Ensemble Machine Learning in Python: Random Forest, AdaBoost (Udemy.com)

Perfect course for anyone pursuing machine learning. This course focuses on all fundamentals of ensemble methods along with practical based approaches. I have been recommending Lazy Programmer for courses on machine learning and AI to a lot of friends and colleagues. Lazy Programmer is the only instructor online who can make you excel in both theory and practice.

I always struggled to understand the idea behind boosting from several free resources I looked at on the Internet. But with a well curated machine learning course from the Lazy Programmer, I've finally come to a firm understanding of the concepts I once struggled with. It's a must have if you want to learn about ensemble methods.

A super well-constructed course that includes everything you need to understand random forests and boosted trees (some basic knowledge of math and statistics is still necessary). The host is sharp and doesn't waste time, a true professional, every word counts. All in all, I loved every moment in the course and I can definitely recommend it to anyone interested in machine learning. P. S. This course helped me land a job, so I'm eternally grateful to the Lazy Programmer. Well done!

Yet another marvellous course! After a series of courses that exceeded my ever-increasing expectations, I'm expecting the world now, and it still delivers :) I'm loving the come-back of maths in this course. As I've been doing for over a month now, I'll continue taking my time with each section, making sure I've learnt and I can derive everything myself before hopping on the next course. I'm deeply happy to have taken each and every one of Lazy Programmer's courses so far.

In general, the course is good as the instructor provides updated concepts, ideas, and codes to increase the efficiency of machine learning models from some techniques (e.g. Decision Trees and Random Forest) for classification and regression problems . The beginning of the course is a bit caught because it goes into a theoretical part and the instructor does not go into too much detail and does not have such a powerful didactic to do that and everything is left to his own deduction. I did not realize this at the time of purchase: the course description is about 5.5 hours, but about 2 and a half hours is just an appendix not related to the subject of the course. Tip / Hint: One way to improve the first part is to use this time to explain the mathematical formulas by working them out in real time as if you were a teacher explaining on the board. Some instructors use this technique and student retention is best.

I enjoyed the course. I am familiar with the topics discussed, and really enjoyed the more mathematical explanations that were given. The coding was pretty good and really gives me motivation to learn python much better and these concepts much better in order to code them myself. Overall pretty good course.

Best course for Trees, many concepts are taken from the ESL book which could be hard. However, the instructor makes the topics really easy to understand, and the fact that he connects what it is learn in each one of them with previous and advanced courses makes the learning curve less steep. The programming excercises are awesome as they are a vital component to really get the nitty gritty of the algorithms.

This class is a much appreciated rarity, in that it covers ensemble methods both separate from other areas of machine learning and beyond just a basic understanding of bagging and boosting. All explanations were clear and easy to follow. Comparisons between methods really helped in grasping the topic. Good as always Lazy Programmer!

Generally a fantastic course but I sometimes struggle to keep up with the maths. Having said that, it's been pretty clear from the start that people who are not willing to put the hours in will struggle, so it comes as no surprise. Perhaps a compromise would be to have a bit more narration aimed at those people who do struggle with the maths. For example, in the lecture 'coding with poly degree 12', I was in the dark as to what the output would look like before it was plotted, which was a bit demoralising. A few more sentences putting the logic in layman's terms would have helped - but aside from that I think these courses are fantastic.

I highly recommend this course for anyone learning or working on classification problems. For one, it teaches very useful tools that are known in the real world to improve models by 2-3% (a big deal). For another, it does a great job explaining the bias variance decomposition which sits at the heart of why or how these models work. 10 / 10. Would do it again. Major points for implementing boosting, bagging, and to some extent (pseudo) random forests 'from scratch'. Really helps understand exactly how the algorithm works and helps solidify theory.

Is to understand.

Good course with detailed explanations.