AI & ML Courses syllabus : Latest Updated Syllabus for syllabus

Complete Machine Learning Syllabus

Module	Topics	Objectives	Suggested Duration
1. Foundations	- Introduction to ML - Types of ML: Supervised, Unsupervised, Reinforcement - Applications of ML	Understand the basics of ML and its applications	1 week
2. Mathematics for ML	- Linear Algebra - Probability and Statistics - Calculus (Derivatives, Partial Derivatives) - Optimization Basics	Build the mathematical foundation to understand ML algorithms	2–3 weeks
3. Data Handling	- Data Preprocessing - Feature Engineering - Data Cleaning - Exploratory Data Analysis (EDA)	Learn to prepare, clean, and analyze data	2 weeks
4. Programming Basics	- Python for ML: NumPy, Pandas, Matplotlib, Seaborn - Introduction to Jupyter Notebooks	Gain proficiency in Python and ML libraries	1–2 weeks
5. Supervised Learning	- Linear Regression - Logistic Regression - Decision Trees - Random Forests - Support Vector Machines (SVM)	Understand core supervised learning algorithms	3–4 weeks
6. Model Evaluation	- Cross-Validation - Performance Metrics (Accuracy, Precision, Recall, F1 Score, ROC, AUC)	Learn to evaluate and improve model performance	1–2 weeks
7. Unsupervised Learning	- Clustering: K-Means, Hierarchical, DBSCAN - Dimensionality Reduction: PCA, t-SNE	Master unsupervised learning techniques	2–3 weeks
8. Feature Engineering	- Handling Categorical Variables - Scaling and Normalization - Feature Selection and Extraction	Learn to improve model performance through feature manipulation	1 week
9. Ensemble Methods	- Bagging - Boosting (AdaBoost, Gradient Boosting, XGBoost) - Stacking	Understand advanced model combination techniques	2 weeks
10. Neural Networks	- Basics of Neural Networks - Activation Functions - Backpropagation - Feedforward Networks	Build a foundation in deep learning	2 weeks
11. Deep Learning	- Convolutional Neural Networks (CNNs) - Recurrent Neural Networks (RNNs) - Transfer Learning	Gain advanced skills in deep learning techniques	3 weeks
12. Reinforcement Learning	- Introduction to RL - Markov Decision Processes - Q-Learning and Deep Q-Learning	Learn the basics of decision-making algorithms	2 weeks
13. Natural Language Processing (NLP)	- Text Preprocessing - Word Embeddings (Word2Vec, GloVe) - Transformers (BERT, GPT)	Understand and implement text-based ML models	3 weeks
14. Time Series Analysis	- Forecasting Basics - ARIMA Models - LSTM for Time Series	Learn to analyze and predict time-based data	2 weeks
15. Advanced Topics	- AutoML - Explainable AI (SHAP, LIME) - Generative Models (GANs, VAEs)	Explore cutting-edge advancements in ML	2–3 weeks
16. Deployment	- Model Deployment with Flask, FastAPI - Introduction to ML Ops - Cloud Platforms: AWS, GCP, Azure	Learn to deploy ML models into production	2 weeks
17. Capstone Project	- Identify a real-world problem - Data Collection and Preprocessing - Model Development and Deployment	Apply learned skills to a comprehensive project	4 weeks

Open Source Dataset for Machine Learning Projects:

Domain	Dataset Name	Description	Link
General Purpose	Kaggle Datasets	A variety of datasets across domains such as healthcare, sports, finance, etc.	Kaggle
	UCI Machine Learning Repository	Classic machine learning datasets for regression, classification, and clustering tasks.	UCI Repository
	Google Dataset Search	Search engine for datasets across multiple domains.	Google Dataset Search
Computer Vision	MNIST	Handwritten digit dataset (0–9).	MNIST
	CIFAR-10 / CIFAR-100	Dataset of 32x32 images across 10 and 100 classes, respectively.	CIFAR
	COCO	Large-scale object detection, segmentation, and captioning dataset.	COCO
	ImageNet	A large database organized according to the WordNet hierarchy.	ImageNet
	Open Images	Dataset with labeled images for object detection and segmentation.	Open Images
	LFW (Labeled Faces in the Wild)	A dataset of face images for facial recognition and verification tasks.	LFW
Natural Language Processing (NLP)	IMDb Movie Reviews Dataset	Sentiment analysis dataset for movie reviews.	IMDb
	Common Crawl	Large dataset of web pages for NLP tasks.	Common Crawl
	SQuAD (Stanford Question Answering)	Dataset for question-answering systems.	SQuAD
	Yelp Reviews	Reviews dataset for sentiment analysis and text classification.	Yelp
	Twitter Sentiment Analysis Dataset	Dataset for analyzing sentiment in tweets.	Twitter Sentiment
	Wikipedia Dump	Text corpus of Wikipedia articles for NLP tasks.	Wikipedia Dump
Healthcare	MIMIC-III	Critical care dataset with de-identified health data.	MIMIC-III
	Breast Cancer Wisconsin Dataset	Classification dataset for diagnosing breast cancer.	Breast Cancer
	NIH Chest X-rays	Chest X-ray images with 14 disease labels.	NIH Chest X-rays
	COVID-19 Open Research Dataset (CORD-19)	Research dataset for COVID-19 literature and studies.	CORD-19
	Heart Disease Dataset	Predicting heart disease using clinical data.	Heart Disease
Finance	Lending Club Loan Dataset	Dataset for loan classification and default prediction.	Lending Club
	Bitcoin Historical Dataset	Bitcoin price and trading volume data for time-series analysis.	Bitcoin Dataset
	Stock Market Dataset	Stock prices of S&P 500 companies for predictive modeling.	Stock Market
Audio and Speech	LibriSpeech	Speech dataset for automatic speech recognition tasks.	LibriSpeech
	UrbanSound8K	Urban sound classification dataset with audio clips.	UrbanSound8K
	VoxCeleb	Large-scale speaker identification dataset.	VoxCeleb
Time-Series Data	UCI HAR Dataset	Human activity recognition using smartphone accelerometer data.	UCI HAR
	Electricity Load Forecasting Dataset	Time-series dataset for electricity consumption.	Electricity
	NOAA Weather Dataset	Weather data for temperature and precipitation analysis.	NOAA Weather
	Jena Climate Dataset	Climate data for weather forecasting.	Jena Climate
Gaming	OpenAI Gym	Simulated environments for reinforcement learning.	OpenAI Gym
	Atari Games Dataset	Atari game data for reinforcement learning.	Atari Games
Social Media	Facebook Comment Volume Dataset	Predict the volume of comments on Facebook posts.	Facebook
	Instagram Dataset	Dataset for social media analytics.	Instagram
Transportation	New York City Taxi Dataset	Trip duration prediction and time-series analysis.	NYC Taxi
	Uber Pickups Dataset	Uber trip data for predictive analysis.	Uber Dataset

Recommended Machine Learning Books You Must Read

Book Title	Author(s)	Why It’s Recommended	Level
1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow	Aurélien Géron	A practical guide to building machine learning and deep learning models using Python.	Beginner to Intermediate
2. Machine Learning Yearning	Andrew Ng	Focuses on practical machine learning strategies and how to structure ML projects effectively.	Beginner
3. Python Machine Learning	Sebastian Raschka, Vahid Mirjalili	Covers a wide range of ML techniques with practical examples in Python, including deep learning with PyTorch.	Beginner to Intermediate
4. Deep Learning	Ian Goodfellow, Yoshua Bengio, Aaron Courville	Comprehensive coverage of the mathematical and theoretical foundations of deep learning.	Advanced
5. Pattern Recognition and Machine Learning	Christopher Bishop	Offers a mathematical perspective on ML, covering Bayesian approaches and probabilistic models.	Intermediate to Advanced
6. The Hundred-Page Machine Learning Book	Andriy Burkov	Concise and beginner-friendly overview of key ML concepts and algorithms.	Beginner
7. Introduction to Statistical Learning	Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani	A beginner-friendly introduction to statistical learning methods with examples in R.	Beginner to Intermediate
8. Elements of Statistical Learning	Trevor Hastie, Robert Tibshirani, Jerome Friedman	A more in-depth and mathematical version of "Introduction to Statistical Learning."	Advanced
9. Artificial Intelligence: A Modern Approach	Stuart Russell, Peter Norvig	Comprehensive textbook covering AI concepts, including ML, reasoning, and decision-making.	Intermediate to Advanced
10. Data Science for Business	Foster Provost, Tom Fawcett	Focuses on the business applications of machine learning and data science.	Beginner
11. Probabilistic Machine Learning: An Introduction	Kevin Murphy	Provides a detailed introduction to probabilistic modeling and inference techniques in ML.	Intermediate
12. Bayesian Reasoning and Machine Learning	David Barber	Explores Bayesian approaches in machine learning with practical applications.	Intermediate
13. Reinforcement Learning: An Introduction	Richard S. Sutton, Andrew G. Barto	Definitive book on reinforcement learning concepts and algorithms.	Intermediate to Advanced
14. Deep Reinforcement Learning Hands-On	Maxim Lapan	Practical guide to implementing RL algorithms using PyTorch.	Intermediate
15. Mathematics for Machine Learning	Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong	Focuses on the essential mathematics required for understanding machine learning.	Beginner to Intermediate
16. Data Science from Scratch	Joel Grus	Covers ML and data science concepts with implementation from scratch in Python.	Beginner to Intermediate
17. Building Machine Learning Powered Applications	Emmanuel Ameisen	A hands-on guide to designing, building, and deploying production-ready ML applications.	Intermediate
18. Applied Predictive Modeling	Max Kuhn, Kjell Johnson	Focuses on predictive modeling techniques and evaluation methods.	Intermediate
19. Machine Learning: A Probabilistic Perspective	Kevin Murphy	Comprehensive coverage of probabilistic models and modern machine learning techniques.	Advanced
20. TensorFlow for Deep Learning	Bharath Ramsundar, Reza Bosagh Zadeh	Practical guide to implementing deep learning models with TensorFlow.	Intermediate

Machine Learning Platforms to Explore

Platform	Description	Link
Kaggle	Datasets, ML competitions, and tutorials to practice.	Kaggle
Hugging Face	Tutorials and pre-trained models for NLP and deep learning.	Hugging Face
Google Colab	Free cloud-based platform for running ML experiments.	Colab
Papers with Code	ML research papers paired with open-source code implementations.	Papers with Code
TensorFlow	Official documentation and tutorials for TensorFlow framework.	TensorFlow
PyTorch	Tutorials for deep learning using PyTorch.	PyTorch

Machine Learning Capstone Projects You Can Try

Domain	Project Idea
Supervised Learning	Predict house prices using regression techniques.
	Build a spam email classifier.
	Predict loan defaults with credit scoring data.
	Forecast sales for a retail store.
	Classify handwritten digits (MNIST dataset).
	Predict diabetes risk using medical data.
	Build a stock price prediction model.
	Customer churn prediction for a telecom company.
	Predict exam scores using student performance data.
	Identify fake news articles.
Unsupervised Learning	Customer segmentation using clustering.
	Movie recommendation system using collaborative filtering.
	Anomaly detection in network traffic.
	Market basket analysis using association rule mining.
	Create a music genre classifier.
	Analyze clickstream data to cluster web visitors.
	Document topic modeling using Latent Dirichlet Allocation (LDA).
	Cluster articles based on sentiment analysis.
	Image compression using K-Means.
	Build a fraud detection system using clustering techniques.
Deep Learning	Develop a facial recognition system.
	Build a chatbot using recurrent neural networks.
	Create an object detection model (YOLO or Faster R-CNN).
	Implement an image caption generator.
	Build a speech-to-text conversion model.
	Create a handwriting generation model.
	Develop an autonomous driving car simulation.
	Create a human pose estimation system.
	Design a neural style transfer system.
	Generate deepfake videos using GANs.
Natural Language Processing	Sentiment analysis for Twitter data.
	Build a named entity recognition (NER) system.
	Create a question-answering system (BERT or GPT models).
	Automatic text summarization of news articles.
	Translate languages using machine translation models.
	Build a chatbot with conversational AI.
	Perform sarcasm detection in online reviews.
	Create a plagiarism detection tool using NLP techniques.
	Develop an email subject line predictor for marketing.
	Implement grammar and spell-check systems using NLP.
Computer Vision	Develop an emotion recognition system using facial expressions.
	Create a plant disease detection system using leaf images.
	Build a traffic sign classification model.
	Implement an autonomous driving lane detection system.
	Create a face mask detection model for public spaces.
	Develop an AI-powered virtual try-on system for clothing.
	Create a wildlife species recognition system using camera trap images.
	Build an AI-based video surveillance system for security.
	Develop a photo restoration system for old or damaged images.
	Build an augmented reality application for real-time object labeling.
Reinforcement Learning	Develop a reinforcement learning agent to play chess.
	Create a self-driving car simulation using RL.
	Build a trading bot for stock market simulations.
	Develop an RL agent to optimize delivery routes.
	Create an energy management system for smart grids.
	Develop a personalized tutoring system using RL.
	Build a game-playing AI for classic arcade games (e.g., Pac-Man).
	Create a multi-agent RL system for cooperative robotics.
	Optimize warehouse inventory using RL-based strategies.
	Implement RL to solve maze navigation tasks.
Advanced Applications	Develop a multi-class image segmentation system for medical imaging.
	Create a predictive maintenance model for industrial machinery.
	Build a personalized shopping assistant using recommendation engines.
	Develop a text-to-image generation model using DALL-E-like techniques.
	Build a neural architecture search system for optimizing ML models.
	Create a smart assistant for scheduling and time management.
	Develop a blockchain-based fraud detection system using AI.
	Build an AI-driven resume screening tool for recruitment.
	Develop a weather forecasting system using time-series analysis.
	Create a personalized fitness coach using IoT and AI integration.
Health and Medicine	Create a disease diagnosis system using medical images (e.g., X-rays).
	Predict heart disease risk using patient health data.
	Develop a drug discovery pipeline using deep learning.
	Build an AI system for personalized nutrition recommendations.
	Create an AI-powered health chatbot for preliminary medical queries.
	Develop an assistive AI tool for detecting early signs of Alzheimer's disease.
	Implement an AI-based cancer detection model using biopsy images.
	Build a remote health monitoring system using wearable devices.
	Create a stress detection system using facial and physiological signals.
	Develop a genetic sequence analyzer for identifying genetic disorders.
IoT and Edge AI	Create a smart home automation system using AI and IoT devices.
	Build a predictive maintenance system for IoT-connected machinery.
	Develop an AI-driven traffic management system using edge computing.
	Create a smart parking system with real-time availability detection.
	Implement an AI-powered energy consumption optimizer for smart homes.
	Build an AI-based voice assistant for IoT device control.
	Create a smart irrigation system using AI and IoT sensors.
	Develop an AI-powered air quality monitoring system.
	Build a fleet management system using AI and IoT integration.
	Create a real-time intrusion detection system for smart homes.

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