Machine Learning MOOC Instructor Shares Insights

Machine Learning MOOC instructor Hadelin de Ponteves is the primary instructor of Machine Learning A-Z on Udemy. This blog post is a quick summary of a podcast featuring Hadelin and his perspective on data science.

Before building a Machine Learning MOOC, Hadelin worked at Google and Canal+, which is the French competitor to Netflix. He stated that his biggest challenge in data science was to build a recommended system for Canal+. Recommended systems are based on an algorithm that suggest what movies for the user to watch.

He states he was able to quickly land a job after graduating from college. Many corporations are starting a data science team, so demand is high.

What is Machine Learning?

Machine Learning is a broad field. It can be used to predict the future. It can used to find an unknown. It covers many sub-fields, and can also be referred to as Artificial Intelligence. Essentially, it involves machines that learn how to do things.

Data science and machine learning go hand in hand. Linear regression is an example of data science that requires machine learning. Other two important areas are classification and clustering. Other sub-fields are association rule learning, reinforcement learning, deep learning, and natural language processing.

R and Python are most commonly used tools. They have great libraries for machine learning. Hadelin’s Machine Learning MOOC covers all sub-fields, and it gives example in both R and Python.

R vs. Python, Which Is Better?

The debate over R vs Python is fruitless. The fact is they are both widely used, and each one has its strengths. If you are new to data science, then you should get familiar with both languages. This is the best way to learn which tool you prefer. For example,  Hadelin prefers R for visualization. This is the tool he used at Canal+. For deep learning, he prefers Python.

Finally, Hadelin states the best way to learn are to solve challenges base on real world problems. His favorite book on the topic is Data Science for Business. Once you have a grasp of data science concepts, this book will add value to your understanding.

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