ML & DS Guide

Complied Resources for learning Machine Learning & Data Science

Table of Contents

1. Path/Guide
2. Projects Ideas , Guide & Tutorial
3. Online Course, Books & YT Playlists :
4. Commonly Used Websites and YT Channels
5. Other’s Roadmap/Guides & Resources :

Path/Guide

Maths

Main Topics & Detailed Syllabus :

• Linear Algebra :
  • https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
  • https://www.geeksforgeeks.org/engineering-mathematics-tutorials/#alg
• Calculus :
  • https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
  • https://www.geeksforgeeks.org/engineering-mathematics-tutorials/#cal
• Stats :
  • https://www.youtube.com/watch?v=xxpc-HPKN28
  • https://www.geeksforgeeks.org/engineering-mathematics-tutorials/#stats
• Probability :
  • https://www.geeksforgeeks.org/engineering-mathematics-tutorials/#prob

Data Science Libs

Major/Imp Libs are Numpy, Pandas, Matplotlib, Seaborn,

• Numpy :
  • Video Tutorial: https://www.youtube.com/watch?v=QUT1VHiLmmI
  • Practice: https://github.com/rougier/numpy-100
  • Docs: https://numpy.org/doc/
• Pandas :
  • Video Tutorial: https://www.youtube.com/watch?v=vmEHCJofslg
  • Another Tutorial/Course with Practice labs: https://www.kaggle.com/learn/pandas
  • Practice: https://www.machinelearningplus.com/python/101-pandas-exercises-python/
  • Docs: https://pandas.pydata.org/pandas-docs/stable/index.html#
• Matplotlib :
  • Video Tutorial: https://www.youtube.com/watch?v=3Xc3CA655Y4
  • Practice :
    • #1 :https://towardsdatascience.com/11-visualization-examples-to-practice-matplotlib-4fe4c7dd665c
    • #2 : https://pynative.com/python-matplotlib-exercise/
  • Docs : https://matplotlib.org/stable/index.html
• Seaborn :
  • Tutorial :
    • #1 : https://www.youtube.com/watch?v=6GUZXDef2U0
    • #2 : https://elitedatascience.com/python-seaborn-tutorial
  • Practice :
    • #1 : https://www.kaggle.com/statchaitya/seaborn-practice-olympics-dataset
    • #2 : https://towardsdatascience.com/practical-data-analysis-with-pandas-and-seaborn-8fec3cb9cd16
    • #3 : https://www.kaggle.com/learn/data-visualization
  • Docs : https://seaborn.pydata.org/

Mini Project #1

Data Analysis Using Data Science Libraries

• Guided Project :
  • https://www.youtube.com/watch?v=eMOA1pPVUc4
  • https://medium.com/li-ting-liao-tiffany/solving-real-world-business-questions-with-pandas-70ef8ef02675
  • https://www.youtube.com/watch?v=Ewgy-G9cmbg (Includes Data Collection)
• Self Guided Project :
  • Investigating-Netflix-Movies-and-Guest-Stars-in-The-Office
    • Investigating-Netflix-Movies-and-Guest-Stars-in-The-Office Qanhnn12
    • Investigating-Netflix-Movies-and-Guest-Stars-in-The-Office anthonymanotoa
  • Check Data Analysis DataCamp Projects

Machine Learning Beginner Courses

Take Up few Beginner Courses to learn about the fundamentals of ML Models, ML Algorithms, Data Processing Technique, Model Evaluation etc .

• Kaggle Intro to Machine Learning
• Kaggle Intermediate Machine Learning
• Andrew Ng ML Course
• Udemy A-Z Machine Learning Course
• Sentdex ML Course
• Microsoft ML-For-Beginners
• Scikit Learn ML Course

Mini Projects #2 :

• Regression :
  • Boston House Price Prediction
• Classification :
  • Iris Classification
  • Red Wine Quality
• Clustering :
  • Customer Segmentation

Data Science Stuff

• Data Collection
  • Existing DataSets
  • API :
    • Tutorial
  • Scraping :
    • BeautifulSoup
    • Scrapy
• Databases:
  • SQL :
    • MYSQL
    • PostgreSQL
  • NOSQL :
    • MongoDB
• Data Preprocessing :
  • https://towardsdatascience.com/data-preprocessing-concepts-fa946d11c825
  • https://abhishekmaskey-67701.medium.com/data-preprocessing-in-machine-learning-325941a75f88
  • https://www.analyticsvidhya.com/blog/2021/08/data-preprocessing-in-data-mining-a-hands-on-guide/
  • https://serokell.io/blog/data-preprocessing
  • https://neptune.ai/blog/data-preprocessing-guide
• EDA :
  • https://towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15
  • https://medium.com/code-heroku/introduction-to-exploratory-data-analysis-eda-c0257f888676
  • https://medium.com/@raahimkhan_85173/data-cleaning-and-exploratory-data-analysis-with-pandas-on-trending-you-tube-video-statistics-e06d7cd08710
  • https://towardsdatascience.com/an-extensive-guide-to-exploratory-data-analysis-ddd99a03199e
  • https://medium.com/analytics-vidhya/how-to-ace-exploratory-data-analysis-d3821011532b
  • https://www.analyticsvidhya.com/blog/2020/10/the-clever-ingredient-that-decide-the-rise-and-the-fall-of-your-machine-learning-model-exploratory-data-analysis/

Machine Learning Stuff

• Feature Engineering
  • https://www.kaggle.com/learn/feature-engineering
  • https://www.kdnuggets.com/2018/12/feature-engineering-explained.html
  • https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114
  • https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/
  • https://www.analyticsvidhya.com/blog/2021/03/step-by-step-process-of-feature-engineering-for-machine-learning-algorithms-in-data-science/
• Feature Selection
  • https://www.kdnuggets.com/2021/06/feature-selection-overview.html
  • https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/
  • https://www.analyticsvidhya.com/blog/2020/10/feature-selection-techniques-in-machine-learning/
  • https://www.machinelearningplus.com/machine-learning/feature-selection/
• Hyper Parameter Tuning

Read in Details about the ML Algorithms from Books mentioned below

• Machine Learning Algorithms
  • Supervised ML Algorithms
    • Linear Regression:
      • Basics :
      • Tutorial :
      • Implementation :
      • Application :
    • Logistic Regression:
    • Decision Tree:
    • Naive Bayes
    • KNN
    • Random Forest:
    • AdaBoost
    • Gradient Boosting
      • GBM
      • XGBoost:
      • LightGBM
      • CatBoost
    • Unsupervised ML Algo
      • K Means
      • DBSSCAN
      • PCA
      • Hierarchal Clustering
    • Reinforcement
      • Deep Q Networks
      • Deep Deterministic Policy Gradient
      • A3C Algo
      • Q Learning
• Model Evaluation :
• Pipeline
• Model Deployment :
  • Flask WebApp & API
  • Fast API
  • Streamlit

Final Projects :

• House Prices - Advanced Regression Techniques : https://www.kaggle.com/c/house-prices-advanced-regression-techniques
• Titanic - Machine Learning from Disaster : https://www.kaggle.com/c/titanic

Projects Ideas , Guide & Tutorial

1. https://www.simplilearn.com/machine-learning-projects-for-beginners-article#1_movie_recommendations_with_movielens_dataset
2. https://data-flair.training/blogs/machine-learning-project-ideas/
3. [https://www.kdnuggets.com/2021/09/20-machine-learning-projects-hired.html](https://www.kdnuggets.com/2021/09/20-machine-learning-projects-hired.html
4. https://www.upgrad.com/blog/machine-learning-project-ideas-for-beginners/
5. https://www.crio.do/projects/category/machine-learning-projects/
6. https://analyticsindiamag.com/machine-learning-101-ten-projects-for-high-school-students-to-get-started/
7. https://github.com/prathimacode-hub/ML-ProjectKart
8. https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
9. https://www.kdnuggets.com/2021/06/top-10-data-science-projects-beginners.html

Online Course, Books & YT Playlists :

Books :

1. Introduction to Machine Learning with Python: A Guide for Data Scientists : https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413
2. Hands–On Machine Learning with Scikit–Learn and TensorFlow: https://www.amazon.in/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
3. An Introduction to Statistical Learning: https://www.statlearning.com/
4. The Elements of Statistical Learning : https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Courses :

1. https://developers.google.com/machine-learning/crash-course/
2. https://www.udemy.com/course/machinelearning/
3. https://www.udacity.com/course/machine-learning--ud262
4. https://www.udacity.com/course/intro-to-machine-learning--ud120
5. https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t
6. https://github.com/Yorko/mlcourse.ai
7. https://www.dataquest.io/
8. https://app.datacamp.com/learn/

YT Playlist :

1. Machine Learning —Andrew Ng
2. StatQuest with Josh Starmer
3. Abhishek Thakur
4. Ranji Raj

Commonly Used Websites and YT Channels

Sites :

1. https://elitedatascience.com/
2. https://www.kaggle.com/
3. https://towardsdatascience.com/
4. https://medium.com/
5. https://www.analyticsvidhya.com/
6. https://elitedatascience.com/
7. https://learn.datacamp.com/
8. https://www.dataquest.io/

YT Channels :

1. Artificial Intelligence - All in One
2. DigitalSreeni
3. Kaggle
4. 3Blue1Brown
5. DeepLearningAI
6. Two Minute Papers
7. Machine Learning TV
8. RANJI RAJ
9. Data School
10. Keith Galli
11. Daniel Bourke
12. StatQuest with Josh Starmer
13. Data Professor
14. Krish Naik

Other's Roadmap/Guides & Resources :

1. Applied ML : https://github.com/eugeneyan/applied-ml
2. Approaching ML Problems : https://github.com/abhishekkrthakur/approachingalmost
3. Data Science Res : https://github.com/jonathan-bower/DataScienceResources
4. ML for Software Engineers : https://github.com/ZuzooVn/machine-learning-for-software-engineers
5. ML Course : https://github.com/Yorko/mlcourse.ai
6. ML Cheatsheet : https://github.com/afshinea/stanford-cs-229-machine-learning
7. ML Projects : https://github.com/prathimacode-hub/ML-ProjectKart
8. Learning : https://github.com/amitness/learning
9. ML Algo Implementation : https://github.com/eriklindernoren/ML-From-Scratch
10. Detail ML Tutorials : https://github.com/ujjwalkarn/Machine-Learning-Tutorials
11. Awesome Data Science : https://github.com/academic/awesome-datascience
12. Microsoft DataScience : https://github.com/microsoft/Data-Science-For-Beginners
13. Microsoft ML : https://github.com/microsoft/ML-For-Beginners
14. https://towardsdatascience.com/a-complete-52-week-curriculum-to-become-a-data-scientist-in-2021-2b5fc77bd160