The following machine learning platforms and tools are available now as resources to seamlessly integrate the power of ML into daily tasks.
Machine learning platforms are not the wave of the future. It’s happening now. Developers need to know how and when to harness their power. Working within the ML landscape while using the right tools like Filestack can make it easier for developers to create a productive algorithm that taps into its power. The following machine learning platforms and tools — listed in no certain order — are available now as resources to seamlessly integrate the power of ML into daily tasks.1. H2O
H2O was designed for the Python, R, and Java programming languages by H2O.ai. By using these familiar languages, this open source software makes it easy for developers to apply both predictive analytics and machine learning to a variety of situations. Available on Mac, Windows, and Linux operating systems, H2O provides developers with the tools they need to analyze data sets in the Apache Hadoop file systems as well as those in the cloud.2. Apache PredictionIO
Developers who are looking for an open-source stack that also has an open-source server for machine learning built on top of it should take a look at Apache PredictionIO as a way to build predictive engines that can meet any artificial intelligence task. In addition to the event server and the platform itself, Apache PredictionIO also includes a template gallery.3. Eclipse Deeplearning4j
Eclipse Deeplearning4j is an open-source library built for the Java Virtual Machine. With deep learning as its core, this tool is aimed at those developers who need to build deep neural networks within business environments that work on distributed CPUs and GPUs. Scala, Clojure, and Java programmers who work with file systems like Hadoop and who have a DIY bent will appreciate Eclipse Deeplearning4j. Paid support and enterprise distribution are available for this tool, which is a project of the San Francisco-based company Skymind.4. Accord.NET Framework
Image and audio processing libraries are written in the C# programming language and then combined with the Accord.NET framework. Within it, developers can create a range of apps for commercial use that rely on machine learning such as computer vision, signal processing, pattern recognition, and machine listening, which is also known as computer audition. With multiple options to choose from, developers can utilize image and signal processing, scientific computing, and support libraries. Robust features such as real-time face detection, natural learning algorithms, and more add to the versatility of this framework.5. Microsoft
During the Ignite conference in September 2017, Microsoft launched three Azure machine learning tools — the Learning Bench, the Learning Model Management service, and the Learning Experimentation service — that allow developers to build their own artificial intelligence models. Three AI tools, Content Moderator, Custom Speech Service, and Bing Speech APIs, were also launched by Microsoft to add to its library of 25 developers’ tools that are designed to increase the accessibility of AI.6. ai-one
Developers can create intelligent assistants that are applicable to nearly any software application by using ai-one. This tool’s list of resources includes developer APIs, a document library, and building agents that can be used to turn data into rule sets that support ML and AI structures.7. IBM
IBM’s Watson platform is where both business users and developers can find a range of AI tools. Users of the platform can build virtual agents, cognitive search engines, and chatbots with the use of starter kits, sample code, and other tools that can be accessed via open APIs.8. Torch
With the Lua programming language as its base, Torch includes a scripting language, a scientific computing framework, and an open-source ML library. Torch supports deep machine learning through an array of algorithms and has been used by DeepMind and the Facebook AI Research Group.9. Protege
At first blush, it might appear that Protege’s focus on enterprises leaves little room for anything else. However, developers can take advantage of Protege’s open source tool suite that provides robust app tools for experts and knowledgeable beginners alike. Both groups of developers can modify, create, share, and upload apps as well as take advantage of a supportive community.10. TensorFlow
Specifically designed for use in projects that rely on machine learning, TensorFlow has the added benefit of being a platform designed using open source software. Aided by a plethora of online resources, documentation, and tutorials, TensorFlow provides a library that contains data flow graphs in the form of numerical computation. The purpose of this approach is that it allows developers to launch frameworks of deep learning across multiple devices including mobile, tablets, and desktops.11. DiffBlue
DiffBlue is that rather rare developer tool that’s an extremely useful yet simple platform dedicated to code automation. DiffBlue has several core purposes — test writing, bug location, refactor code, and the ability to discover and replace weaknesses — that are all accomplished with the use of automation.12. Neon
The brainchild of Intel and Nervana, Neon is an ML library that is based on Python and is open source to boot. Developers that utilize its tools can take advantage of technologically advanced apps and intelligent agents. Housed within the cloud, Neon supports developers as they launch, build, and train deep learning technologies.13. Apache Spark MLlib
As a framework that contains in-memory data processing, Apache Spark MLlib features an algorithms database with a focus on clustering, collaborative filtering, classification, and regression. Developers can also find Singa, an open-source framework, that contains a programming tool that can be used across numerous machines and their deep learning networks.14. OpenNN
A C++ programming library, OpenNN is aimed at those experienced developers who want to implement neural networks. OpenNN includes Neural Designer, a tool that aims to both interpret and simplify data entries with the creation of tables, graphs, and other visual content. Although OpenNN provides its users with an extensive library of tutorials and documentation, it’s primarily aimed at those developers who already have lots of AI experience.15. Amazon Web Services
Developers can take advantage of a number of AI toolkits offered by Amazon Web Services (AWS), which include Amazon Lex, Amazon Rekognition Image, and Amazon Polly. Each is used in a different way by developers to create ML tools. Amazon Polly, for example, takes advantage of AI to automate the process of translating voice to written text. Amazon Lex forms the basis of the brand’s chatbots that are used with its personal assistant, Alexa.16. Mahout
For developers who need to create applications that rely on ML in order to scale, there is Mahout. In addition to resources such as tutorials, Mahout provides beginning developers with the ability to use preconceived algorithms that can then be used with Apache Flink, Apaches Spark, and H2O.17. Veles
Written in C++ and using Python for node coordination, Veles is Samsung’s contribution to the ML landscape. Those developers who already need an API that can be used immediately for data analysis and that is comprised of trained models will find value in Veles.18. Caffe
Caffe was developed by the Berkeley Vision and Learning Center (BVLC) in collaboration with a developer community. It was designed to provide developers with an automatic inspection tool that is based on images. Caffe is used by some of the biggest brands in the world, including Pinterest and Facebook.Get Started With These Machine Learning Platforms
Developers who are just launching their careers, as well as those who are experts, will find a treasure trove of resources as they work their way through the above list. While some are dependent on a specific programming language, others can be used in a variety of instances including in the cloud. Both software and cloud-based offerings allow developers to take advantage of the benefits of each.
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 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 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:
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
Here is a list of the key concepts you need to know:
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.
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 DataDownloadable PDF of Best AI Cheat Sheets in Super High Definition
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.
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
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.
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
Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/
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
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
Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY
Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/
Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/
Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM
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 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
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.
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.
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.
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.
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
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.