The internet is now full of courses on Data Science and Machine Learning and it’s not surprising looking at the high prospects this field offers in terms of career. But it might be overwhelming to sift through a lot of seemingly similar courses to come to decision about the best one for you. Here’s a list of top 10 online courses in Machine Learning and Data Science, both free and paid that you can select from to achieve your learning goals.
- Data Science Specialization on Coursera
This is a specialization course offered by Johns Hopkins University. The course consists of 10 sub-courses which cover topics relating to the entire process of a data science project, starting from the acquisition of the tools and data to sharing results and creating your very own product. The course concentrates on the R language and gives hands-on practice on how to effectively use the language for your data analysis work. If you are completely new to data science and analytics, then you might find this course a little tough to follow. My recommendation would be to first take a beginner course on elementary statistics and Machine Learning Techniques. The course also does not cover deep learning techniques, so it’s more targeted to intermediate level students.
- Python for Data Science and Machine Learning Bootcamp on Udemy
Now, this is what you call a comprehensive and practical course. It’s a great point for beginners to start. It begins with the Python basics, in case you are not experienced in the language, and moves on to the different Python Machine Learning frameworks including NumPy, Pandas, Decision Trees, Support Vector Machines and even Deep Learning. However, since the course primarily targets beginners, it does not go into too much detail and to keep the student motivated, it has a very minimal theory. The low cost for the course has helped it sell out like hot cakes, with a 4.5 rating and almost 200,000 students enrolled.
- Tableau 10 A-Z: Hands-On Tableau Training For Data Science on Udemy
This can be counted as a foundation course for Data Science and can be very effective in jogging your brain in elementary statistics and data handling, particularly in data visualization. A tableau is nothing but a representation of data in a way that gives meaning to the data and helps people understand relationships between variables in the data. Through this course, you will learn how to best convert raw data into visualizations that are both attractive and informative. You will also learn how to perform a few operations on your data and effectively summarize the information gathered. On completing this course you will become your company’s go-to person for presentations and the heart of all company conferences.
- R Programming A-Z™: R For Data Science on Udemy
Although both Python and R are widely used in Data Science, R is more relevant and specialized for Data Science. Problem is, it does have quite a steep learning curve. This course will help you learn complex R concepts in a simple format. The course has a good combination of Theory and Practice and within no time you will find yourself performing matrix operations, building data frames, plotting data and creating beautiful visualizations in R. Instead of rushing to teach you ten different concepts in one go, the course takes you gradually step by step through the concepts. The course is organized to accommodate all the learning elements including spaced repetitions, practice and hands-on learning with real-world problems.
- The Data Science Course 2019: Complete Data Science Bootcamp on Udemy
As the name suggests, the course contains the whole package. It starts with the basic math behind data science concepts, followed by a comprehensive Python course from the fundamentals like installation of the software and its libraries, using variables, loops, and conditions, all the way to using advanced Python tools. The course goes on to teach how you can apply advanced statistical methods like clustering, Machine Learning and Deep Learning using Python. In the end, it explains how you can analyze your outputs using Tableau.
The entire course uses animations effectively to explain complex concepts, so the student is completely immersed and can actually visualize how the formulas are derived and how the data actually flows through the Neural Networks. By the end of the course, you can expect to have gained quite a solid foundation in Data Science and Machine Learning.
- Statistics for Data Science and Business Analysis on Udemy
This course is mainly focused on Data Science and does not cover Machine Learning. However, it’s great for those who are just starting out on their Data Science journey and is an excellent primer for the more advanced courses described in our list, like the Data Science Specialization Course or the R Programming A-Z course. It explains statistical concepts adeptly by using a combination of animations, case studies, and hands-on practice. Since it is a course mainly targeted at beginners, a lot of concepts are not explained in too much detail. The instruction is well-paced, to keep your attention span on point and the course will leave you ready and excited to take the next step in your data science quest.
- Machine Learning on Coursera
This is a free course offered by Coursera, with a reasonable price if you want a certificate. The course is highly interactive. There are a number of Video lectures, quizzes, and assignments, with the lectures presented by the Data Science guru Andrew Ng himself. The practical work is mainly done in MATLAB, which is not an open-source language and costly. So, if you’re more of a Python person, this might take you a little more time to catch up. The course starts with a refresher on linear algebra and goes on to explain machine learning algorithms like Support Vector Machines and Neural Networks and how to make the best use of these. At the end of the course, you will learn how to build some interesting Machine Learning Applications like a Photo OCR and a recommender system.
- Machine Learning Foundations: A Case Study Approach by the University of Washington on Coursera
This is course is meant to serve as a foundation for Coursera’s Machine Learning specialization courses. It uses an effective case-study based approach where they explain the workings of applications like sentiment analysis, price prediction, product recommendation and more. It makes use of the Python Environment to train students in using Machine Learning techniques to practically build and train models using regression, classification, clustering, and even Deep Learning. The entire course is well spaced out over 6 weeks so that you can learn at a steady pace. It focuses more on conceptual understanding rather than going too deep into the Machine Learning concepts to ensure that beginners don’t feel intimidated. By the end of the course, you will have a good understanding of machine learning. Alongside, you will enjoy creating some fun standard ML applications and learning how they work.
- Machine Learning with TensorFlow on Google Cloud Platform Specialization by Coursera
This course focuses on Machine Learning in collaboration with Google cloud computing technology. The course starts with quite an interesting take on how Google uses Machine Learning to update its services on a continual basis. For the students of this course, Google has provided free cloud credits through Qwiklabs to practice the concepts learned in the course, so students need not worry about paying for the service while practicing on the cloud. Following that there’s a brief course on Machine Learning and then one on applying these concepts in TensorFlow to quickly build complex models. In the end, you will learn the best practices in getting your data ready for optimal results and how to use both judgment and experimentation to further improve your models.
- Deep Learning Specialization in Coursera
Deep learning is a buzzword in the AI industry and if you’re looking to build your career in Machine Learning and Data Science, Deep Learning can give you the push that you need to move up higher on your career path. This course is conducted by Andrew Ng, who takes the students through complex Deep Learning concepts like CNN, RNN, LSTM, Dropout, etc. He starts by breezing through Neural Networks before diving head-on into Deep Learning. The course is not advised for complete beginners, but if you have taken a basic course on Neural Networks, prior to this, you will find it easier to follow. You will build a simple translation system and a face recognition system using TensorFlow. Once you are done with the course, you will be able to implement Deep Learning algorithms from scratch rather than use ready-coded libraries, allowing you much more control over the process.
These were my recommendations for the Top 10 Online Courses in Machine Learning and Data Science. I have tried to include a mix of courses that cater to students who are at different levels of Machine Learning, from beginner to advanced. Judging from how far you are along your Data Science and Machine Learning journey, what kinds of applications you wish to build and the level of complexity that you are comfortable with, you can choose a course or series of courses that suit you best.