Primer to Machine Learning

Overview

Welcome to the Primer to Machine Learning repository, 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, 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.

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1. Statistics for Machine Learning

1.1 Descriptive Statistics

• Continuous and Discrete Functions
• Probability Distribution
• Gaussian Normal Distribution
• Measure of Frequency and Central Tendency
• Measure of Dispersion
• Skewness and Kurtosis
• Normality Test

1.2 Regression Analysis

• Linear and Non-Linear Relationship with Regression
• Goodness of Fit
• Underfitting, Appropriate fit and Overfitting

1.3 Inferential Statistics

• t-Test, z-Test
• Hypothesis Testing
• Type I and Type II Errors
• One-way ANOVA
• Chi-Square Test
• Implementation of Continuous and Categorical Data

Suggested Introductory Resources

• Khan Academy: Probability & Statistics
• Coursera: Statistics with Python

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2. Data Preprocessing and Feature Engineering

2.1 Data Cleaning

• Handling Missing Data
• Data Normalization and Standardization

2.2 Data Transformation

• Categorical Data Encoding
• Text Data Preprocessing

2.3 Feature Engineering

• Feature Creation
• Feature Selection

2.4 Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Linear Discriminant Analysis (LDA)

2.5 Outlier Detection and Treatment

• Statistical Methods for Outlier Detection
• Machine Learning Methods for Outlier Detection

Suggested Introductory Resources

• Kaggle: Data Cleaning
• Coursera: Feature Engineering

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3. Optimization

3.1 Gradient Descent

• Batch Gradient Descent
• Stochastic Gradient Descent
• Mini-batch Gradient Descent

3.2 Advanced Optimization Algorithms

• Adam
• RMSprop
• Adagrad

3.3 Hyperparameter Tuning

• Grid Search
• Random Search
• Bayesian Optimization

3.4 Constrained Optimization

• Linear Programming
• Quadratic Programming

3.5 Genetic Algorithms

• Basic Genetic Algorithm

Suggested Introductory Resources

• Coursera: Neural Networks and Deep Learning
• Kaggle: Intro to Optimization

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4. Supervised Learning

4.1 Regression

4.1.1 Linear Regression

• Simple Linear Regression
• Multiple Linear Regression

4.1.2 Polynomial and Regularized Regression

• Polynomial Regression
• Ridge and Lasso Regression
• Elastic Net Regression

4.1.3 Tree-Based and Neighbor-Based Regression

• Decision Tree Regression
• Random Forest Regression
• K-Nearest Neighbors Regression (KNN)

4.1.4 Advanced Regression Methods

• Support Vector Regression (SVR)
• Bayesian Regression
• Locally Weighted Linear Regression (LWLR)

4.1.5 Dimensionality Reduction-Based Regression

• Principal Component Regression (PCR)
• Partial Least Squares Regression (PLS)

4.2 Classification

4.2.1 Basic Classification Models

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Naive Bayes Classifier

4.2.2 Tree-Based Classifiers

• Decision Tree Classifier
• Random Forest Classifier

4.2.3 Advanced Classification Models

• Support Vector Machines (SVM)
• Gradient Boosting (XGBoost, LightGBM, CatBoost)

4.2.4 Neural and Ensemble Methods

• Neural Networks
• Ensemble Methods

Suggested Introductory Resources

• Coursera: Machine Learning by Andrew Ng
• Kaggle: Intro to Machine Learning

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5. Model Evaluation and Validation

5.1 Cross-Validation

• K-Fold Cross-Validation
• Stratified K-Fold Cross-Validation

5.2 Performance Metrics

• Classification Metrics
• Regression Metrics

5.3 Model Validation Techniques

• Train/Test Split
• Leave-One-Out Cross-Validation (LOOCV)

5.4 Advanced Validation Techniques

• Bootstrapping
• Hyperparameter Tuning with Cross-Validation

Suggested Introductory Resources

• Coursera: Evaluating Machine Learning Models
• Kaggle: Model Evaluation Metrics

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6. Unsupervised Learning

6.1 Clustering

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Gaussian Mixture Models
• Spectral Clustering

6.2 Dimensionality Reduction

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Independent Component Analysis (ICA)
• Autoencoders

6.3 Association Rule Learning

• Apriori Algorithm
• Eclat Algorithm
• FP-Growth Algorithm

Suggested Introductory Resources

• Coursera: Unsupervised Learning
• Kaggle: Unsupervised Learning

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7. Neural Networks and Deep Learning

7.1 Basics

• Perceptron
• Feedforward Neural Network

7.2 Training Techniques

• Backpropagation
• Batch Normalization
• Dropout

7.3 Convolutional Neural Networks (CNN)

• Basic CNN
• Advanced CNN Architectures

7.4 Recurrent Neural Networks (RNN)

• Basic RNN
• Long Short-Term Memory (LSTM)
• Gated Recurrent Unit (GRU)

7.5 Generative Models

• Generative Adversarial Networks (GAN)
• Variational Autoencoders (VAE)

Suggested Introductory Resources

• Coursera: Deep Learning Specialization
• Kaggle: Intro to Deep Learning

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8. Natural Language Processing (NLP)

8.1 Text Preprocessing

• Tokenization
• Stemming and Lemmatization
• Stop Words Removal

8.2 Text Representation

• Bag-of-Words (BoW)
• TF-IDF
• Word2Vec
• GloVe

8.3 Language Models

• N-grams
• Recurrent Neural Network Language Model
• Transformer Models
• BERT

Suggested Introductory Resources

• Coursera: Natural Language Processing Specialization
• Kaggle: NLP

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9. Time Series Analysis

9.1 Time Series Decomposition

• Seasonal Decomposition of Time Series (STL)

9.2 Smoothing Techniques

• Moving Average
• Exponential Smoothing

9.3 Time Series Forecasting

• ARIMA
• SARIMA
• Prophet

9.4 Advanced Time Series Models

• LSTM for Time Series
• GRU for Time Series

Suggested Introductory Resources

• Coursera: Practical Time Series Analysis
• Kaggle: Time Series

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10. Reinforcement Learning

10.1 Basics of Reinforcement Learning

• Introduction to Reinforcement Learning

10.2 Dynamic Programming

• Policy Iteration
• Value Iteration

10.3 Monte Carlo Methods

• Monte Carlo Prediction
• Monte Carlo Control

10.4 Temporal Difference Learning

• TD(0) Prediction
• SARSA (State-Action-Reward-State-Action)
• Q-Learning

10.5 Advanced Methods

• Deep Q-Networks (DQN)
• Policy Gradient Methods
• Actor-Critic Methods

10.6 Applications

• CartPole

Suggested Introductory Resources

• Coursera: Reinforcement Learning Specialization
• OpenAI: Spinning Up in Deep RL

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