5 reasons to join my Supervised Machine Learning course

I’ve recently launched my online course about Supervised Machine Learning in Python. In this post, I’ll explain to you 5 reasons it’s worth joining it.

1 – It’s practical

In my experience as a Data Scientist and Physicist I’ve understood that, sometimes, practice is more important than theory. Data Science is a very practical discipline and a data scientist is like a craftsman working data just like wood. He needs to know how the wood responds to his tools, but his job is more practical than theoretical.

A theoretical lesson of my course

My online course gives you the right theoretical basis to understand the basic concepts of supervised machine learning, but every lesson has a practical Python example, whose code is written and explained by me step by step during the video and then reported ad the bottom of the video.

A practical lesson of my course

I believe that machine learning theory is fascinating and wonderful, but I don’t want to educate future college professors; my goal as a teacher is to educate data scientists that are able to do their job. That’s why I don’t go too deep in the theoretical part, giving to the student the right theoretical elements to better understand the practical part in Python.

2 – It’s more than 10 hours long

My supervised machine learning course alone takes more than 10 hours. Some courses are 10-14 hours long and try to teach supervised machine learning, Pandas, Numpy, data pre-processing and unsupervised machine learning. I hate this approach because the student loses interest or experience rough educational content that doesn’t focus on the right things. I think that a student that pays for a course must have what he paid for. That’s why I’ve split the several components of Data Science into several courses in my school. I prefer focusing on the right things for the right time and that’s something that helps my student learning better.

3 – It focuses on the right topics

Let’s face it: when it comes to talking about machine learning, people usually think about models. Models, models, models… everything seems to move around models. That’s completely wrong.

Models are important and powerful, but the most important part of machine learning is feature importance. Only if you calculate the importance of the features properly you are going to master your model and control it. If you don’t understand what your model does, you are not achieving the right goal of a data scientist, that is to find the information behind data.

I spend an entire section of my course talking about feature importance and another section on how to use feature importance for dimensionality reduction because I strongly believe in this part of machine learning and in its importance for a data scientist’s job.

Feature importance and RFE sections of my course

4 – You can attend the lessons when you want

The lessons of my course are recorded, so you can attend them when you want and where you want. Just use a laptop and an internet connection. I strongly believe in 1:1 teaching, but I also believe that everybody has different needs according to their job, family, spare time. So, it’s important to attend the lessons of a course according to a personal time schedule.

That’s why I’ve created this recorded video course to help students around the world attend my lessons according to their own time availability.

5 – It’s based on my first-hand experience

I’m a data scientist. That’s the job that has given me my salary for years and I have condensed all my knowledge about supervised machine learning in this course. So, I’m not a college professor teaching theoretical details that you’ll never see in a daily job; I’m a professional data scientist teaching the practical applications of machine learning according to a multi-year experience in this field. I love theory and theoretical lessons, but in this course, my goal is to teach how to do a particular job, so I’ve decided to be more practical and pragmatic following what I have learned in my experience. That is, I think, a great value for my course.

Conclusions

I’ve created this online course to teach supervised machine learning to everybody who wants to know more about it or wishes to become a data scientist. It’s created after years of experience, focusing on the practical topics and techniques that a data scientist has to use in his everyday job.

If you are interested in my course, join it now.

Upcoming webinars

If you are interested in the topics of my supervised machine learning course, you can register for one of my upcoming webinars.

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