ML-Learning-Resources

A list of Learning resouces for machine learning and deep learning. It consists of full learning plan and also have list of research papers, datasets, conference and talk and Projects.

• Learning Plan
• Books
• Other Free Courses
• Talks and Tutorials
• Benchmarks and Datasets
• ML-DL Projects
• Top Github Repositories
• Conferences
• Youtube Channels
• How to Contribute

Learning Plan

Categories	Modules
Data Science Tool Kit	1. Introduction to Python 2. Python for Data Science 3. Visualization in Python 4. Maths for Machine Learning 5. SQL 6. Data Analysis in Excel
Statistics	1. Exploratory Data Analysis 2. Inferential Statistics 3. Hypothesis Testing 4. EDA Projects
Introduction to ML 🌟	1. Introduction to Machine Learning 2. Types of Machine Learning 3. Applications of ML 4. Machine Learning Process
Data Preprocessing 📊	1. Data Collection and Cleaning 2. Data Transformation 3. Feature Engineering 4. Handling Missing Data 5. Scaling and Normalization
Machine Learning Algorithms🧠	Algorithms

Data Science Tool Kit

1. Intorduction to Python

• Learn Python - Full Course for Beginners
• Learn Python Programming Masterclass

2. Python for Data Science

• Python for Data Science and Machine Learning Bootcamp
• Python for Data Science - Course for Beginners - duration 12 hours
• Data Science With Python - duration 1 hour

3. Visulaization in Python

• Data Visualization | Intellipaat
• Python Data Visualization Projects Examples | Simplilearn

4. Maths for Machine Learning

• Mathematics for Machine Learning and data Science
• Essence of linear algebra

5. SQL

• SQL for data Analysis
• SQL for Data Science

6. Data Analysis in Excel

• Mastering Data Analysis in Excel
• Data Analusis in excel

---

Statistics

1. Exploratory Data Analysis

• Exploratory Data Analysis In Python

2. Inferential Statistics

• Inferential Statistics

3. Hypothesis Testing

• Hypothesis Testing in Data Science

4. EDA Projects

• EDA Projects
• EDA Projects- 40 examples

Introduction to ML

1. Introduction to Machine Learning

• Definition and Concepts
• Machine Learning vs Traditional Programming
• ML Applications

2. Types of Machine Learning

• Types of Machine learning

3. Applications of ML

• Image and Speech Recognition
• Natural Language Processing
• Recommender Systems
• Fraud Detection
• Autonomous Vehicles
• Applications of Machine learning

4. Machine Learning Process

• Data Collection and Cleaning
• Data Preprocessing
• Feature Selection and Engineering
• Model Selection and Training
• Evaluation and Fine-Tuning
• Build Your First Machine Learning Project

---

Data Preprocessing

1. Data Collection and Cleaning

• Data Cleaning
• Data Sources and Formats
• Handling Missing Data
• Outlier Detection and Removal

2. Data Transformation

• Data Transformation

• Normalization and Standardization and Scaling

• Log Transformation

• Binning and One-Hot Encoding

3. Feature Engineering

• Feature Construction
• Feature Selection

4. Handling Missing Data

• Complete Case Analysis
• Handling missing numerical data
• Handling missing categorical data
• Missing indicator
• KNN Imputer
• MICE

5. Scaling and Normalization

• Standarization
• Normalization

6. projects

• Data Cleaning

---

Machine Learning Algorithms

algorithms

• Linear Regression
• Gradient Descent
• Logistic Regression
• Support Vector Machines
• Naive Bayes
• K Nearest Neighbors
• Decision Trees
• Random Forest
• Bagging
• Adaboost
• Gradient Boosting
• Xgboost
• Principle Component Analysis (PCA)
• KMeans Clustering
• Heirarchical Clustering
• DBSCAN
• T-sne

---

Books

• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems- Aurélien Géron, O'Reilly Media, Inc.", 2022.

• Data Mining: Practical Machine Learning Tools and Techniques.
• Python Machine Learning

• The Hundred-Page Machine Learning Book
• Approaching (Almost) Any Machine Learning Problem
• Pattern Recognition and Machine Learning
• Python Data Science Handbook: Essential Tools for Working with Data
• Machine learning by tom mitchell

Other Free Courses

• Machine Learning Specialization by Andrew NG on Coursera
• Kaggle Courses
• Machine Learning Crash Course
• Machine Learning Course by EdX
• Machine Learning Specialization University of Washington on Coursera
• Introduction to Machine Learning Course by Udacity
• Machine Learning with Python by IBM

Talks and Tutorials

• Gradient Dissent - A Machine Learning Podcast by Weights & Biases
• This Week in ML & AI Podcast
• Data Skeptic
• Linear Digressions
• O'Reilly Data Show
• The Talking Machines
• Practical AI
• Lex Fridman Podcast
• Talk Python To Me

Benchmarks and Datasets

• Kaggle Dataset
• UCI Machine Learning Repository
• Google Dataset
• Microsoft Research Open Data
• VisualData Discovery
• AWS Registry of Open Data on AWS
• AwesomeData GitHub

ML DL Projects

• 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
• Papers with Codes

Top Github Repositories

• ML-For-Beginners by Microsoft
• ML-YouTube-Courses
• Mathematics For Machine Learning
• MIT Deep Learning Book
• Machine Learning ZoomCamp
• Machine Learning Tutorials
• Awesome Machine Learning
• Machine Learning cheatsheets
• Machine learning Interview
• Awesome Production Machine Learning

Conferences and Journals

• List of ML AI conferences in 2023 and 2024
• List of ML AI journals

Youtube Channels

• StatQuest with Josh Starmer
• deeplearning.ai
• Google DeepMind
• Sentdex
• Data School
• Abhishek Thakur
• Machine Learning with Phil

How to Contribute

send one of the maintainers a pull request.