have enough working capital to afford the expensive machine and they would have to decide whether to purchase the cheaper machine and incur the additional maintenance costs and risk the downtime or to borrow money with the interest payment, to afford the expensive machine.

Data Analytics: Analytics is about applying a mechanical or algorithmic process to derive the insights for example running through various data sets looking for meaningful correlations between them. Looking at the weather data and pest data we see that there is a high correlation of a certain type of fungus when the humidity level reaches a certain point. The future weather projections for the next few months (during planting season) predict a low humidity level and therefore lowered risk of that fungus. For the farmer this might mean being able to plant a certain type of strawberry, higher yield, higher market price and not needing to purchase a certain fungicide.

Data Mining: this term was most widely used in the late 90's and early 00's when a business consolidated all of its data into an Enterprise Data Warehouse. All of that data was brought together to discover previously unknown trends, anomalies and correlations such as the famed 'beer and diapers' correlation (Diapers, Beer, and data science in retail). Going back to the strawberries, assuming that our farmer was a large conglomerate like Cargill, then all of the data above would be sitting ready for analysis in the warehouse so questions such as this could be answered with relative ease: What is the best time to harvest strawberries to get the highest market price? Given certain soil conditions and rainfall patterns at a location, what are the highest yielding strawberry breeds that we should grow?

Data Science: a combination of mathematics, statistics, programming, the context of the problem being solved, ingenious ways of capturing data that may not be being captured right now plus the ability to look at things 'differently' (like this Why UPS Trucks Don't Turn Left ) and of course the significant and necessary activity of cleansing, preparing and aligning the data. So in the strawberry industry we're going to be building some models that tell us when the optimal time is to sell, which gives us the time to harvest which gives us a combination of breeds to plant at various times to maximize overall yield. We might be short of consumer demand data - so maybe we figure out that when strawberry recipes are published online or on television, then demand goes up - and Tweets and Instagram or Facebook likes provide an indicator of demand. Then we need to align demand data up with market price to give us the final insights and maybe to create a way to drive up demand by promoting certain social media activity.

Machine Learning: this is one of the tools used by data scientist, where a model is created that mathematically describes a certain process and its outcomes, then the model provides recommendations and monitors the results once those recommendations are implemented and uses the results to improve the model. When Google provides a set of results for the search term "strawberry" people might click on the first 3 entries and ignore the 4th one - over time, that 4th entry will not appear as high in the results because the machine is learning what users are responding to. Applied to the farm, when the system creates recommendations for which breeds of strawberry to plant, and collects the results on the yeilds for each berry under various soil and weather conditions, machine learning will allow it to build a model that can make a better set of recommendations for the next growing season.

I am adding this next one because there seems to be some popular misconceptions as to what this means. My belief is that 'predictive' is much overused and hyped.

Predictive Analytics: Creating a quantitative model that allows an outcome to be predicted based on as much historical information as can be gathered. In this input data, there will be multiple variables to consider, some of which may be significant and others less significant in determining the outcome. The predictive model determines what signals in the data can be used to make an accurate prediction. The models become useful if there are certain variables than can be changed that will increase chances of a desired outcome. So what might be useful for our strawberry farmer to want to predict? Let's go back to the commercial strawberry grower who is selling product to grocery retailers and food manufacturers - the supply deals are in tens and hundreds of thousands of dollars and there is a large salesforce. How can they predict whether a deal is likely to close or not? To begin with, they could look at the history of that company and the quantities and frequencies of produce purchased over time, the most recent purchases being stronger indicators. They could then look at the salesperson's history of selling that product to those types of companies. Those are the obvious indicators. Less obvious ones would be the what competing growers are also bidding for the contract, perhaps certain competitors always win because they always undercut. How many visits the rep has paid to the prospective client over the year, how many emails and phone calls. How many product complaints has the prospective client made regarding product quality? Have all our deliveries been the correct quantity, delivered on time? All of these variables may contribute to the next deal being closed. If there is enough historical data, we can build a model that will predict that a deal will close or not. We can use a sample of the historic data set aside to test if the model works. If we are confident, then we can use it to predict the next deal

[Update June 19, 2017 - just discovered: Farmers Business Network (FBN) Farmers Business Network is proudly Farmers First SM. Created by farmers for farmers, FBN is an independent and unbiased farmer-to-farmer network of thousands of American farms. FBN democratizes farm information by making the power of anonymous aggregated analytics available to all FBN members. The FBN Network helps level the playing field for independent farmers with unbiased information, profit enhancing farm analysis, and network buying power.]

1. MapReduce - A programming model for processing large data sets in parallel across a cluster of computers.
2. Lambda Architecture - A design pattern for building big data systems that balances the need for low latency and high throughput.
3. Data Partitioning - The process of dividing a large data set into smaller, manageable pieces for parallel processing.
4. Distributed File Systems - Used to store and manage large amounts of data across multiple nodes in a distributed manner.
5. Stream Processing - A design pattern for processing data in real-time as it is generated, rather than in batch mode.
6. CQRS (Command Query Responsibility Segregation) - A pattern that separates the responsibilities of reading and writing data in a big data system.
7. Microservices Architecture - A design pattern that breaks down a monolithic application into smaller, independent services that communicate through APIs.
8. Materialized Views - A precomputed data structure that stores the results of a query for fast and efficient retrieval.

In my experience:

A certain few of the GOF design patterns show up a lot, in most cases programmers use them without consciously deciding to “use a design pattern”.

• singleton: most often this is nothing more than a global variable in OOP clothing - a static method to get. People will always need global variables.
• iterator pattern: used in almost every standard library with containers, and that’s not counting “incremented pointers”, even though that often can provide a similar interface.
• strategy pattern: often using some form of language-provided dynamic dispatch (virtual, or functor). Picking behavior at run time is a very powerful tool, even if at some level it just boils down to branching, switching, or looking up.
• Factory pattern: essentially just the strategy pattern applied to object creation, usually achieved in the same ways.
• observer pattern: nearly every game engine or other system with “tasks” will have something similar. Some forms of reference counting and concurrency even imply its use.
• visitor pattern: the popularity of observer and strategy pattern often results in many uses of the visitor pattern: “I have a list of objects I need to inform of an event, but the objects are generic. I’ll have them all share some common interface so I can iterate over them and call the right method for each.”

The rest absolutely do show up from time to time, and some probably should show up more often than they do, but these ones have made it into almost every piece of software. Maybe the command pattern deserves an honorary mention on it’s applicability to HTTP requests and Rest APIs.

The problems with neural networks are

• they are bad at explaining how they arrived at their solution
• it is hard to extract that from the weights and topology of the NN.

There are of course more forms of machine learning that can do better on these points.

Without a doubt, machine learning can discover (greedy) algorithms to solve certain problems with 100% guaranteed success (given enough time and electricity etc), or can discover new strategies, heuristics or methods that (often) converge quickly and give good approximations otherwise.

This does not apply only to design patterns, but to a much wider range of problems. Any problem that can be cracked with intelligence will benefit from having more of the latter.

There are problems for which not only no good algorithms are known, but for which we think such algorithms do not exist. You can only solve these by exploring the entire solution space. Intelligence will be of little help here. Possible examples are: finding useful patterns in prime numbers or in the decimals of PI or e (the base of the natural logarithm).

The term “pattern” is rather generic.

For example you don’t have to be no scientist to discern the pattern in the sequence 100000, 010000, 001000, 000100, 000010 and 000001. In this trivial example the term pattern makes perfect sense. Sometime this trivial example generalizes. For ham/spam classification often a simple patterns like the count of certain objects [sic] patterns (sometime called “majority rule”) is an effective method to classify ham from spam.

For highly complex model, one with many degrees of freedoms (lots of parameters) a pattern is lost to the eye and an argument utilizing model abstractions in terms of algorithm, cost function, etc is an apt substitute. Often knowledge representation, encoding, feature generation is a fundamental part of the algorithm in which case the pattern is hidden behind additional levels of indirection (“a pointer to a pointer to a pointer.”)

Frankly, I’m not enamored with “patterns”. In ML I prefer just algorithm and in statistics I prefer model. Often a model is of hierarchical nature in which the plural models is apt. Now we’re in danger of falling into an infinite descent but I am no philosopher and good with our standard metaphors (as long as we know what we’re talking about.)

I had the same question on my mind exactly an year ago. One of the most difficult tasks keeping in mind your aim to publish a paper by the end of 2 months is to make a sound problem statement. A lot of groundwork goes into coming out with a reasonable and new problem statement which could be solved.

The only way to come out with a problem statement is to read voraciously about the concerned topic. You have to keep reading a lot of research papers, good blogs, solve problems from Kaggle,etc to understand the subject better. While you are pursuing all the above mentioned tasks, you might come across a new problem or a new improved way to solve an existing problem. So, I would suggest that your sole focus should be to enhance your skills in the above subjects, the project idea will strike you sooner or later.

Though this does not answer your question directly but this is the only way to find a research project, you have to spend a lot of time researching !
All the best :)

The task of sequential pattern mining is a data mining task specialized for analyzing sequential data to discover sequential patterns… More precisely it consists of discovering interesting subsequences in a set of sequences, where the interestingness of a subsequence can be measured in terms of various criteria like occurrence frequency, length….

To do sequential pattern mining, a user must provide a sequence database and specify a parameter called minimum support threshold… This parameter indicates a minimum number of sequences in which a pattern must appear to be considered frequent..

Sequential pattern is useful for analyzing sequential data.. Some classic algorithms used for are Prefixspan, spade, GSP etc…

Hope it's useful…

I had been wanting to take a stab at this one since a few days, but it always looked like an enormous task, because this question has used too many words. In addition, this is a question on which a lot of people have their eyes, and a lot of others have already written elaborate answers.

Let me first re-order all the important words:

• Big data
• Data mining
• Data analysis
• Analytics
• Machine learning
• Data science

Imagine that you want to become a data scientist, and work in a big organization like Amazon, Intel, Google, FB, Apple and so on.

How would that look like?

• You would have to deal with big data, you would have to write computer programs in SQL, Python, R, C++, Java, Scala, Ruby…and so on, to only maintain big-data databases. You would be called a database manager.
• As an engineer working on process control, or someone wanting to streamline operations of the company, you would perform Data Mining, and Data Analysis; You may use simple software to do this where you would only run a lot of codes written by others, or you may be writing your elaborate codes in SQL, Python, R and you would be doing data mining, data cleaning, data analysis, modeling, predictive modeling and so on.
• All this will be called Analytics. Several software exist to do this. One popular one is Tableau. Some others are JMP and SAS. Lot of people do everything online where a SAP based business intelligence setup can be used. Here, simple reporting can be done easily.
• Further, you would then be able to use machine learning to derive conclusions, and come up with predictions, wherever analytical answers are not possible. Think of analytical answers as [If/then] type of computer programs, where all the input conditions are already known, and only a few parameters change.
• Machine learning uses statistical analysis to partition data. An example would be this: Read the comments written by various people on Yelp, and predict from the comments whether the person would have marked a restaurant 4 star or 5 star.
• If that is not enough, you would be able to use deep learning as well. Deep learning is used to process data such as musical files, images, even text data such as natural languages, where data are enormous, but their type is very diverse.
• You would use everything to your advantage ~ analytical solutions, partitioning data, hacking mindset, automation by programming, reporting, deriving conclusions, making decisions, taking actions, and telling stories about your data.
• Last but not the least, a part of this will be happening on cruise control, where you may not be there physically, but the programs you may have created would do most of the things themselves. Probably if you take it to the level of AI, one day it may get smarter than you, needless to say it would already be faster than you. One day it can go to the level that it can surprise you with the solutions that you may not even have imagined.
• Now you are a data scientist, and what you would do is called Data-science.
• Whatever you would do may or may not be seen by people outside your company such as people asking Alexa various questions if you work for Amazon, or people asking questions to ok Google if you work for Google. Or they may not be getting to see anything you do. Your functions would be helping the companies engineer things better.
• To do all this, you may need lots of expertise in handling data and knowledge of a few programming languages.
• One popular data science Venn Diagram I have seen on internet is here: Note that a data scientist is at the intersection of a lot of things. Communication, statistics, programming, and business.

• Read also:
• Rohit Malshe's answer to How do I learn machine learning?
• Rohit Malshe's answer to How should I start learning Python?
• Rohit Malshe's answer to What is deep learning? Why is this a growing trend in machine learning? Why not use SVMs?
• Rohit Malshe's answer to Are ‘curated paths to a Data Science career’ on Coursera worth the money and time?

In all the seriousness, if you want a elaborate documentation on all this, I would suggest, go ahead and read this McKinsey report to get a full understanding. I only extracted a few sections out of it conveniently because I only wanted to add on the top of someone else’s knowledge, and put together these concepts like a story so as to inspire the people to think about this subject and begin their own journeys.

Big data: The next frontier for innovation, competition, and productivity

I will answer a few questions step by step, and wherever possible, I will give a few pictures, or plots to show you how things look like.

McKinsey consultants! You are amazing, so if you read things written in this answer that were typed by you at some point in time, I give full credit to you.

• What do we mean by "big data"?
• “Big data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. This definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered big data—i.e., we need not define big data in terms of being larger than a certain number of terabytes (thousands of gigabytes). We assume that, as technology advances over time, the size of datasets that qualify as big data will also increase. Also note that the definition can vary by sector, depending on what kinds of software tools are commonly available and what sizes of datasets are common in a particular industry. With those caveats, big data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes).
• What is a typical size of data I may have to deal with? Sometimes GBs, sometimes just a few MBs, sometimes up to as high as 1TB. Sometimes the complexity is nothing. The data may be representing the same thing. Sometimes the complexity can be very high. I might have a giant file full of a lot of data and logs which can be structured or unstructured.
• Think for example about Macy’s. There are thousands of stores, selling thousands of items per day to millions of customers. If Macy’s wants to derive a conclusion ~ should they rather diversify in shoes, or should they rather diversify in women’s purses? How would they make this decision?
• Well then, a natural question is: How do we measure the value of big data?
• Measuring data Measuring volumes of data provokes a number of methodological questions. First, how can we distinguish data from information and from insight? Common definitions describe data as being raw indicators, information as the meaningful interpretation of those signals, and insight as an actionable piece of knowledge.
• For example - In this chart, someone has plotted cost per student for various regions. It makes a few of them stand out.

Let us now talk about analysis: This is big part of being a data scientist.

• TECHNIQUES FOR ANALYZING BIG DATA
• There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need to analyze new combinations of data.
• Also, note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques listed here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
• A/B testing. A technique in which a control group is compared with a variety of test groups in order to determine what treatments (i.e., changes) will improve a given objective variable, e.g., marketing response rate. This technique is also known as split testing or bucket testing. An example application is determining what copy text, layouts, images, or colors will improve conversion rates on an e-commerce Web site. Big data enables huge numbers of tests to be executed and analyzed, ensuring that groups are of sufficient size to detect meaningful (i.e., statistically significant) differences between the control 28 and treatment groups (see statistics). When more than one variable is simultaneously manipulated in the treatment, the multivariate generalization of this technique, which applies statistical modeling, is often called “A/B/N” testing. What would an example look like?
• Imagine that Coke signs up with Facebook to work on marketing and sales. Facebook would put advertisements according to the customers. It can create versions of advertisements. Not all versions will suit to every geography. Some will suit to USA, some will suit to India. Some can suit to Indians living in USA. What Facebook can do is to choose a subset of people from a massive pool, and pass advertisements to them in their feed according to whether those people love food or not. For each advertisement, Facebook will collect the responses and accordingly determine which advertisement does better, and on a larger pool of people it will use a better one. Does data science let someone determine better what the answer should be? Absolutely!
• Association rule learning. A set of techniques for discovering interesting relationships, i.e., “association rules,” among variables in large databases. These techniques consist of a variety of algorithms to generate and test possible rules. One application is market basket analysis, in which a retailer can determine which products are frequently bought together and use this information for marketing (a commonly cited example is the discovery that many supermarket shoppers who buy diapers also tend to buy beer).
• Classification. A set of techniques to identify the categories in which new data points belong, based on a training set containing data points that have already been categorized. One application is the prediction of segment-specific customer behavior (e.g., buying decisions, churn rate, consumption rate) where there is a clear hypothesis or objective outcome. These techniques are often described as supervised learning because of the existence of a training set; they stand in contrast to cluster analysis, a type of unsupervised learning.
• Cluster analysis. A statistical method for classifying objects that splits a diverse group into smaller groups of similar objects, whose characteristics of similarity are not known in advance. An example of cluster analysis is segmenting consumers into self-similar groups for targeted marketing. This is a type of unsupervised learning because training data are not used. This technique is in contrast to classification, a type of supervised learning.
• Crowdsourcing. A technique for collecting data submitted by a large group of people or community (i.e., the “crowd”) through an open call, usually through networked media such as the Web.28 This is a type of mass collaboration and an instance of using Web 2.0.29 Data fusion and data integration.
• A set of techniques that integrate and analyze data from multiple sources in order to develop insights in ways that are more efficient and potentially more accurate than if they were developed by analyzing a single source of data.
• Data mining. A set of techniques to extract patterns from large datasets by combining methods from statistics and machine learning with database management. These techniques include association rule learning, cluster analysis, classification, and regression. Applications include mining customer data to determine segments most likely to respond to an offer, mining human resources data to identify characteristics of most successful employees, or market basket analysis to model the purchase behavior of customers.
• Ensemble learning. Using multiple predictive models (each developed using statistics and/or machine learning) to obtain better predictive performance than could be obtained from any of the constituent models. This is a type of supervised learning.
• Genetic algorithms. A technique used for optimization that is inspired by the process of natural evolution or “survival of the fittest.” In this technique, potential solutions are encoded as “chromosomes” that can combine and mutate. These individual chromosomes are selected for survival within a modeled “environment” that determines the fitness or performance of each individual in the population. Often described as a type of “evolutionary algorithm,” these algorithms are well-suited for solving nonlinear problems. Examples of applications include improving job scheduling in manufacturing and optimizing the performance of an investment portfolio.
• Machine learning. A subspecialty of computer science (within a field historically called “artificial intelligence”) concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Natural language processing is an example of machine learning.
• Natural language processing (NLP). A set of techniques from a sub-specialty of computer science (within a field historically called “artificial intelligence”) and linguistics that uses computer algorithms to analyze human (natural) language. Many NLP techniques are types of machine learning. One application of NLP is using sentiment analysis on social media to determine how prospective customers are reacting to a branding campaign. Data from social media, analyzed by natural language processing, can be combined with real-time sales data, in order to determine what effect a marketing campaign is having on customer sentiment and purchasing behavior.
• Neural networks. Computational models, inspired by the structure and workings of biological neural networks (i.e., the cells and connections within a brain), that find patterns in data. Neural networks are well-suited for finding nonlinear patterns. They can be used for pattern recognition and optimization. Some neural network applications involve supervised learning and others involve unsupervised learning. Examples of applications include identifying high-value customers that are at risk of leaving a particular company and identifying fraudulent insurance claims.
• Network analysis. A set of techniques used to characterize relationships among discrete nodes in a graph or a network. In social network analysis, connections between individuals in a community or organization are analyzed, e.g., how information travels, or who has the most influence over whom. Examples of applications include identifying key opinion leaders to target for marketing, and identifying bottlenecks in enterprise information flows.
• Optimization. A portfolio of numerical techniques used to redesign complex systems and processes to improve their performance according to one or more objective measures (e.g., cost, speed, or reliability). Examples of applications include improving operational processes such as scheduling, routing, and floor layout, and making strategic decisions such as product range strategy, linked investment analysis, and R&D portfolio strategy. Genetic algorithms are an example of an optimization technique. Same way, mixed integer programming is another way.
• Pattern recognition. A set of machine learning techniques that assign some sort of output value (or label) to a given input value (or instance) according to a specific algorithm. Classification techniques are an example.
• Predictive modeling. A set of techniques in which a mathematical model is created or chosen to best predict the probability of an outcome. An example of an application in customer relationship management is the use of predictive models to estimate the likelihood that a customer will “churn” (i.e., change providers) or the likelihood that a customer can be cross-sold another product. Regression is one example of the many predictive modeling techniques.
• Regression. A set of statistical techniques to determine how the value of the dependent variable changes when one or more independent variables is modified. Often used for forecasting or prediction. Examples of applications include forecasting sales volumes based on various market and economic variables or determining what measurable manufacturing parameters most influence customer satisfaction. Used for data mining.
• Sentiment analysis. Application of natural language processing and other analytic techniques to identify and extract subjective information from source text material. Key aspects of these analyses include identifying the feature, aspect, or product about which a sentiment is being expressed, and determining the type, “polarity” (i.e., positive, negative, or neutral) and the degree and strength of the sentiment. Examples of applications include companies applying sentiment analysis to analyze social media (e.g., blogs, microblogs, and social networks) to determine how different customer segments and stakeholders are reacting to their products and actions.
• Signal processing. A set of techniques from electrical engineering and applied mathematics originally developed to analyze discrete and continuous signals, i.e., representations of analog physical quantities (even if represented digitally) such as radio signals, sounds, and images. This category includes techniques from signal detection theory, which quantifies the ability to discern between signal and noise. Sample applications include modeling for time series analysis or implementing data fusion to determine a more precise reading by combining data from a set of less precise data sources (i.e., extracting the signal from the noise). Signal processing techniques can be used to implement some types of data fusion. One example of an application is sensor data from the Internet of Things being combined to develop an integrated perspective on the performance of a complex distributed system such as an oil refinery.
• Spatial analysis. A set of techniques, some applied from statistics, which analyze the topological, geometric, or geographic properties encoded in a data set. Often the data for spatial analysis come from geographic information systems (GIS) that capture data including location information, e.g., addresses or latitude/longitude coordinates. Examples of applications include the incorporation of spatial data into spatial regressions (e.g., how is consumer willingness to purchase a product correlated with location?) or simulations (e.g., how would a manufacturing supply chain network perform with sites in different locations?).
• Statistics. The science of the collection, organization, and interpretation of data, including the design of surveys and experiments. Statistical techniques are often used to make judgments about what relationships between variables could have occurred by chance (the “null hypothesis”), and what relationships between variables likely result from some kind of underlying causal relationship (i.e., that are “statistically significant”). Statistical techniques are also used to reduce the likelihood of Type I errors (“false positives”) and Type II errors (“false negatives”). An example of an application is A/B testing to determine what types of marketing material will most increase revenue.
• Supervised learning. The set of machine learning techniques that infer a function or relationship from a set of training data. Examples include classification and support vector machines.30 This is different from unsupervised learning.
• Simulation. Modeling the behavior of complex systems, often used for forecasting, predicting and scenario planning. Monte Carlo simulations, for example, are a class of algorithms that rely on repeated random sampling, i.e., running thousands of simulations, each based on different assumptions. The result is a histogram that gives a probability distribution of outcomes. One application is assessing the likelihood of meeting financial targets given uncertainties about the success of various initiatives.
• Time series analysis. Set of techniques from both statistics and signal processing for analyzing sequences of data points, representing values at successive times, to extract meaningful characteristics from the data. Examples of time series analysis include the hourly value of a stock market index or the number of patients diagnosed with a given condition every day.
• Time series forecasting. Time series forecasting is the use of a model to predict future values of a time series based on known past values of the same or other series. Some of these techniques, e.g., structural modeling, decompose a series into trend, seasonal, and residual components, which can be useful for identifying cyclical patterns in the data. Examples of applications include forecasting sales figures, or predicting the number of people who will be diagnosed with an infectious disease.
• Unsupervised learning. A set of machine learning techniques that finds hidden structure in unlabeled data. Cluster analysis is an example of unsupervised learning (in contrast to supervised learning).
• Visualization. Techniques used for creating images, diagrams, or animations to communicate, understand, and improve the results of big data analyses. This expands into creating dashboards, on web or desktop platforms.

Hope this somewhat elaborate write up gives you some inspiration to hold on to. Stay blessed and stay inspired!

Tough question to answer. People that mostly develop algorithms will have different design patterns from people that mostly build data pipelines and those that mostly build models.

There’s also the question that some data scientists have a more CS background and others have a less CS background. This plays heavily in how they code their data products. For people with less of a technical background, it is useful to understand design patterns. This doesn’t mean that they have to implement them by the book, but the simple fact that they were exposed to that knowledge makes a huge difference. Instead of just accepting the idiosyncrasies of the programming language they are using, they’ll have, at the very least, a different view on how a problem can be solved.

As an example, sometimes things take a long time in R. It’s not R’s fault. It’s the coders lack of exposure to programming side of things.

With this and only this in mind, here are the ones I believe are beneficial to know by data scientists, particularly the ones that have less programming knowledge.

1. Singleton. While singletons are close to impossible to be properly implemented in many languages, the basic concept of a singleton and how to access it from a global location could avoid many problems to people that simply create global variables “because it’s easy” or “there’s no other way”.
2. Module… what can I say? Many people don’t even refactor to isolate copy/pasted functions let alone write whole modules.
3. Factory. I used factories quite a lot in game dev but I have to admit that I mostly use them to get pieces to build visualisations. My use case is more of a helper function than a factory but I started doing this with this design pattern in mind.
4. And I’m not entirely sure if MVC is a design pattern but I’ll leave it here. I think that approaching data products with the overall concept of MVC in mind makes their development much easier. It is true that this makes a lot more sense for interactive data products but it isn’t completely far fetched to conceptualise an automated data product has an MVC implementation. The model (in MVC not model as in machine learning model) is our data, whatever data, training, test, validation or new production data. The controller is pre-processing, training and predicting. The view is the presentation layer, how we pass it to other systems or how other systems read the output.

No number 5 but I hope these help.

Nice question!

For expanding you knowledge on design patterns I recommend the below book

There is also an excellent Udemy course by Mark Farragher on C# design patterns where he explores the gang of four and has some code examples. If you search in Udemy for “Mark Farragher C# with structural and creation also design patterns” you should find it

As for different data structures I would recommend

And

Well euhm, this question is really really broad. ML is being used for sooo many awesome things right now its hard to cherry pick, but let me give you some cool links:

• Generating High-Res images of faces with GANS

• Realtime audio translation between any two languages with the new Google Pixel Buds
• Assisting radiologists by automatically detecting cancer in tissue scans

• Training robot arms to pick up all kinds of natural objects
• Using GANs to achieve new breakthroughs in cosmology
• Deep foto style Transfer

• Combining Style-transfer with Augmented Reality to create amazing visual experiences
• A great blogpost on Medium has a lot of other super cool links to GitHub projects of the past year: 30 Amazing Machine Learning Projects for the Past Year (v.2018)
• …

I mean, I could go on for hours like this. The application potential for Machine Learning is currently only limited by the human imagination. Wild things are coming guys!

Cheers ;)

Here’s one of the more interesting ones from my personal experience:

I once knew a guy, let’s call him Bob, who became really interested in machine learning after witnessing all the wonders it could do for society (and all the wonders it could do for his bank account after he saw the average annual pay of a machine learning engineer).

Anyways, Bob decided that for one of his CS classes, his end-of-term project would be a conversational chatbot. Since this was an introductory CS class, creating even a mediocre chatbot would be amazing (most of the projects were just remakes of Tetris using Python Tkinter).

Bob asked me for some advice on chatbots, since I had experience working with dialog systems before. I told him that, while the machine learning part wouldn’t be that bad (I pointed him to some basic tutorials in TensorFlow), finding the data and training the model would be the worst part.

“Chatbots need a ton of conversational data to train properly, so try to find a really large open-source conversational dataset,” I said.

Two months later (a day before the project is due), Bob comes up to me again, anxious and panicking. I asked him if he finished his chatbot, and he did. The problem, however, was that the chatbot would literally only give answers that included profanity. Example:

“Hi, my name is Allen”

“Well hello you #@$%”

“Excuse me?”

“You little #@%*”

Bob had ended up training his chatbot on a huge dataset from Reddit, which included all the uncensored comments on threads. Given the nature of Reddit threads, I was shocked that there wasn’t even more profanity from his chatbot.

Moral of the story: Know your dataset very well, and make sure to process and clean the data before training a model.

P.S. Bob ended up doing an alternative project. It was a remake of Tetris in Tkinter.

The focus of machine learning is on building learning algorithms. The focus of data mining is on finding insights, regardless of methods.

The goal of machine learning is to make a prediction about something in the world. The goal of data mining is not well specified - you may not know what exactly you are looking for.

The evaluation of machine learning has well defined metrics like precision, recall, type I/II errors and F scores. The evaluation of data mining findings are highly contextual to the use case or application.

In short, machine learning is about model building, but data mining is about knowledge discovery.

The big project of IBM Watson.

Watson is a question answering computer system capable of answering questions posed in natural language,developed in IBM's DeepQA project

For each clue, Watson's three most probable responses were displayed on the television screen. Watson consistently outperformed its human opponents on the game's signaling device, but had trouble in a few categories, notably those having short clues containing only a few words.

In February 2013, IBM announced that Watson software system's first commercial application would be for utilization management decisions in lung cancer treatment at Memorial Sloan Kettering Cancer Center in conjunction with health insurance company WellPoint.

IBM Watson's former business chief Manoj Saxena says that 90% of nurses in the field who use Watson now follow its guidance.

Ref :Say Hello to IBM Watson and Watson (computer)

I find Apache Mayhout an excellent option to build Machine Learning Based applications(though i dont use it because no need of scalability has arrived). It supports clustering , classification and batch based filtering using a number of standard algorithms.
For reference - "Apache Mahout In Action" is a good book fora kickstart with Machine Learning and learn about clustering, recommendations, classification algorithms using the Mahout Library. For learning the basic niche of ML , you can also take a look at the Coursera course on Machine Learning by Andrew Ng - I am sure it has helped many people realise the power of this new emerging science.
The great thing about this library is that it is highly scalable and the examples provided in the release give useful insights into using it to build full fledged applications. One other big advantage is that it harnesses the capability of Apache Hadoop and Map Reduce in order to scale the data sets on which the different algorithms run and achieve great performance by doing distributed computing.

Apart from this for non-CS people , i would say using Weka for applications of ML and Big Data is pretty easy and user-friendly.Secondly , you can also extend its libraries by using the tar.

I hope this helps :-)

Machine learning (ML) algorithms are not currently good at the kind of pattern recognition you are referring to:

It9s = It’s

You9ll = You’ll

Don9t = ?

This seems to require reasoning and not just mapping from one vector space to another. Current ML models are excellent at mapping input vectors to output vectors but not reasoning. Thus to solve such a problem we need a form of reasoning in machines and lots of such training data.

Thus the model close to what we are looking for is a memory network which is basically just a typical neural network (NN) attached to a block of memory for fact storage. Memory networks are able to reason to some extent and one of the more interesting version of memory augmented NNs is the differentiable neural computer (DNC). These systems can learn to discover some facts and patterns that are otherwise hard for a mapping only ML model to discover.

Recurrent neural networks (RNN) are also turing complete. So they may be able to learn that pattern when provided with sufficient examples. In fact the long-short-term-memory (LSTM) is a type of memory network and hence it can somehow reason, well a little. Turing complete means being able to evaluate any computational function.

Thus the following is the approach you can use to build such an ML model.

• Use word embeddings using word2vec as the first processing stage. The words can be compactly represented that way instead of using a one-hot encoded long vector.
• Use a memory network such as the neural turing machine (NTM) or the DNC. These systems are turing complete and can discover interesting patterns in data. They are also able to learn a method to solve problems such as puzzles. Thus they are most likely to solve this one.
• Or use an RNN especially those that don't have long-term dependency issues such the LSTM and the gated recurrent unit (GRU) networks. Being also turing complete, these networks can also solve the problem.

But I am not sure how such a model can perform on that problem above. The only way to know is to build and try it on the data.

Hope this helps.

Virtual personal assistants and other progressive technologies rely on advances in Artificial Intelligence. The most popular AI fields are natural language processing, machine learning, and deep learning. Big companies employ them in activities ranging from online advertising targeting to self-driving cars. Consequently, ML experts are in demand, and ML and deep learning are some of the hottest skills currently. The number of tools that simplify the programmers’ work is growing too.

The focus on Java machine learning reflects the popularity of the language. Due to its extreme stability, leading organizations and enterprises have been adopting Java for decades. It’s widely used in mobile app development for Android which serves billions of users worldwide.

For implementing machine learning algorithms Java developers can utilize various tools and libraries. At least 90 Java-based ML projects are listed on MLOSS alone.

Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL. The software is written in Java and Scala and is suitable for mathematicians, statisticians, data scientists, and analytics professionals. Built-in machine learning algorithms facilitate easier and faster implementation of new ones.

Mahout is built atop scalable distributed architectures. It uses the MapReduce approach for processing and generating datasets with a parallel, distributed algorithm utilizing a cluster of servers. Mahout features console interface and Java API to scalable algorithms for clustering, classification, and collaborative filtering. Apache Spark is the recommended out-of-the-box distributed back-end, but Mahout supports multiple distributed backends.

Mahout is business-ready and useful for solving three types of problems:

1. item recommendation, for example, in a recommendation system;

2. clustering, e.g., to make groups of topically-related documents;

3. classification, e.g., learning which topic to assign to an unlabeled document.

If you're working on a data-driven project that relies on analytics, mining, science, and machine learning to succeed, you need to get these steps right. A revolution in information and knowledge has taken place as a result of the exponential growth of data. In today's research and strategy development, obtaining in-depth knowledge and essential information from available data is a critical component.

If I can, I'll try to give a brief introduction to each of the terms you've mentioned in your question. This is where it all begins...

• To get started, you'll need to know where your information is coming from as well as how much data there is and at what speed the data is being produced. If these conditions are met, it can be classified as Big Data, but if they are not, it is just data to be processed without any fancy names.
• All that data you are ingesting must be formatted and cleaned before it can be used. This process is known as Data Analysis.
• As soon as you know what questions you want to be answered and where to find the data that contains the answers, you're using Data Analytic.
• Use Data Mining when you don't know what to ask or where to look for the answers to your questions.
• Data Science refers to the tools and techniques used in both Data Analytics and Data Mining to make the extraction of insights easier.
• As a result of Machine Learning, some tools and techniques are in the form of self-learning programs.

Is there a distinction?

For example, data scientists are responsible for developing data-driven products and applications that handle data in a way that conventional frameworks cannot. Much more emphasis is placed on the specialized abilities to deal with data in data science. For example, it can be used to determine the impact of data on an item or association, as opposed to mining and machine learning.

In contrast to data science, which focuses on the study of data, data mining focuses on the process. By doing so, it paves the way for discovering newer patterns in large data collections. Since it sorts algorithms, it's obvious that it's similar to machine learning. However, algorithms and calculations are just a part of data mining and not machine learning. Algorithms are used to extract information from datasets in machine learning. Even so, the process of consolidating data mining algorithms is simply part of the overall procedure. It differs from ML in that it does not solely rely on calculations.

If you're interested in learning more about these topics, you can enroll in a course in this field to obtain the desired job role. In this case, I'd recommend Udacity, UpGrad, Simplilearn, and Learnbay, among other options.

1. Udacity

Data Analyst Nanodegree

You'll learn everything you need to know about data analytics in Udacity's Data Analyst Nanodegree. Four courses make up the Data Scientist Nanodegree curriculum, each of which right movements assignments you can show potential employers. Data analytics technologies such as R, Python, and Tableau are also covered. You'll be required to apply what you've learned in real-world projects inspired by industry companies or those that they provide.

2. Upgrad

• As part of its Online Management PG Program in Data Science, Upgrad offers an intensive 12-month program designed to impart knowledge on highly sought-after data scientist skills.
• This 12-month program is designed for working professionals and includes 450 hours of study, 30+ case studies, and placement assistance while allowing you to work on 12+ industry environments and various programming tools.
• For this program, you must pass a coding/algorithm-based exam to be accepted.

3. Simplilearn

As a recommendation, this course should be taken by anyone interested in learning exclusively through an online BootCamp.

To learn through BootCamp, Simplilearn is the best option for you. Bootcamp courses are now available at Simplilearn. Live, non-interactive classes in machine learning provide hands-on learning through real-world projects. It's also worth noting that temporary job assistance programs are available.

4. Learnbay

These courses are designed for working professionals who want to advance their careers in this field and are IBM Certified data science courses that are globally recognized. They offer a variety of courses for beginners, as well as help from their experts. In addition to being less expensive than well-known institutions, their course structure is well-designed, providing both important data science ideas and actual data science experiences.

Learnbay offers the following courses:

Data Science & AI Certification With Domain Specialisation

• Those who are new to the profession will benefit most from this training. Anyone with at least one year of experience in any field is welcome to enroll.
• For this course to be completed, it will take approximately 7.5 months.
• There is a cost of 65,000 rupees for the course.

Advance AI & ML Certification | Become AI Expert In Product based MNCs

• This course is designed for individuals who have 4+ years of experience working in the tech industry.
• This course will take nine months to complete.
• You will have to pay 79,900 rupees for the course.

Data Science & AI Certification Program For Managers and Leaders

• A functional expert is someone who has worked for at least 8+ years in any domain.
• You will have to pay 79,900 rupees for the course.
• For this course to be completed, you will need 11 months to do so.

Data Science & Business Analytics Program | Fast Track Course

• Only people who have been out of work for at least six months are eligible for this course, which is designed for them.
• This course will take four months to complete.
• The cost of the course is 50,000 rupees.

You can choose any of the above programs based on your experience and knowledge.

What do you need to keep in mind?

There's a great combination of industry-based learning and a very valuable industrial capstone project from multinational corporations.

You must be able to attend all of the live online meetings because the courses are only related to them (weekend and weekdays schedule available). This training will be extremely beneficial to non-technical candidates. An additional session, Module 0, is available for basic programming support. To prepare for a data analyst career, job aid includes mock interview tests and soft-skill grooming. To gain employment, you must enroll in such a program.

Here are a few things you'll enjoy about this course:

• As a 200+ hour online course geared toward working professionals, the Learnbay Data Science and machine learning Certification program is open to beginners as well, as it has a well-structured curriculum that includes all prerequisites. Therefore, it covers everything from the basics to the advanced.
• Theoretical knowledge is important, but implementation is more important in Data Science. Even though Learnbay offers 15+ real-time projects to work on with proper supervision, and having completed them gives you a significant advantage and insert to your CV, this is where Learnbay differs from other institutes.
• Data Science/AI professionals who are IIT/IIM graduates are teaching the class, and they have a great deal of experience in the field.
• Their job aid program prepares you for product-based firms through mock interviews, and if you complete the program successfully, both companies will offer you referral interviews. The result is that many of its graduates work as Data Scientists for companies like IBM, TCS, and Accenture.

Currently, Learnbay focuses on the following locations for the course mentioned above:

• HR, marketing, and sales.
• Manufacturing, telecommunication, and mechanical
• Healthcare and pharma
• Transportation, media, and hospitality.
• BFSI
• Oil, gas, and energy.

Apart from this, the course offers elective modules for technical professionals such as

• Advance data structure and algorithm (suitable for programming ninjas)
• Cloud and DevOps (suitable for IT professionals with a cloud computing background)
• IoT and embedded (auto-engineering-related knowledge)

Experiential data scientists who want a lucrative career in data science can take advantage of the elective modules that are available. Oil and gas, HR, marketing, and telecommunications candidates are likely to be considered for the position.

There are several data analytics courses at Learnbay that can help you make the switch from one career to another. People will be able to adopt specialized machine learning theories and other courses using Learnbay's processes and tactics, according to my personal opinion. Although there are several other educational institutions, based on my research, I would recommend Learnbay as a good option.

The future is bright for you!!!