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Harpreet Sahota 🥑

Dec 29 • 7 tweets • 4 min read

Machine learning and Python go hand in hand.

Ready to take the first step towards a rewarding career in machine learning?

These 4 resources will help you learn Python and get started 👇🏽🧵

#100DaysOfCode #66DaysOfData #DeepLearning

1/ Python Principles

I've never seen anything like this course.

This is a text based course with an interactive coding environment that will teach you all the basics of Python.

There's lots of challenges and exercises, too.

This should take 2 weeks.

pythonprinciples.com/lessons/

2/ CognitiveClass' Python for Data Science

Spend 1 hour a day and you'll be done in a week.

cognitiveclass.ai/courses/python…

3/ Python for Everybody.

This course goes deeeep.

After this course you'll have a solid foundation in Python.

Spend 6 hours a week and you'll be done in 6 weeks.

py4e.com/lessons

4/ Coursera's Python for EverybodyLearn to Program and Analyze Data with Python.

Develop programs to gather, clean, analyze, and visualize data.

Spend 4 hours a week and you will be done in 6 weeks.

coursera.org/specialization…

All of this should take you 16 weeks to finish.

While going through these resources, find a dataset to work with and apply what you've learned.

Do projects.

It's the only way to solidify everything you've learned here.

@nevrekaraishwa2

For more resources on deep learning, be sure to follow me!

For SQL resources follow: @nevrekaraishwa2

For more Python and ML follow: @SanthoshKumarS_ @datawithsuman @Sumanth_077

For generative AI follow @Saboo_Shubham_

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More from @DataScienceHarp

Harpreet Sahota 🥑

@DataScienceHarp

Dec 30

Feature selection is a crucial part of building a good machine learning model.

But most data scientists don't think before they select features.

The fact is: feature selection in machine learning is not always necessary.

Here are 5 situation when you don't need it 👇🏽🧵

1. You have a small dataset that doesn't have many features.

If the data you're using is small and doesn't have many features, you don't need to do feature selection.

2. The features are already carefully selected

If the features you're using have already been carefully chosen and are important for the task you are trying to do, you don't need to do feature selection.

Read 7 tweets

Harpreet Sahota 🥑

@DataScienceHarp

Dec 29

The number one cause of machine learning model failure is data set drift.

Yet most data scientists and machine learning practitioners don't know why their data sets are drifting.

Here are 6 of the most common reasons for data set drift in machine learning 👇🏽🧵

What is dataset drift? It's when the statistical properties of a dataset change over time, which can negatively impact the performance of a machine learning model.

1. Changes in the data distribution:

The distribution of the data used to train the model may change over time, leading to dataset drift. This could be due to changes in the underlying process that generates the data, or due to changes in the data collection process itself.

Read 9 tweets

Harpreet Sahota 🥑

@DataScienceHarp

Dec 28

Calculus is the foundation of deep learning.

If you don't understand it, you won't get far in your journey.

Here are 3 resources that will quickly get you up to speed on the topic.

👇🏽🧵

1/ The Essence of Calculus by 3B1B

This will take you three hours to get through and you'll develop an intuition for the subject and a lay of the land.

youtube.com/playlist?list=…

2/ Khan Academy Calculus

Spend 4 hours a week on this and you'll be done in 5 weeks.

It goes deep into the topic and sets you up for solid understanding.

khanacademy.org/math/calculus-1

Read 6 tweets

Harpreet Sahota 🥑

@DataScienceHarp

Dec 27

Linear algebra is the backbone and foundation of machine learning (especially deep learning).

If you don't know this stuff, you won't get taken seriously in the field.

You don't need to spend hours searching for the best resources, I have 4 of them here for you 👇🏽🧵

1/ Essence of Linear Algebra by 3B1B

Grant has a way of explaining topics in an easy to understand way. The animations make this intuitive and easy to grasp.

Spend 3 hours to go through the playlist and you'll have a good lay of the land

youtube.com/playlist?list=…

2/ Khan Academy Linear Algebra course

This course saved my ass when I took linear algebra in college.

Spend 5 hours a week and you'll be done in 4 weeks.

khanacademy.org/math/linear-al…

Read 6 tweets

Harpreet Sahota 🥑

@DataScienceHarp

Dec 26

Want to get into machine learning in 2023?

Then you need to know statistics and probability.

If you don't know this stuff, you're just a data charlatan.

Here are four resources that will get you up to speed:

👇🏽🧵

First up is Khan Academy Statistics and Probability

Spend 7 hours a week on this you'll be done in 2 weeks and have a solid overview of the basics.

khanacademy.org/math/statistic…

Next is the Coursera Basic Statistics course.

This will go deeper than the Khan academy course.

Spend 3 hours a week and you'll be done in 8 weeks.

coursera.org/learn/basic-st…

Read 9 tweets

Harpreet Sahota 🥑

@DataScienceHarp

Dec 6

import os or import pathlib...

That is the question.

Let's talk about what to use and why

👇🏽🧵

Ultimately using the os library or the pathlib library depends on your personal preference and the requirements of your project.

Both libraries provide similar functionality for working with file paths and directories, but they have some key differences.

An advantage of using the os library is that it is a standard library that is included with Python.

You don't need to install any additional packages to use it.

It also has a long history and a large user base, so there is a wealth of documentation and examples available.

Read 6 tweets

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