Machine Learning Tutorial

40 min read Machine Learning in AI

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Machine Learning Tutorial

Machine Learning (ML) sits within Artificial Intelligence as the discipline that lets systems improve from experience—drawing on data to form predictions and choices without hand-coding every rule.

  • It surfaces patterns, streamlines repetitive work, and sharpens forecasts by learning from historical records.
  • For instance, an online retailer can mine customer and sales histories to map purchasing habits, while a medical provider can study patient records to spot health patterns and raise care quality.

Module 1: ML Fundamentals

This module lays out the foundational ideas of machine learning and clarifies how ML connects to AI and Deep Learning.

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

Module 2: Python for ML

Python dominates machine learning work thanks to readable syntax and a rich ecosystem of 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 working grasp of math makes it clearer how algorithms absorb data and refine 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

Raw data is frequently messy or incomplete, so it must be shaped before modeling. Here you cover cleaning, train/test splits, scaling, and feature preparation methods.

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

Module 5: Exploratory Data Analysis

EDA builds intuition about a dataset by revealing patterns, associations, and anomalies. This module surveys analytical and visualization approaches for exploring data.

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

Module 6: Supervised Learning

Supervised Learning trains on labeled examples so models can predict outcomes for unseen cases. This module surveys widely used classification and regression algorithms.

  • 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

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

8. Random Forest (Bagging Algorithm)

  • Introduction
  • Random Forest Classifier
  • Random Forest Regression

Module 7: Model Evaluation & Optimization

Assessment shows how accurate a model is and whether it generalizes beyond the training set. This module covers methods for measuring quality and lifting 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 finds structure and relationships when labels are absent. This module introduces the main approaches for exploring and organizing unlabeled 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 trains an agent that acts inside an environment and improves by collecting rewards or penalties for what it does.

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

These approaches combine labeled and unlabeled examples, which is valuable when annotations are expensive or scarce.

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

Module 11: Time Series & Forecasting

Forecasting methods read historical sequences to anticipate what comes next—common for sales, demand, or stock-price series.

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

Module 12: Deployment & MLOps

A finished model must be wired into an app or service so its predictions can be consumed in production.

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

Module 13: Projects & Practice

Hands-on projects cement ML ideas by applying them to real problems. This module points you to projects and interview material for practice.

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

After building machine learning skills and getting practical experience, you can move on to deep learning with the Deep Learning Tutorial.