Machine Learning Course (Industry-Ready 2026 Curriculum)

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Python for Machine Learning

Python basics, NumPy, Pandas, Matplotlib & Seaborn, Jupyter, virtual environments, Git & version control.

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Learning objectives Refresh Python basics Use NumPy and Pandas for data Visualize with Matplotlib & Seaborn Work in Jupyter and virtual environments Apply Git & version control

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Mathematics for ML

Linear algebra (vectors, matrices, eigenvalues), calculus (derivatives, gradient), probability (distributions, Bayes), statistics (mean, variance, hypothesis testing, CLT).

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Learning objectives Apply linear algebra for ML Use calculus (derivatives, chain rule, gradient) Apply probability and Bayes theorem Use statistics: mean, variance, covariance, hypothesis testing, CLT

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Data Preprocessing & EDA

Data cleaning, missing values, outliers, feature scaling, encoding, feature engineering, EDA, visualization best practices.

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Learning objectives Clean data and handle missing values Detect outliers and scale features Encode categorical variables Perform feature engineering Conduct EDA and apply visualization best practices

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Introduction to Machine Learning

What is ML, types (supervised, unsupervised, semi-supervised, reinforcement), ML pipeline, bias-variance tradeoff.

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Learning objectives Define ML and its types Understand supervised, unsupervised, semi-supervised, reinforcement learning Build ML pipeline Understand bias-variance tradeoff

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Supervised Learning – Regression

Linear, multiple linear, polynomial regression; Ridge, Lasso, ElasticNet; MAE, MSE, RMSE, R².

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Learning objectives Implement linear, multiple linear, polynomial regression Apply Ridge, Lasso, ElasticNet Use MAE, MSE, RMSE, R²

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Supervised Learning – Classification

Logistic regression, KNN, Naive Bayes, SVM, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost; confusion matrix, precision, recall, F1, ROC-AUC.

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Learning objectives Apply logistic regression, KNN, Naive Bayes, SVM Use decision trees, Random Forest, gradient boosting, XGBoost, LightGBM, CatBoost Evaluate with confusion matrix, precision, recall, F1, ROC-AUC

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Model Evaluation & Tuning

Train/test split, K-fold cross validation, hyperparameter tuning (grid search, random search), overfitting/underfitting, feature importance.

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Learning objectives Use train/test split and K-fold cross validation Tune hyperparameters with grid and random search Address overfitting and underfitting Interpret feature importance

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Unsupervised Learning

K-Means, hierarchical clustering, DBSCAN, PCA, t-SNE, association rules (Apriori).

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Learning objectives Apply K-Means, hierarchical clustering, DBSCAN Use PCA and t-SNE for dimensionality reduction Apply association rule learning (Apriori)

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Ensemble Methods

Bagging, boosting, stacking, voting classifiers, advanced gradient boosting.

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Learning objectives Apply bagging, boosting, stacking Use voting classifiers Apply advanced gradient boosting

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Feature Engineering & Selection

Feature transformation, interaction features; filter, wrapper, embedded methods; dimensionality reduction.

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Learning objectives Transform features and create interaction features Apply filter, wrapper, embedded selection methods Use dimensionality reduction

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Time Series Analysis

Time series components, ARIMA, SARIMA, Prophet, forecasting models, evaluation metrics.

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Learning objectives Understand time series components Apply ARIMA, SARIMA, Prophet Build forecasting models and evaluate them

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Anomaly Detection

Statistical methods, Isolation Forest, One-Class SVM, real-world use cases.

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Learning objectives Apply statistical methods for anomaly detection Use Isolation Forest and One-Class SVM Solve real-world use cases

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Neural Network Fundamentals

Perceptron, activation functions, backpropagation, loss functions, gradient descent variants.

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Learning objectives Understand perceptron and activation functions Apply backpropagation and loss functions Use gradient descent variants

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Deep Learning Frameworks

TensorFlow, Keras, PyTorch, model training workflow.

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Learning objectives Use TensorFlow, Keras, PyTorch Follow model training workflow

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Computer Vision

CNN architecture, image classification, object detection, transfer learning, image augmentation.

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Learning objectives Design CNN architecture Perform image classification and object detection Apply transfer learning and image augmentation

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Natural Language Processing (NLP)

Text preprocessing, tokenization, word embeddings, RNN & LSTM, Transformers, BERT, sentiment analysis, text classification.

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Learning objectives Preprocess text and tokenize Use word embeddings, RNN, LSTM Apply Transformers and BERT Perform sentiment analysis and text classification

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Generative AI & Large Language Models

Transformers architecture, GPT models, prompt engineering, fine-tuning, RAG, LLM deployment.

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Learning objectives Understand Transformers and GPT models Apply prompt engineering and fine-tuning Use RAG and deploy LLMs

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Reinforcement Learning

Markov Decision Process, Q-Learning, policy gradient, Deep Q Networks.

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Learning objectives Model problems with MDP Apply Q-Learning and policy gradient Use Deep Q Networks

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Model Deployment

Flask/FastAPI, REST APIs, Docker, model serialization, CI/CD basics.

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Learning objectives Deploy with Flask/FastAPI and REST APIs Use Docker and model serialization Apply CI/CD basics

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MLOps

ML pipelines, model monitoring, drift detection, experiment tracking, MLflow, DVC.

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Learning objectives Build ML pipelines and monitor models Detect drift and track experiments Use MLflow and DVC

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Cloud for ML

AWS for ML, GCP AI Platform, Azure ML, model hosting, AutoML.

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Learning objectives Use AWS, GCP, Azure for ML Host models and apply AutoML

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ML System Design

End-to-end ML pipeline design, data architecture, feature store, scalable inference.

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Learning objectives Design end-to-end ML pipeline Plan data architecture and feature store Design scalable inference

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Responsible AI

Bias & fairness, model explainability (SHAP, LIME), ethical AI, data privacy.

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Learning objectives Address bias and fairness Apply explainability (SHAP, LIME) Consider ethical AI and data privacy

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Real-World Projects

Customer churn, fraud detection, recommendation system, stock prediction, NLP chatbot, computer vision app.

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Learning objectives Build customer churn prediction Implement fraud detection and recommendation system Build stock prediction, NLP chatbot, computer vision app