Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning - Learn about each concept and relation between them for their ...

Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning - Learn about each concept and relation between them for their ...

**Data Science** is an interdisciplinary field whose primary objective is the extraction of meaningful knowledge and insights from data. These insights are extracted with the help of various mathematical and Machine Learning-based algorithms. Hence, Machine Learning is a key element of Data Science.

Alongside Machine Learning, as the name suggests, “data” itself is the fuel for Data Science. Without the availability of appropriate data, key insights cannot be extracted from it. Both the volume and accuracy of data matters in this field, since the algorithms are designed to “learn” with “experience”, which comes through the data provided. Data Science involves the use of various types of data, from multiple sources. Some of the types of data are image data, text data, video data, time-dependent data, time-independent data, audio data, etc.

Data Science requires knowledge of multiple disciplines. As shown in the figure, it is a combination of Mathematics and Statistics, Computer Science skills and Domain Specific Knowledge. Without a mastery of all these sub-domains, the grasp on Data Science will be incomplete.

**Machine Learning** is a subset or a part of Artificial Intelligence. It primarily involves the scientific study of algorithmic, mathematical, and statistical models which performs a specific task by analyzing data, without any explicit step-by-step instructions, by relying on patterns and inference, which is drawn from the data. This also contributes to its alias, Pattern Recognition.

Its objective is to recognize patterns in a given data and draw inferences, which allows it to perform a similar task on similar but unseen data. These two separate sets of data are known as the “Training Set” and “Testing Set” respectively.

Machine Learning primarily finds its applications in solving complex problems, which, a normal procedure oriented program cannot solve, or in places where there are too many variables that need to be explicitly programmed, which is not feasible.

As shown in the figure, Machine Learning is primarily of three types, namely: Supervised Learning, Unsupervised Learning and Reinforcement Learning.

- Supervised Learning: This is the most commonly used form of machine learning and is widely used across the industry. In fact, most of the problems that are solved by Machine Learning belong to Supervised Learning. A learning problem is known as supervised learning when the data is in the form of feature-label pairs. In other words, the algorithm is trained on data where the ground truth is known. This is learning with a teacher. Two common types of supervised learning are:

Classification: This is a process where the dataset is categorized into discrete values or categories. For example, if the input to the algorithm is an image of a dog or a cat, ideally, a well-trained algorithm should be able to predict whether the input image is that of a dog or of a cat.Regression: This is a process where the dataset has continuous valued target values. That is, the output of the function is not categories, but is a continuous value. For example, algorithms that forecast the future price of the stock market would output a continuous value (like 34.84, etc.) for a given set of inputs. * Unsupervised Learning: This is a much lesser used, but quite important learning technique. This technique is primarily used when there is unlabeled data or data without the target values mentioned. In such learning, the algorithm has to analyze the data itself and bring out insights based on certain common traits or features in the dataset. This is learning without a teacher. Two common types of unsupervised learning are:

Clustering: Clustering is a well known unsupervised learning technique where similar data are automatically grouped together by the algorithm based on common features or traits (eg. color, values, similarity, difference, etc.).Dimensionality Reduction: Yet another popular unsupervised learning is dimensionality reduction. The dataset that is used for machine learning is often huge and of high dimensions (higher than three dimensions). One major problem in working with high dimensional data is data-visualization. Since we can visualize and understand up-to 3 dimensions, higher dimensional data is often difficult for human beings to interpret. In addition to this, higher dimension means more features, which in turn means a more complex model, which is often a curse for any machine learning model. The aim is to keep the simplest model that works best on a wide range of unseen data. Hence, dimensionality reduction is an important part of working with high dimensional data. One of the most common methods of dimensionality reduction is Principal Component Analysis (PCA).* Reinforcement Learning: This is a completely different approach to “learning” when compared to the previous two categories. This particular class of learning algorithms primarily finds its applications in Game AI, Robotics and Automatic Trading Bots. Here, the machine is not provided with a huge amount of data. Instead, in a given scenario (playground) some parameters and constrictions are defined and the algorithm is let loose. The only feedback given to the algorithm is that, if it wins or performs a correct task, it is rewarded. If it loses or performs an incorrect task, it is penalized. Based on this minimal feedback, over time the algorithm learns to how to do the correct task on its own.

Artificial Intelligence is a vast field made up of multidisciplinary subjects, which aims to artificially create “intelligence” to machines, similar to that displayed by humans and animals. The term is used to describe machines that mimic cognitive functions such as learning and problem-solving.

Artificial Intelligence can be broadly classified into three parts: Analytical AI, Human-Inspired AI, and Humanized AI.

- Analytical AI: It only has characteristics which are consistent with Cognitive Intelligence. It generates a cognitive representation of the world around it based on past experiences, which inspires future decisions.
- Human-Inspired AI: In addition to having Cognitive Intelligence, this class of AI also has Emotional Intelligence. It has a deeper understanding of human emotions in addition to Cognitive Intelligence and thus has a better understanding of the world around it. Both Cognitive Intelligence and Emotional Intelligence contributes to the decision making of Human-Inspired AI.
- Humanized AI: This is the most superior form of AI among the three. This form of AI incorporates Cognitive Intelligence, Emotional Intelligence, and Social Intelligence into its decision making. With a broader understanding of the world around it, this form of AI is able to make self-conscious and self-aware decisions and interactions with the external world.

From the above introductions, it may seem that these fields are not related to each other. However, that is not the case. Each of these three fields is quite closely related to each other than it may seem.

If we look at Venn Diagrams, Artificial Intelligence, Machine Learning and Data Science are overlapping sets, with Machine Learning being a subset or a part of Artificial Intelligence, and Data Science having a significant chunk of it under Artificial Intelligence and Machine Learning.

Artificial Intelligence is a much broader field and it incorporates most of the other intelligence-related fields of study. Machine Learning, being a part of AI, deals with the algorithmic learning and inference based on data, and finally, Data Science is primarily based on statistics, probability theory, and has significant contribution of Machine Learning to it; of course, AI also being a part of it, since Machine Learning is indeed a subset of Artificial Intelligence.

Similarities: All of the three fields have one thing in common, Machine Learning. Each of these is heavily dependent on Machine Learning Algorithms.

In Data Science, the statistical algorithms that are used are limited to certain applications. In most cases, Data Scientists rely on Machine Learning techniques to extract inferences from data.

The current technological advancement in Artificial Intelligence is heavily based on Machine Learning. The part of AI without Machine Learning is like a car without an engine. However, without the “learning” part, Artificial Intelligence is basically Expert Systems, Search and Optimization algorithms.

Difference between the three

Even though they are significantly similar to each other, there are still a few key differences that are to be noted.

Since all the three domains are interrelated, they have some common applications and some unique to each of them. Most applications involve the use of Machine Learning in some form or the other. Even then, there are certain applications of each domain, which are unique. A few of them are listed below:

- Data Science: The applications in this domain are dependent on machine learning and mathematical algorithms, such as statistics and probability based algorithms.

Time Series Forecasting: This is a very important application of data science and is used across the industry, primarily in the banking sector and the stock market sector. Even though there are Machine Learning based algorithms for this specific application, Data Scientists usually prefer the statistical approach.Recommendation Engines: This is a statistics-based approach towards recommending products or services to the user, based on data of his/her previous interests. Similar to the previous application, Machine Learning based algorithms to achieve similar or better results is also present.* Machine Learning: The applications of this domain is nearly limitless. Every industry has some problem that can partially or fully be solved by Machine Learning techniques. Even Data Science and Artificial Intelligence roles make use of Machine Learning to solve a huge set of problems.

Computer Vision: This is another sub-field which falls under Machine Learning and deals with visual information. This field itself finds its applications in many industries, for example, Autonomous Driving Vehicles, Medical Imaging, Autonomous Surveillance Systems, etc.Natural Language Processing: Similar to the previous example, this field is also self-contained sub-field of research. Natural Language Processing (NLP) or Natural Language Understanding (NLU) primarily deals with the interpretation and understanding of the meaning behind spoken or written text/language. Understanding the exact meaning of a sentence is quite difficult (even for human beings). Teaching a machine to understand the meaning behind a text is even more challenging. Few of the major applications of this sub-field are the development of intelligent chatbots, artificial voice assistants (Google Assistant, Siri, Alexa, etc.), spam detection, hate speech detection and so on.* Artificial Intelligence: Most of the current advancements and applications in this domain is based on a sub-field of Machine Learning, known as Deep Learning. Deep Learning deals with artificially emulating the structure and function of the biological neuron. However, since few of the applications of Deep Learning have already been discussed under Machine Learning, let us look at applications of Artificial Intelligence that is not primarily dependent on Machine Learning.

Game AI: Game AI is an interesting application of Artificial Intelligence, where the machine automatically learns to play complex games to the level where it can challenge and even win against a human being. Google’s DeepMind had developed a Game AI called AlphaGo, which outperformed and beat the human world champion in 2017. Similarly, video game AI’s have been developed to play Dota 2, flappy bird and Mario. These models are developed using several algorithms like Search and Optimization, Generative Models, Reinforcement Learning, etc.Search: Artificial Intelligence has found several applications in Search Engines, for example, Google and Bing Search. The method of displaying results and the order in which results are displayed are based on algorithms developed in the field of Artificial Intelligence. These applications do contain Machine Learning techniques, but their older versions were developed by algorithms like Google’s proprietary PageRank Algorithm, which were not based on “Learning”.Robotics: One of the major applications of Artificial Intelligence is in the field of robotics. Teaching robots to walk/run automatically (for example, Spot and Atlas) using Reinforcement Learning has been one of the biggest goals of companies like Boston Dynamics. In addition to that, humanoid robots like Sophia are a perfect example of AI being applied for Humanized AI.##**Skill-set Required**

Since the fields are interrelated by a significant degree, the skill-set required to master each of these fields is nearly the same and overlapping. However, there are a few skill-sets that are uniquely associated with each of them. The same has been discussed further.

- Mathematics: Each of these fields is math heavy, which means mathematics are the basic building blocks of these fields and in order to fully understand the algorithms and master them, a great math background is necessary. However, all the fields of math are not necessary for all of these. The specific fields of math that are required are discussed below:

Linear Algebra: Since all of these fields are based on data, which comes in huge volumes of rows and columns, matrices are the easiest and most convenient method of representing and manipulating such data. Hence, a thorough knowledge of Linear Algebra and Matrix operations is necessary for all three fields.Calculus: Deep Learning, the sub-field of Machine Learning is heavily dependent on calculus. To be more precise, multivariate derivatives. In neural networks, backpropagation algorithms require multiple derivative calculations, which demands a thorough knowledge of calculus.Statistics: Since these fields deal with a huge amount of data, the knowledge of statistics is imperative. Statistical methods to deal with the selection and testing of smaller sample size with diversity is the common application for all three fields. However, statistics finds its main application in Data Science, where most of the algorithms are purely based on statistics (eg. ARIMA algorithm used for Time Series Analysis).Probability: Similar to the reason behind statistics, probability and the conditional probability of a certain event is the basic building block of important Machine Learning algorithms like Naive Bayes Classifier. Probability theory is also very important in understanding Data Science Algorithms.* Computer Science: There is no doubt about either of these fields being a part of the Computer Science field. Hence, a thorough knowledge of computer science algorithms is quite necessary.

Search and Optimization Algorithms: Fundamental Search Algorithms like Breadth-First Search (BFS), Depth-First Search (DFS), Bidirectional Search, Route Optimization Algorithms, etc. are quite important. These search and optimization algorithms find their use in the Artificial Intelligence field.Fuzzy Logic: Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. It imitates the way human beings make decisions. For example, making a YES or NO decision based on a certain set of events or environmental conditions. Fuzzy Logic is primarily used in Artificially Intelligent Systems.Basic Algorithms and Optimization: Even though this is not a necessity, but it is a good-to-have knowledge since fundamental knowledge on algorithms (searching, sorting, recursion, etc.) and optimization (space and time complexity) is necessary for any computer science related fields.* Programming Knowledge: The implementation of any of the algorithms in these fields is through programming. Hence a thorough knowledge of programming is a necessity. Some of the most commonly used programming languages are discussed further.

Python: One of the most commonly used programming languages for either of these fields is Python. It is used across the industry and has support for a plethora of open source libraries for Machine Learning, Deep Learning, Artificial Intelligence, and Data Science. However, programming is not just about writing code, it is about writing proper Pythonic code. This has been discussed in detail in this article: A Guide to Best Python Practices.R: This is the second most used programming language for such applications across the industry. R excels in statistical libraries and data visualization when compared to python. However, lacks significantly when it comes to Deep Learning libraries. Hence, R is a preferred tool for Data Scientists.##**Job Market**

The Job Market for each of these fields is in very high demand. As a direct quote from Andrew Ng says, “AI is the new Electricity”. This is quite true as the extended field of Artificial Intelligence is at the verge of revolutionizing every industry in ways that could not be anticipated earlier.

Hence, the demand for jobs in the field of Data Science and Machine Learning is quite high. There are more job openings worldwide than the number of qualified Engineers who are eligible to fill that position. Hence, due to supply-demand constraints, the amount of compensation offered by companies for such roles exceeds any other domain.

The job scenario for each of the different domains are discussed further:

- Data Science: The number of job posting with the profile of Data Science is highest, among the three discussed domains. Data Scientists are handsomely paid for their work. Due to the blurred lines in terms of the difference between the fields, the job description of a Data Scientist ranges from Time Series Forecasting to Computer Vision. It basically covers the entire domain. For further insights on the job aspect of Data Science, the article on What is Data Science can be referred to.
- Machine Learning: Even though the number of jobs postings having the job profile as “Machine Learning Engineer” is much lesser when compared to that of a Data Scientist, it is still a significant field to consider when it comes to availability of jobs. Moreover, someone who is skilled in Machine Learning is a good candidate to consider for a Data Science role. However, unlike Data Science, Machine Learning job descriptions primarily deal with the requirements of “Learning” algorithms (including Deep Learning), and the industry ranges from Natural Language Processing to developing Recommendation Engines.
- Artificial Intelligence: Coming across job postings with profiles of “Artificial Intelligence Developer” developer is quite rare. Instead of “Artificial Intelligence”, most companies write “Data Scientists” or “Machine/Deep Learning Engineers” in the job profile. However, Artificial Intelligence Developers, in addition to getting jobs in the Machine Learning domain, mostly find jobs in Robotics and AI R&D oriented companies like Boston Dynamics, DeepMind, OpenAI, etc.

Data Science, Machine Learning and Artificial Intelligence are like the different branches of the same tree. They are highly overlapping and there is no clear boundary amongst them. They have common skill set requirements and common applications as well. They are just different names given to slightly different versions of AI.

Finally, it is worth mentioning that since there is high overlap in required skill-set, an optimally skilled Engineer is eligible to work in either of the three domains and switch domains without any major changes.

Learn Data Science | How to Learn Data Science for Free. In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free.

The average cost of obtaining a masters degree at traditional bricks and mortar institutions will set you back anywhere between $30,000 and $120,000. Even online data science degree programs don’t come cheap costing a minimum of $9,000. So what do you do if you want to learn data science but can’t afford to pay this?

I trained into a career as a data scientist without taking any formal education in the subject. In this article, I am going to share with you my own personal curriculum for learning data science if you can’t or don’t want to pay thousands of dollars for more formal study.

The curriculum will consist of 3 main parts, technical skills, theory and practical experience. I will include links to free resources for every element of the learning path and will also be including some links to additional ‘low cost’ options. So if you want to spend a little money to accelerate your learning you can add these resources to the curriculum. I will include the estimated costs for each of these.

The first part of the curriculum will focus on technical skills. I recommend learning these first so that you can take a practical first approach rather than say learning the mathematical theory first. Python is by far the most widely used programming language used for data science. In the Kaggle Machine Learning and Data Science survey carried out in 2018 83% of respondents said that they used Python on a daily basis. I would, therefore, recommend focusing on this language but also spending a little time on other languages such as R.

Before you can start to use Python for data science you need a basic grasp of the fundamentals behind the language. So you will want to take a Python introductory course. There are lots of free ones out there but I like the Codeacademy ones best as they include hands-on in-browser coding throughout.

I would suggest taking the introductory course to learn Python. This covers basic syntax, functions, control flow, loops, modules and classes.

Next, you will want to get a good understanding of using Python for data analysis. There are a number of good resources for this.

To start with I suggest taking at least the free parts of the data analyst learning path on dataquest.io. Dataquest offers complete learning paths for data analyst, data scientist and data engineer. Quite a lot of the content, particularly on the data analyst path is available for free. If you do have some money to put towards learning then I strongly suggest putting it towards paying for a few months of the premium subscription. I took this course and it provided a fantastic grounding in the fundamentals of data science. It took me 6 months to complete the data scientist path. The price varies from $24.50 to $49 per month depending on whether you pay annually or not. It is better value to purchase the annual subscription if you can afford it.

If you have chosen to pay for the full data science course on Dataquest then you will have a good grasp of the fundamentals of machine learning with Python. If not then there are plenty of other free resources. I would focus to start with on scikit-learn which is by far the most commonly used Python library for machine learning.

When I was learning I was lucky enough to attend a two-day workshop run by Andreas Mueller one of the core developers of scikit-learn. He has however published all the material from this course, and others, on this Github repo. These consist of slides, course notes and notebooks that you can work through. I would definitely recommend working through this material.

Then I would suggest taking some of the tutorials in the scikit-learn documentation. After that, I would suggest building some practical machine learning applications and learning the theory behind how the models work — which I will cover a bit later on.

**SQL**

SQL is a vital skill to learn if you want to become a data scientist as one of the fundamental processes in data modelling is extracting data in the first place. This will more often than not involve running SQL queries against a database. Again if you haven’t opted to take the full Dataquest course then here are a few free resources to learn this skill.

Codeacamdemy has a free introduction to SQL course. Again this is very practical with in-browser coding all the way through. If you also want to learn about cloud-based database querying then Google Cloud BigQuery is very accessible. There is a free tier so you can try queries for free, an extensive range of public datasets to try and very good documentation.

To be a well-rounded data scientist it is a good idea to diversify a little from just Python. I would, therefore, suggest also taking an introductory course in R. Codeacademy have an introductory course on their free plan. It is probably worth noting here that similar to Dataquest Codeacademy also offers a complete data science learning plan as part of their pro account (this costs from $31.99 to $15.99 per month depending on how many months you pay for up front). I personally found the Dataquest course to be much more comprehensive but this may work out a little cheaper if you are looking to follow a learning path on a single platform.

It is a good idea to get a grasp of software engineering skills and best practices. This will help your code to be more readable and extensible both for yourself and others. Additionally, when you start to put models into production you will need to be able to write good quality well-tested code and work with tools like version control.

There are two great free resources for this. Python like you mean it covers things like the PEP8 style guide, documentation and also covers object-oriented programming really well.

The scikit-learn contribution guidelines, although written to facilitate contributions to the library, actually cover the best practices really well. This covers topics such as Github, unit testing and debugging and is all written in the context of a data science application.

For a comprehensive introduction to deep learning, I don’t think that you can get any better than the totally free and totally ad-free fast.ai. This course includes an introduction to machine learning, practical deep learning, computational linear algebra and a code-first introduction to natural language processing. All their courses have a practical first approach and I highly recommend them.

Whilst you are learning the technical elements of the curriculum you will encounter some of the theory behind the code you are implementing. I recommend that you learn the theoretical elements alongside the practical. The way that I do this is that I learn the code to be able to implement a technique, let’s take KMeans as an example, once I have something working I will then look deeper into concepts such as inertia. Again the scikit-learn documentation contains all the mathematical concepts behind the algorithms.

In this section, I will introduce the key foundational elements of theory that you should learn alongside the more practical elements.

The khan academy covers almost all the concepts I have listed below for free. You can tailor the subjects you would like to study when you sign up and you then have a nice tailored curriculum for this part of the learning path. Checking all of the boxes below will give you an overview of most elements I have listed below.

*Calculus*

Calculus is defined by Wikipedia as “the mathematical study of continuous change.” In other words calculus can find patterns between functions, for example, in the case of derivatives, it can help you to understand how a function changes over time.

Many machine learning algorithms utilise calculus to optimise the performance of models. If you have studied even a little machine learning you will probably have heard of Gradient descent. This functions by iteratively adjusting the parameter values of a model to find the optimum values to minimise the cost function. Gradient descent is a good example of how calculus is used in machine learning.

What you need to know:

*Derivatives*

- Geometric definition
- Calculating the derivative of a function
- Nonlinear functions

*Chain rule*

- Composite functions
- Composite function derivatives
- Multiple functions

*Gradients*

- Partial derivatives
- Directional derivatives
- Integrals

*Linear Algebra*

Many popular machine learning methods, including XGBOOST, use matrices to store inputs and process data. Matrices alongside vector spaces and linear equations form the mathematical branch known as Linear Algebra. In order to understand how many machine learning methods work it is essential to get a good understanding of this field.

What you need to learn:

*Vectors and spaces*

- Vectors
- Linear combinations
- Linear dependence and independence
- Vector dot and cross products

*Matrix transformations*

- Functions and linear transformations
- Matrix multiplication
- Inverse functions
- Transpose of a matrix

Here is a list of the key concepts you need to know:

*Descriptive/Summary statistics*

- How to summarise a sample of data
- Different types of distributions
- Skewness, kurtosis, central tendency (e.g. mean, median, mode)
- Measures of dependence, and relationships between variables such as correlation and covariance

*Experiment design*

- Hypothesis testing
- Sampling
- Significance tests
- Randomness
- Probability
- Confidence intervals and two-sample inference

*Machine learning*

- Inference about slope
- Linear and non-linear regression
- Classification

The third section of the curriculum is all about practice. In order to truly master the concepts above you will need to use the skills in some projects that ideally closely resemble a real-world application. By doing this you will encounter problems to work through such as missing and erroneous data and develop a deep level of expertise in the subject. In this last section, I will list some good places you can get this practical experience from for free.

“With deliberate practice, however, the goal is not just to reach your potential but to build it, to make things possible that were not possible before. This requires challenging homeostasis — getting out of your comfort zone — and forcing your brain or your body to adapt.”,

Anders Ericsson,Peak: Secrets from the New Science of Expertise

Machine learning competitions are a good place to get practice with building machine learning models. They give access to a wide range of data sets, each with a specific problem to solve and have a leaderboard. The leaderboard is a good way to benchmark how good your knowledge at developing a good model actually is and where you may need to improve further.

In addition to Kaggle, there are other platforms for machine learning competitions including Analytics Vidhya and DrivenData.

The UCI machine learning repository is a large source of publically available data sets. You can use these data sets to put together your own data projects this could include data analysis and machine learning models, you could even try building a deployed model with a web front end. It is a good idea to store your projects somewhere publically such as Github as this can create a portfolio showcasing your skills to use for future job applications.

One other option to consider is contributing to open source projects. There are many Python libraries that rely on the community to maintain them and there are often hackathons held at meetups and conferences where even beginners can join in. Attending one of these events would certainly give you some practical experience and an environment where you can learn from others whilst giving something back at the same time. Numfocus is a good example of a project like this.

In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free. Showcasing what you are able to do in the form of a portfolio is a great tool for future job applications in lieu of formal qualifications and certificates. I really believe that education should be accessible to everyone and, certainly, for data science at least, the internet provides that opportunity. In addition to the resources listed here, I have previously published a recommended reading list for learning data science available here. These are also all freely available online and are a great way to complement the more practical resources covered above.

Thanks for reading!

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Downloadable PDF of Best AI Cheat Sheets in Super High DefinitionLet’s begin.

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science in HD

Neural Networks Cheat Sheets

Neural Networks Basics Cheat Sheet

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

- Input Layer (All the inputs are fed in the model through this layer)
- Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
- Output Layer (The data after processing is made available at the output layer)

Neural Networks Graphs Cheat Sheet

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Machine Learning Cheat Sheets

Machine Learning with Emojis Cheat Sheet

Scikit Learn Cheat Sheet

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines is a simple and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and matplotlib an open source, commercially usable — BSD license

Scikit-learn Algorithm Cheat Sheet

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.

If you like these cheat sheets, you can let me know here.### Machine Learning: Scikit-Learn Algorythm for Azure Machine Learning Studios

Scikit-Learn Algorithm for Azure Machine Learning Studios Cheat Sheet

Data Science with Python Cheat Sheets

TensorFlow Cheat Sheet

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

If you like these cheat sheets, you can let me know here.### Data Science: Python Basics Cheat Sheet

Python Basics Cheat Sheet

Python is one of the most popular data science tool due to its low and gradual learning curve and the fact that it is a fully fledged programming language.

PySpark RDD Basics Cheat Sheet

“At a high level, every Spark application consists of a *driver program* that runs the user’s `main`

function and executes various *parallel operations* on a cluster. The main abstraction Spark provides is a *resilient distributed dataset* (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to *persist* an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.” via Spark.Aparche.Org

NumPy Basics Cheat Sheet

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

***If you like these cheat sheets, you can let me know *****here.**

Bokeh Cheat Sheet

“Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.” from Bokeh.Pydata.com

Karas Cheat Sheet

Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Padas Basics Cheat Sheet

Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.

If you like these cheat sheets, you can let me know here.### Pandas Cheat Sheet: Data Wrangling in Python

Pandas Cheat Sheet: Data Wrangling in Python

The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.

Data Wrangling with Pandas Cheat Sheet

- Although many fundamental data processing functions exist in R, they have been a bit convoluted to date and have lacked consistent coding and the ability to easily
*flow*together → leads to difficult-to-read nested functions and/or*choppy*code. - R Studio is driving a lot of new packages to collate data management tasks and better integrate them with other analysis activities → led by Hadley Wickham & the R Studio team → Garrett Grolemund, Winston Chang, Yihui Xie among others.
- As a result, a lot of data processing tasks are becoming packaged in more cohesive and consistent ways → leads to:
- More efficient code
- Easier to remember syntax
- Easier to read syntax” via Rstudios

Data Wrangling with ddyr and tidyr Cheat Sheet

If you like these cheat sheets, you can let me know here.### Data Science: Scipy Linear Algebra

Scipy Linear Algebra Cheat Sheet

SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.[3]

Matplotlib Cheat Sheet

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented APIfor embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib.

Pyplot is a matplotlib module which provides a MATLAB-like interface matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.

Data Visualization with ggplot2 Cheat Sheet

Big-O Cheat Sheet

Special thanks to DataCamp, Asimov Institute, RStudios and the open source community for their content contributions. You can see originals here:

Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/

Bokeh Cheat Sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Bokeh_Cheat_Sheet.pdf

Data Science Cheat Sheet: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics

Data Wrangling Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

Data Wrangling: https://en.wikipedia.org/wiki/Data_wrangling

Ggplot Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

Keras Cheat Sheet: https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs

Keras: https://en.wikipedia.org/wiki/Keras

Machine Learning Cheat Sheet: https://ai.icymi.email/new-machinelearning-cheat-sheet-by-emily-barry-abdsc/

Machine Learning Cheat Sheet: https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet

ML Cheat Sheet:: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY

Matpotlib: https://en.wikipedia.org/wiki/Matplotlib

Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/

Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/

Neural Networks: https://www.quora.com/Where-can-find-a-cheat-sheet-for-neural-network

Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE

NumPy: https://en.wikipedia.org/wiki/NumPy

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM

Pandas: https://en.wikipedia.org/wiki/Pandas_(software)

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc

Pyspark Cheat Sheet: https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ

Scikit Cheat Sheet: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet

Scikit-learn: https://en.wikipedia.org/wiki/Scikit-learn

Scikit-learn Cheat Sheet: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Scipy Cheat Sheet: https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI

SciPy: https://en.wikipedia.org/wiki/SciPy

TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html

Data Science, Machine Learning, Deep Learning, and Artificial intelligence are really hot at this moment and offering a lucrative career to programmers with high pay and exciting work.

Data Science, Machine Learning, Deep Learning, and Artificial intelligence are really hot at this moment and offering a lucrative career to programmers with high pay and exciting work.

It's a great opportunity for programmers who are willing to learn these new skills and upgrade themselves and want to solve some of the most interesting real-world problems.

It's also important from the job perspective because Robots and Bots are getting smarter day by day, thanks to these technologies and most likely will take over some of the jobs which many programmers do today.

Hence, it's important for software engineers and developers to upgrade themselves with these skills. Programmers with these skills are also commanding significantly higher salaries as data science is revolutionizing the world around us.

You might already know that the Machine learning specialist is one of the top paid technical jobs in the world. However, most developers and IT professionals are yet to learn this valuable set of skills.

For those, who don't know what is a Data Science, Machine learning, or deep learning, they are very related terms with all pointing towards machine doing jobs which is only possible for humans till date and analyzing the huge set of data collected by modern day application.

Data Science, in particular, is a combination of concepts such as machine learning, visualization, data mining, programming, data mugging, etc.

If you have some programming experience then you can learn Python or Rto make your carer as a Data Scientist.

There are a lot of popular scientific Python libraries such as Numpy, Scipy, Scikit-learn, Pandas, which is used by Data Scientist for analyzing data.

To be honest with you, I am also quite new to Data Science and Machine learning world but I have been spending some time from last year to understand this field and have done some research in terms of best resources to learn machine learning, data science, etc.

I am sharing all those resources in a series of a blog post like this. Earlier, I have shared some courses to learn **TensorFlow**, one of the most popular machine-learning library and today I'll share some more to learn these technologies.

These are a combination of both free and paid resource which will help you to understand key data science concepts and become a Data Scientist. Btw, I'll get paid if you happen to buy a course which is not free.

Here is my list of some of the best courses to learn Data Science, Machine learning, and deep learning using Python and R programming language. As I have said, Data Science and machine learning work very closely together, hence some of these courses also cover machine learning.

If you are still on fence with respect to choosing Python or R for machine learning, let me tell you that both Python and R are a great language for Data Analysis and have good APIs and library, hence I have included courses in both Python and R, you can choose the one you like.

I personally like Python because of its versatile usage, it's the next best in my list of language after Java. I am already using it for writing scripts and other web stuff, so it was an easy choice for me. It has also got some excellent libraries like Sci-kit Learn and TensorFlow.

Data Science is also a combination of many skills e.g. visualization, data cleaning, data mining, etc and these courses provide a good overview of all these concepts and also presents a lot of useful tools which can help you in the real world.

**Machine Learning by Andrew Ng**

This is probably the most popular course to learn machine learning provided by Stanford University and Coursera, which also provides certification. You'll be tested on each and every topic that you learn in this course, and based on the completion and the final score that you get, you'll also be awarded the certificate.

This course is free but you need to pay for certificates, if you want. Though, it does provide value to you as a developer and gives you a good understanding of the mathematics behind all the machine learning algorithms that you come up with.

I personally really like this one. Andrew Ng takes you through the course using Octave, which is a good tool to test your algorithm before making it go live on your project.

1.**Machine Learning A-Z: Hands-On Python and R --- In Data Science**

This is probably the best hands on course on Data Science and machine learning online. In this course, you will learn to create Machine Learning Algorithms in Python and R from two Data Science experts.

This is a great course for students and programmers who want to make a career in Data Science and also Data Analysts who want to level up in machine learning.

It's also good for any intermediate level programmers who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

**2.** **Data Science with R by Pluralsight**

Data science is the practice of transforming data into knowledge, and R is one of the most popular programming language used by data scientists.

In this course, you'll learn first learn about the practice of data science, the R programming language, and how they can be used to transform data into actionable insight.

Next, you'll learn how to transform and clean your data, create and interpret descriptive statistics, data visualizations, and statistical models.

Finally, you'll learn how to handle Big Data, make predictions using machine learning algorithms, and deploy R to production.

Btw, you would need a Pluralsight membership to get access this course, but if you don't have one you can still check out this course by taking their **10-day free Pass**, which provides 200 minutes of access to all of their courses for free.

**3.**** ****Harvard Data Science Course**

The course is a combination of various data science concepts such as machine learning, visualization, data mining, programming, data mugging, etc.

You will be using popular scientific Python libraries such as Numpy, Scipy, Scikit-learn, Pandas throughout the course.

I suggest you complete the machine learning course on course before taking this course, as machine learning concepts such as PCA (dimensionality reduction), k-means and logistic regression are not covered in depth.

But remember, you have to invest a lot of time to complete this course, especially the homework exercises are very challenging

In short, if you are looking for an online course in data science(using Python), there is no better course than Harvard's CS 109. You need some background in programming and knowledge of statistics to complete this course.

**4.** **Want to be a Data Scientist? (FREE)**

This is a great introductory course on what Data Scientist do and how you can become a data science professional. It's also free and you can get it on Udemy.

If you have just heard about Data Science and excited about it but doesn't know what it really means then this is the course you should attend first.

It's a small course but packed with big punches. You will understand what Data Science is? Appreciate the work Data Scientists do on a daily basis and differentiate the various roles in Data Science and the skills needed to perform them.

You will also learn about the challenges Data Scientists face. In short, this course will give you all the knowledge to make a decision on whether Data Science is the right path for you or not.

**5.** **Intro to Data Science by Udacity**

This is another good Introductory course on Data science which is available for free on Udacity, another popular online course website.

In this course, you will learn about essential Data science concepts e.g. Data Manipulation, Data Analysis with Statistics and Machine Learning, Data Communication with Information Visualization, and Data at Scale while working with Big Data.

This is a free course and it's also the first step towards a new career with the Data Analyst Nanodegree Program offered by Udacity.

**6.** **Data Science Certification Training --- R Programming**

The is another good course to learn Data Science with R. In this course, you will not only learn R programming language but also get some hands-on experience with statistical modeling techniques.

The course has real-world examples of how analytics have been used to significantly improve a business or industry.

If you are interested in learning some practical analytic methods that don't require a ton of maths background to understand, this is the course for you.

**7.** **Intro To Data Science Course by Coursera**

This course provides a broad introduction to various concepts of data science. The first programming exercise "Twitter Sentiment Analysis in Python" is both fun and challenging, where you analyze tons of twitter message to find out the sentiments e.g. negative, positive etc.

The course assumes that you know statistics, Python, and SQL.

Btw, It's not so good for beginners, especially if you don't know Python and SQL but if you do and have a basic understanding of Data Science then this is a great course.

8. **Python for Data Science and Machine Learning Bootcamp**

There is no doubt that Python is probably the best language, apart from R for Data Analysis and that's why it's hugely popular among Data Scientists.

This course will teach you how to use all important Python scientific and machine learning libraries Tensorflow, NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, and many more libraries which I have explained earlier in my list of useful machine learning libraries.

It's a very comprehensive course and you will how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

**9.** **Data Science A-Z: Real-Life Data Science Exercises Included**

This is another great hands-on course on Data Science from Udemy. It promises to teach you Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more.

This course will give you so many practical exercises that the real world will seem like a piece of cake when you complete this course.

The homework exercises are also very thought-provoking and challenging. In short, If you love doing stuff then this is a course for you.

10. **Data Science, Deep Learning and Machine Learning with Python**

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry --- and help you to become a data scientist.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers, that makes it even more special and useful.

That's all about some of the **popular courses to learn Data Science**. As I said, there is a lot of demand for good Data Analytics and there are not many developers out there to fulfill that demand.

It's a great chance for the programmer, especially those who have good knowledge of maths and statistics to make a career in machine learning and Data analytics. You will be awarded exciting work and incredible pay.

Other useful **Data Science and Machine Learning** resources

Top 8 Python Machine Learning Libraries

5 Free courses to learn R Programming for Machine learning

5 Free courses to learn Python in 2018

Top 5 Data Science and Machine Learning courses

Top 5 TensorFlow and Machine Learning Courses

10 Technologies Programmers Can Learn in 2018

Top 5 Courses to Learn Python Better

How a Japanese cucumber farmer is using deep learning and TensorFlow

Thanks, You made it to the end of the article ... Good luck with your Data Science and Machine Learning journey! It's certainly not going to be easy, but by following these courses, you are one step closer to becoming the Machine Learning Specialists you always wanted to be.