RESOURCES TO LEARN MACHINE LEARNING ALGORITHMS

In this repository, I have curated all the materials one can use to study SQL, Data Analysis, and Machine learning Algorithms.

Table of Contents

1. YouTubers/Playlists to Follow
2. Probability and Statistics
3. Python
4. SQL
5. Machine Learning
6. Classical Machine Learning Algorithms
7. Case Studies

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YOUTUBERS/PLAYLISTS TO FOLLOW:

• KRISH NAIK COMPLETE MACHINE LEARNING PLAYLISTS
• CampusX
• Data Monk Channel - Machine Learning
• Data Professor
• StatQuest with Josh Starmer
• Cory Schafer
• Codebasics
• Data School
• Siddhardhan
• The Data Scientist Show by Daliana Liu - DATA PODCAST
• Ken Jee
• 3Blue1Brown
• Sentdex
• 365 Data Science
• Data Science Dojo
• Data Camp
• Data Science Jay
• Abhishek Thakur
• Two Minute Papers

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1. PROBABILITY AND STATISTICS:

• Introduction to Probability
• Statistics A/B testing Interview Questions

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2. PYTHON:

• Python for Data Analysis by DataDraft - Youtube Playlist
• Corey Schafer - Youtube Channel

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3. SQL:

PRACTICE:

• DataLemur Questions
• StrataScatch Questions
• Interview Query Questions
• DataFord Io Questions
• LeetCode Questions
• Hackerrank Questions
• Analyst Builder Questions
• Data with Danny: 8 Week SQL Challenge

INTERVIEW QUESTIONS:

• StrataScratch: Interview Questions - Youtube
• Tina Huang’s SQL Sunday Series: Interview
• SQL Interview Prep - GitHub
• Clever Programming SQL Interview Questions
• Ken Jee’s Simple SQL Fundamentals
• Alex The Analyst’s YouTube SQL Playlists
• Data Science Jay’s SQL Mock Interview
• Edureka Top 65 SQL Interview Questions and Answers
• Top 23 SQL Interview Questions

LEARNING:

• GeeksForGeeks: 30 Days of SQL Basic to Advance
• Crack Concepts SQL - Youtube Channel
• Ankit Bansal - SQL - Youtube Channel
• MySQL Playlist by Krish Naik - Youtube Channel
• Intro to SQL by Mode Analytics
• SQLZoo: SQL Tutorial
• W3Resources: SQL Exercises

GENERAL RESOURCES:

• 5 Common SQL Interview Problems for Data Scientists

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4. MACHINE LEARNING:

INTERVIEW:

• Data Science Interviews by Krish Naik

LEARNING:

• DATA SCIENCE FOR BEGINNERS BY CODEBASICS - YOUTUBE PLAYLIST
• Unsupervised Machine Learning by Cognitive Class (Project Based)

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5. CLASSICAL MACHINE LEARNING ALGORITHMS

• All Learning Algorithms Explained in 14 Minutes

A. Linear Regression

• 30 Questions to test a Data Scientist on Linear Regression
• Linear Regression - Understand Everything (Theory + Maths + Coding) in 1 video
• 5 Types of Regression and their properties
• Ridge Regression - Clearly Explained
• Lasso Regression - Clearly Explained

B. Logistic Regression

• Logistic Regression In-depth Maths Intuition In Hindi by Krish Naik
• All about Logistic Regression in one article
• Understanding Logistic Regression step-by-step
• Logistic Regression - Short and Clear Explanation - 9 Mins: Video
• Linear Regression vs Logistic Regression: Video
• 30 Questions to test a Data Scientist on Logistic Regression
• Logistic Regression - Understand Everything (Theory + Maths + Coding): Video
• Lasso, Ridge and Logistic Regression: Video

C. Tree-Based/Ensemble Algorithms

• 30 Questions to test a Data Scientist on Tree based models
• Gini-index v/s Information Entropy
• Decision Tree vs. Random Forest – Which Algorithm Should you Use?
• Why Random Forest doesn't work well for Time-Series?
• Comprehensive guide to Ensemble Models
• The Simple Math behind 3 Decision Tree Splitting criteria

D. K-Nearest-Neighbors

• Fundamental Interview Questions on KNN - A Quick refresh
• 30 Questions to test a Data Scientist on KNN
• Pros and Cons of KNN
• KNN Algorithm - Understand Everything (Theory + Maths + Coding) in 1 video

E. Support Vector Machines

• All about SVMs - Math, Terminology, Intuition, Kernels in one article
• 25 Questions to test a Data Scientist on SVMs

F. Naive Bayes

• 12 tips to make the most out of Naive Bayes
• Naive Bayes - Understand Everything (Theory + Maths + Coding) in 1 video
• 6 easy steps to learn Naive Bayes

G. Feature Selection

• Awesome Feature Engineering for Machine Learning: GitHub

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6. CASE STUDIES:

The best way to approach such a question is to have a framework -

1. Ask questions to narrow down the problem area
2. Suggest and use feedback to decide on business metrics relevant to the problem
3. Decide the best ML formulation (classification/forecasting/recommendation)
4. Decide on model metrics that can tie to business metrics.
5. Suggest which models you would experiment with
6. Explain how you would productionalize.
7. Explain how you would A/B test the final model

LEARNING:

• Product Case Interviews for Data Science Jobs by Emma Ding: Youtube Playlist

RESOURCES:

• Dawn of Taxi Aggregators
• Optimizing product prices for an online vendor
• Tips for a Case-Study Interview
• Mercari Price Prediction
• End-to-End multiclass Text Classification pipeline
• End-to-End multiclass Image Classification pipeline
• Large Scale Forecasting for 1000+ products - Nagarro
• Clustering and Classification in E-Commerce
• The ABCs of Learning to Rank
• Data Science Case Study: Optimizing Product Placement in Retail

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