jose portilla data science

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming.

reddit.com › r/datascience › just finished jose portilla's udemy course on data science and machine learning. sharing thoughts and looking for suggestions on further learning

r/datascience on Reddit: Just Finished Jose Portilla's Udemy course on Data Science and Machine Learning. Sharing thoughts and looking for suggestions on further learning

May 23, 2022 -

I'm a data professional of 4+ years by the way of a biostatistics heavy masters-my work experience included first using STATA to do more inferential statistics stuff (GLMs, survival analysis) and then I learned R to do a bunch of stuff outside of even statistics and programming/automating. Though I have the years of experience, coding skills and took some short-courses on ML/data science here and there I never actually took a full-length DS/ML dedicated course and nor have I used ML at work so decided to enroll in this course to help me learn ML in a deeper sense (no pun intended hehe).

I took Jose Portilla's General Python programming course and thought it was excellent for me. I have tried learning Python previously through books (Learn Python the Hard Way) and websites like codecademy and data camp, but this course really helped me learn all the fundamentals. After taking the course and doing some practice problems, I feel comfortable enough to know how to program in Python.

I decided to then take his ML/DS bootcamp. As I went through the lectures, I found it much harder to follow/keep up with the course than the programming course. I will give him credit that in 25 hours he was trying to teach both fundamentals and syntax of Python's data science libraries so because he couldn't realistically do all that (and did refer to introduction to statistical learning as a companion source), the course weighed heavily on syntax. As a result, I ended up finding it hard to focus and started skimming through most of the lectures (and took me months to finish this course because I felt so disengaged at some point). I feel comfortable with pandas and the visualization libraries in Python (I'm already extremely comfortable with tidyverse and ggplot2 in R) through this course but when it came to the ML lectures, I still don't feel comfortable with unsupervised/deep learning and Spark.

I came to the conclusion I don't feel like lectures/videos are really the best method to learn this material. Does anybody have any suggestions as to what worked for you (especially those who have not done DS as a degree)? I have looked through the Introduction to Statistical Learning book years ago and started looking into it again now but sometimes that can get overwhelming as well.

Insight is appreciated. Thank you!

Top answer

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I liked Jose Portilla's DS/ML masterclass course a lot but I took an entire year to work through it. It was also tough for me at times, and I didn't have the energy to do the exercises that accompanied the course mostly, I think, because I needed more "real" challenges - if that makes sense. The exercises were good - just really not my cup of tea. But the interesting thing is, nowadays I'm really not bad with the (basic) theory of machine learning as many of the lectures stuck with me. It proved to be a really good basis for interviews, too, where I could use the knowledge I acquired on classification even though I've only done regression problems so far. Since I finished the course I have mostly learned by working on projects. I had some gaps in my statistics knowledge, so working on and solving a project (from data sourcing to showcasing my PoC), made me jump leaps ahead of where I was 6 months ago. Application of knowledge in a project is where the work actually gets interesting but is also where your mettle is tested, skill-wise. You have an extensive background in statistics so that should be good. The question is: how much deeper do you want/need to go? ML has so many different sub-domains, maybe doing a project could help you figure out where else you could invest your time and energy in - in order to get the best out of it for you. For example, maybe your statistics is sufficient for now but working on your coding/ETL skills might really make you jump ahead. When you do a project front to end you'll figure that out very quickly. 😄

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Does anybody have any suggestions as to what worked for you Books + select lectures from other unis. I hop aroud until I found whatever clicks for me. Going to sound cliché but projects help so much as well. I think the moment that deep learning became 100 % clear to me was when I started doing CNN based ensemble's. Coding these from scratch is what helped me the most in the long run.

Discussions

Anyone here took Jose Portilla's Udemy course? What's the overall review of his course?

I’ve taken several Jose Portilla's courses and they’re all quite good. He mixes theory and practice pretty well, and if the courses are cheap I think is worth it to give it a try 👌🏽 More on reddit.com

r/learnmachinelearning

August 10, 2025

I just finished Jose Portilla's Data Science and ML Python Bootcamp on Udemy. Am I ready to try and make…

I have Jose’s class from Udemy. And I recently completed DCs Python for Data Science track. I preferred DC for learning basic numpy, matplotlib, pandas. But for ml i really like how jose walks you through the project. I found DCs machine learning classes a little to dense and frustrating. I would take notes and then find a better source to learn the info. Im and old database developer, so Im just learning DS to see what all the hype is. Not taking it to seriously. More on amp.reddit.com

r/datascience

August 5, 2020

Complete a Data Science and Machine Learning course,now what?!

Look here, now you have 2 remaining wishes :)

https://github.com/clone95/Machine-Learning-Study-Path-March-2019

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