My attempt at making a One Stop for ML Learners

Primer to Machine Learning

A structured guide designed to help you explore and master various aspects of machine learning and data science. This repository covers a broad range of topics, from foundational concepts in statistics and data preprocessing to advanced techniques in neural networks, natural language processing, time series analysis and reinforcement learning. Each section includes theoretical insights, practical applications, and links to recommended resources, making it a valuable tool for learners at all levels, from beginners to seasoned professionals. Whether you are looking to understand the basics or implement cutting-edge algorithms, this repository provides everything you need to start or advance your journey in the field of machine learning.

Index

1. Statistics for Machine Learning

2. Data Preprocessing and Feature Engineering

3. Optimization

4. Supervised Learning

5. Model Evaluation and Validation

6. Unsupervised Learning

7. Neural Networks and Deep Learning

8. Natural Language Processing

9. Time Series Analysis

10. Reinforcement Learning

Illustrations

The following images are the output of the codes of various algorithms,

>> PCA - Principal Component Analysis

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>> Linear Regression

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>> Support Vector Regression

--> Added links and resources

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>> Decision Tree Classifier

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>> K Means Clustering

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>> DBSCAN

>> ICA: Independent Component Analysis

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>> Autoencoders

--> Dimensionality Reduction using Autoencoders

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>> CNN - Convolutional Neural Networks

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>> LSTMs - Long Short Term Memory Models

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>> GANs : Generative Adversarial Networks

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>> STL - Seasonal Decomposition Of Time Series

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>> Moving Average Smoothing (Time Series Analysis)

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>> Exponential Smoothing (Time Series Analysis)

>> ARIMA - Time Series Forecasting

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>> SARIMA - Time Series Forecasting

>> Prophet (Forecasting)

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>> AND MUCH MORE