ISL-python VS ydata-profiling

Here or here for the Python versions of ISLR.

I typically recommend a few different books to everyone who finishes the bootcamp, based on a self-assessment they take. I recommend some books based on their strengths, so they can find a career path sooner, and some books based on their weaknesses, so they can widen their cone of oppportunity within ML.

In our consultancy, data science is done in Python and SQL (and PySpark, but I don't hand out books on that during bootcamp!), and ML delivery is a combination of math, software engineering, and architecture/product owner disciplines.

If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.

If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.

If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.

If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.

If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)

For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code https://github.com/emredjan/ISL-python ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."

Anyway, I've spent a lot of time reading and reviewing books about ML, and my key takeaway is ones that get you closer to writing actual code to solving actual problems for actual people are the ones to focus on.

If you're not sure about the behaviour of your data (i.e., if the original data has properties like seasonality), you can use ydata-profiling to profile your data first.

Super cool! For EDA, you could give ydata-profiling a spin sometime and speed up the process!

Alternatively, if you found DCAI through pandas-profiling or ydata-synthetic you can find support for your troubleshooting and provide feedback on interesting features!

As a learning project, this is nice, but for standard use, what would be the advantage of this over just loading a program into Pandas and calling df.describe()? And if you need more complete details on a data set, using the pandas-profiling package?

Nice try making it clickable to generate different charts based on loaded data, but I can't help but notice that YOPO's functionality overlaps with another quite big tool called pandas-profiling. It automatically creates report in html or json format to explore dataset and has been used quite successfully in many production solutions.

Visions is a python library for working with user defined data type systems. Out of the box, it provides type inference and automated data cleaning of sequence data with backend specific implementations for pandas, spark, python, and numpy. We often use it as a first pass cleaning step when working with tabular data and to simplify the backend logic of both pandas-profiling and our tabular data compression library compressio.