AI

Machine Learning Course (Industry-Ready 2026 Curriculum)

Industry-ready Machine Learning curriculum covering Foundations (Python, Math, Data Preprocessing), Core ML (Regression, Classification, Evaluation, Unsupervised), Advanced ML (Ensemble, Feature Engineering, Time Series, Anomaly Detection), Deep Learning (Neural Networks, Computer Vision, NLP, Generative AI), Reinforcement Learning, MLOps & Deployment, and …

3 Months Duration
24 Modules
Paid Access

Course fee ₹5000.00

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Learning path

Course Modules

Work through each module and pass quizzes to unlock the next step in your journey.

Module 1 Locked

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

Module 2 Locked

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

Module 3 Locked

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

Module 4 Locked

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

Module 5 Locked

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

Module 6 Locked

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

Module 7 Locked

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

Module 8 Locked

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)

Module 9 Locked

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

Module 10 Locked

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

Module 11 Locked

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

Module 12 Locked

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

Module 13 Locked

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

Module 14 Locked

Deep Learning Frameworks

TensorFlow, Keras, PyTorch, model training workflow.

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Learning objectives

Use TensorFlow, Keras, PyTorch
Follow model training workflow

Module 15 Locked

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

Module 16 Locked

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

Module 17 Locked

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

Module 18 Locked

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

Module 19 Locked

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

Module 20 Locked

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

Module 21 Locked

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

Module 22 Locked

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

Module 23 Locked

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

Module 24 Locked

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

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