Machine Learning Tutorial

Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches systems to think and understand like humans by learning from the data.

Module 1: ML Fundamentals

This section introduces the core concepts of machine learning and explains how ML relates to AI and Deep Learning.

• Introduction
• AI vs ML vs Deep Learning
• ML workflow

Module 2: Python for ML

Python is widely used in machine learning because of its simple syntax and powerful libraries.

• Introduction
• Variables
• Data Types
• Operators
• Conditional Statements
• Loops
• Functions
• Lists
• Dictionary
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn

Module 3: Mathematics for ML

A basic understanding of mathematics helps explain how machine learning algorithms learn from data and optimize their performance.

Probability

• Basic probability
• Conditional Probability
• Bayes' Theorem
• Probability distributions

Statistics

• Descriptive Statistics
• Inferential Statistics
• Skewness and Kurtosis
• Correlation: Pearson, Spearman

Linear Algebra & Calculus

• Vectors
• Matrices
• Dot Product
• linear equations
• Differentiation
• Gradient Descent

Module 4: Data Preparation

Real-world data is often incomplete or inconsistent and needs to be prepared before modeling. This section covers data cleaning, splitting, scaling and feature preparation techniques.

• Introduction
• Data Cleaning
• Missing Values
• Outliers
• Splitting Data 
• Feature Selection
• Feature Scaling
• Feature Engineering
• Feature Extraction
• Imbalanced Data

Module 5: Exploratory Data Analysis

EDA helps understand data by finding patterns, relationships and unusual observations. This section covers analysis and visualization techniques for exploring datasets.

• Introduction
• Univariate, Bivariate and Multivariate
• Data Visualization
• Correlation Analysis
• Advance EDA

Module 6: Supervised Learning

Supervised Learning uses labeled data to make predictions on new data. This section covers common algorithms for classification and regression tasks.

• Classification
• Regression

1. Linear Regression

• Introduction to Linear Regression
• Multiple Linear Regression

2. Logistic Regression

• Understanding Logistic Regression
• Cost function in Logistic Regression

3. Decision Trees

• Decision Tree in Machine Learning
• Types of Decision tree algorithms
• Decision Tree Regression (Implementation)
• Decision Tree Classification (Implementation)

4. k-Nearest Neighbors (k-NN)

• Introduction to KNN
• Decision Boundaries in K-Nearest Neighbors (KNN)

5. Naïve Bayes

• Introduction to Naive Bayes
• Gaussian Naive Bayes
• Multinomial Naive Bayes
• Bernoulli Naive Bayes

6. Support Vector Machines (SVM)

• Understanding SVMs
• Non-Linear SVM

7. Ensemble Learning

• EnsembleI learning
• Bagging & Boosting
• AdaBoost
• Gradient Boosting
• XGBoost

8. Random Forest (Bagging Algorithm)

• Introduction
• Random Forest Classifier
• Random Forest Regression

Module 7: Model Evaluation & Optimization

Evaluating a model helps determine how well it performs and whether it can generalize to new data. This section covers techniques for assessing and improving model performance.

• Confusion Matrix
• Precision, Recall & F1-Score
• AUC-ROC Curve
• Regression Metrics
• Cross-validation
• Overfitting & Underfitting
• Bias-Variance Tradeoff
• Regularization
• Hyperparameter Tuning
• Model Selection

Module 8: Unsupervised learning

Unsupervised Learning helps uncover useful patterns and relationships in unlabeled data. This section introduces the main techniques used to analyze and organize such data.

• Clustering 
• Association Rule Mining
• Dimensionality Reduction

1. Clustering

Centroid-based Methods:

• K-Means clustering
• Elbow Method
• K-Means++ clustering

Connectivity based methods:

• Hierarchical clustering
• Agglomerative Clustering

Density Based methods:

• DBSCAN
• OPTICS

Distribution-based Methods:

• Gaussian mixture models
• Expectation-Maximization Algorithm

2. Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-distributed Stochastic Neighbor Embedding (t-SNE)
• Non-negative Matrix Factorization (NMF)
• Independent Component Analysis (ICA)
• Isomap

3. Anomaly Detection & Association Rule Mining

• Apriori algorithm
• FP-Growth (Frequent Pattern-Growth)
• Anomaly Detection

Module 9: Reinforcement Learning

Reinforcement Learning is a type of machine learning in which an agent interacts with an environment and learns to make decisions by receiving rewards or penalties for its actions.

1. Fundamental Methods

• Markov decision processes (MDPs)
• Bellman equation
• Value iteration algorithm

2. Model-Free Methods

• Q-Learning
• SARSA

3. Policy-Based Methods

• Reinforce Algorithm
• Actor-Critic Algorithm
• Asynchronous Advantage Actor-Critic (A3C)

Module 10: Semi-Supervised & Self-Supervised Learning

It uses a mix of labeled and unlabeled data making it helpful when labeling data is costly or it is very limited.

• Semi-Supervised Learning
• Semi Supervised Classification
• Self-Training
• Self-Supervised Learning

Module 11: Time Series & Forecasting

Forecasting models analyze past data to predict future trends, commonly used for time series problems like sales, demand or stock prices.

• Introduction
• Seasonality
• Stationarity
• Exponential Smoothing
• ARIMA
• SARIMA

Module 12: Deployment & MLOps

The trained ML model must be integrated into an application or service to make its predictions accessible.

• Machine learning deployment
• ML Applications: Streamlit, Gradio
• ML APIs: Flask, FastAPI
• MLOps
• CI/CD in MLOps
• End-to-End MLOps

Module 13: Projects & Practice

Projects help reinforce machine learning concepts by applying them to practical problems. This section provides projects and interview resources for hands-on practice.

• Machine Learning Projects
• Interview Questions and Answers
• Coding Interview Questions

After machine learning and have hands on experience in it we can start with deep learning from here: Deep Learning Tutorial