Source: Deep Learning on Medium
In my journey to data science and machine learning, I have completed several massive open online courses (MOOCs) from different platforms such as DataCamp, edX, Coursera, and YouTube.
Among the numerous MOOCs out there, find below are my 3 favorite data science and machine learning specializations.
(i) Professional Certificate in Data Science (HarvardX, through edX):https://www.edx.org/professional…
Includes the following courses, all taught using R (you can audit courses for free or purchase a verified certificate):
- Data Science: R Basics;
- Data Science: Visualization;
- Data Science: Probability;
- Data Science: Inference and Modeling;
- Data Science: Productivity Tools;
- Data Science: Wrangling;
- Data Science: Linear Regression;
- Data Science: Machine Learning;
- Data Science: Capstone
(ii) Analytics: Essential Tools and Methods (Georgia TechX, through edX): https://www.edx.org/micromasters…
Includes the following courses, all taught using R, Python, and SQL (you can audit for free or purchase a verified certificate):
- Introduction to Analytics Modeling;
- Introduction to Computing for Data Analysis;
- Data Analytics for Business.
(iii) Applied Data Science with Python Specialization (the University of Michigan, through Coursera): https://www.coursera.org/special…
Includes the following courses, all taught using python (you can audit most courses for free, some require the purchase of a verified certificate):
- Introduction to Data Science in Python;
- Applied Plotting, Charting & Data Representation in Python;
- Applied Machine Learning in Python;
- Applied Text Mining in Python;
- Applied Social Network Analysis in Python.
MOOCs do provide an excellent coverage of fundamental data science and machine learning concepts. For anyone new to data science, I personally think it’s good to start your journey to data science with MOOCs. However, the main drawback of MOOCs is the lack of rigor. Most MOOCs are too superficial, they don’t delve much into the theoretical foundations of data science and machine learning. It is essential to supplement knowledge from MOOCs with knowledge of advance concepts in order to be well rounded in your data science knowledge and foundation.
In the next section, I will share some resources from Sebastian Raschka (author of the bestselling book Python Machine Learning) that will enable you to master the fundamentals of machine learning and deep learning with Python, scikit-learn, and TensorFlow.
Data Science, Machine Learning, and Deep Learning Resources from Sebastian Raschka
Before delving into some of the resources for data science, machine learning, and deep learning from Sebastian Raschka, let’s establish a little background about the bestselling author.
Who is Sebastian Raschka?
Sebastian Raschka is a machine learning researcher developing new deep learning architectures to solve problems in the field of biometrics with a focus on face recognition and privacy protection. Among others, his research activities include applications of machine learning to solve problems in (computational) biology. After receiving his doctorate from Michigan State University, Sebastian recently joined the University of Wisconsin-Madison as Assistant Professor of Statistics.
Among his other works is his book “Python Machine Learning,” a bestselling title at Packt and on Amazon.com, which received the ACM Best of Computing award in 2016 and was translated into many different languages, including German, Korean, Chinese, Japanese, Russian, Polish, and Italian. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle.
During my journey to data science, a friend of mine introduced me to the book “Python Machine Learning” by Sebastian Raschka.
This book provides a great introduction to data science, machine learning, and deep learning, with code included. The author explains fundamental concepts in machine learning in a way that is very easy to follow. Also, code is included, so you can actually use the code provided to practice and build your own models. I have personally found this book to be very useful in my journey as a data scientist. I would recommend this book to any data science aspirant. All that you need is basic linear algebra and programming skills to be able to understand the book.
Here is a synopsis of the book from the author himself:
“If you want to become a machine learning practitioner, a better problem solver, or maybe even consider a career in machine learning research, then this book is for you! However, for a novice, the theoretical concepts behind machine learning can be quite overwhelming. Yet, many practical books that have been published in recent years will help you get started in machine learning by implementing powerful learning algorithms. In my opinion, the use of practical code examples serve an important purpose. They illustrate the concepts by putting the learned material directly into action. However, remember that with great power comes great responsibility! The concepts behind machine learning are too beautiful and important to be hidden in a black box. Thus, my personal mission is to provide you with a different book; a book that discusses the necessary details regarding machine learning concepts, offers intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the most common pitfalls.” Sebastian Raschka, author of Python Machine Learning.
Sebastian Raschka’s Open Resources for Data Science, Machine Learning, and Deep Learning
Sebastian Raschka’s github account contains several repositories for data science, machine learning, and deep learning. These repositories include code that can be downloaded freely and modified as needed for tackling problems in data science and machine learning.
Here are some of his machine learning and deep learning repositories:
In summary, we’ve discussed essential data science, machine, and deep learning resources from Sebastian Raschka that will enable you to develop a deeper understanding of machine learning beyond what is provided by MOOCs. I have personally benefited from Sebastian Raschka’s works throughout my journey to data science. I hope you will find some of these resources of great you as you continue to educate yourself about machine learning.