What is Machine Learning syllabus : Latest Updated Syllabus for syllabus
Updated on Nov 6, 2024 05:58 IST
Jaya SharmaAssistant Manager - Content
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. | |
| Instagram Dataset | Dataset for social media analytics. | ||
| 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. |
Table of content
- Popular Exams
- Most Popular Courses
- Popular What is Machine Learning UG Courses
- Popular What is Machine Learning PG Courses
Popular Exams
Following are the top exams for What is Machine Learning. Students interested in pursuing a career on What is Machine Learning, generally take these important exams.You can also download the exam guide to get more insights.
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Most Popular Courses
Following are the most popular What is Machine Learning courses, based on alumni reviews. Explore these reviews to choose the best course in What is Machine Learning.
Popular Courses
- B.Tech. in Artificial IntelligenceSRM Institute of Science and Technology, Kattankulathur
- B.Tech. in Computer Science and Engineering (Artificial Intelligence and Machine Learning)SRM Institute of Science and Technology, Kattankulathur
- B.E. in Computer Science and Engineering (IOT and AI)Chandigarh University (CU)
- B.Tech. in Artificial IntelligenceIIT Hyderabad - Indian Institute of Technology
- B.E. in Artificial Intelligence and Machine LearningRamaiah Institute of Technology
- B.E. in Computer Science and Engineering (Artificial Intelligence & Machine Learning)Ramaiah Institute of Technology
- B.Tech. in Artificial Intelligence and Data ScienceIIT Jodhpur - Indian Institute of Technology
- B.Tech. in Computer Science and Engineering (Artificial Intelligence and Machine Learning)Kalasalingam Academy of Research and Education
- B.E. in Artificial Intelligence and Machine LearningR.V. College of Engineering
- B.Tech. in Computer Science and Engineering (Artificial Intelligence and Machine Learning)Manipal University, Jaipur
Popular What is Machine Learning UG Courses
Following are the most popular What is Machine Learning UG Courses . You can explore the top Colleges offering these UG Courses by clicking the links below.
UG Courses
- B.E. / B.Tech
1407 Colleges
- A.M.E.
1 College
Popular What is Machine Learning PG Courses
Following are the most popular What is Machine Learning PG Courses . You can explore the top Colleges offering these PG Courses by clicking the links below.
PG Courses
- M.E./M.Tech
13 Colleges
- MBA/PGDM
3 Colleges
News & Updates
Student Forum
Anything you would want to ask experts?Answered 4 weeks ago
op Colleges and Branches
According to COMEDK 202425 cut off, you have the following possible best options:
- Siddaganga Institute of Technology (SIT), Tumkur
CSE ends with 8,336, AI+ML with 10,352
A good fit with CSE, a good fit with AIML/AIDS.
- CMR Institute of Technology (CMRIT), Bangalore
CSE- ~7,676, AI
A
Antu Kaibartya
Contributor-Level 10
Answered a month ago
B.Tech fees through WBJEE:
Government colleges (like JU, IIEST, KGEC): INR 4,000–60,000/year (plus INR 20k–50k for hostel/mess)
Semi-govt/autonomous (like RCCIIT): INR 2–3 lakh/year
Private colleges (like IEM, NSHM): INR 1–3 lakh/year
R
Rupesh Katariya
Contributor-Level 10
Answered a month ago
Yes, a few Online BCA colleges offer a BCA course that specializes in Artificial Intelligence and Machine Learning. Among them, the Manav Rachna Centre for Distance and Online Education is a good option for students offering a BCA in Artificial Intelligence and Machine Learning for a total fee of IN
N
Nikita Chauhan
Contributor-Level 8
Answered a month ago
Yes, with a BCA and 60% marks, you meet the eligibility criteria for the MCA (AI & ML) programme at Galgotias University. The programme requires a minimum of 50% marks in graduation, and preference is given to students with a mathematics background at the 10+2 or graduation level. Since you have a B
V
Venkatesh Gorla
Contributor-Level 8
Answered a month ago
Absolutely! B.Tech CSE (Computer Science Engineering) students have great scope today and in the future. With technology growing fast, there are many job opportunities in software development, data science, AI, cybersecurity, and more. Plus, you can work in IT companies, startups, or even start your
R
Rupesh Katariya
Contributor-Level 10
Answered 2 months ago
With a VITEEE rank of 1,30,000, you'll likely be eligible for admission to VIT Bhopal or VIT AP. However, securing a seat in AI/ML, which is a competitive branch, might be challenging, especially in the main campuses like Vellore and Chennai, as these branches typically have lower closing ranks. In
R
Ramanjan Kumar
Beginner-Level 5
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My COMEDK score is 8512. Kindly suggest good colleges for computer science/IT/AIML and other B.Tech courses.