Here is a detailed AI & ML syllabus covering the fundamentals to advanced concepts, designed to provide a comprehensive understanding of Artificial Intelligence and Machine Learning:

  1. Introduction to Artificial Intelligence
  • What is AI? Definitions and Scope
  • History and Evolution of AI
  • Applications of AI in Various Domains
  • Types of AI: Narrow AI, General AI, Super AI
  • Ethics and Challenges in AI
  1. Mathematics for AI and ML
  • Linear Algebra
    • Vectors, Matrices, and Operations
    • Eigenvalues and Eigenvectors
  • Probability and Statistics
    • Random Variables, Distributions, Bayes’ Theorem
    • Descriptive and Inferential Statistics
  • Calculus
    • Derivatives and Gradients
    • Optimization Techniques
  • Graph Theory (optional for advanced learners)
  1. Programming Foundations
  • Introduction to Python for AI/ML
    • Data Types, Functions, and Libraries
    • Libraries for AI/ML: NumPy, Pandas, Matplotlib, Seaborn
  • Basics of R Programming (optional)
  • Code Versioning with Git/GitHub

 

 

  1. Machine Learning Foundations
  • What is Machine Learning?
  • Types of ML:
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • ML Workflow: Problem Framing, Data Collection, Preprocessing, Model Building
  1. Data Preprocessing and Feature Engineering
  • Handling Missing Data
  • Data Cleaning and Transformation
  • Feature Scaling and Normalization
  • Encoding Categorical Variables
  • Feature Selection and Dimensionality Reduction (e.g., PCA)
  1. Supervised Learning
  • Regression Models:
    • Linear Regression
    • Polynomial Regression
    • Ridge and Lasso Regression
  • Classification Models:
    • Logistic Regression
    • Decision Trees and Random Forest
    • Support Vector Machines (SVM)
    • k-Nearest Neighbors (k-NN)
  1. Unsupervised Learning
  • Clustering Algorithms:
    • k-Means Clustering
    • Hierarchical Clustering
    • DBSCAN
  • Dimensionality Reduction:
    • Principal Component Analysis (PCA)
    • t-SNE
  1. Neural Networks and Deep Learning
  • Introduction to Neural Networks
    • Perceptron Model
    • Activation Functions
  • Feedforward Neural Networks
  • Backpropagation Algorithm
  • Introduction to Deep Learning
    • Convolutional Neural Networks (CNNs)
    • Recurrent Neural Networks (RNNs)
    • Long Short-Term Memory (LSTM)
  • Frameworks and Libraries: TensorFlow, Keras, PyTorch
  1. Natural Language Processing (NLP)
  • Text Preprocessing: Tokenization, Lemmatization, and Stop Words Removal
  • Bag-of-Words and TF-IDF
  • Sentiment Analysis
  • Word Embeddings: Word2Vec, GloVe
  • Sequence Models for NLP: RNNs, LSTMs, Transformers
  • Chatbot Development and Text Generation
  1. Computer Vision
  • Basics of Image Processing
  • Image Classification with CNNs
  • Object Detection Algorithms (YOLO, SSD)
  • Image Segmentation Techniques
  • Transfer Learning and Pretrained Models (ResNet, VGG, etc.)
  1. Reinforcement Learning
  • Basics of Reinforcement Learning
  • Markov Decision Process (MDP)
  • Q-Learning and Deep Q-Networks (DQN)
  • Applications of RL: Gaming, Robotics
  1. AI for Real-World Applications
  • AI in Healthcare
  • AI in Finance
  • AI in Autonomous Vehicles
  • AI in Smart Cities
  1. Advanced Topics
  • Generative Adversarial Networks (GANs)
  • Explainable AI (XAI)
  • Hyperparameter Tuning and Model Optimization
  • AI in Edge and IoT Devices
  • Ethical AI and Bias Mitigation
  1. Deployment and Model Serving
  • Model Serialization (e.g., Pickle, ONNX)
  • Model Deployment Frameworks (Flask, FastAPI)
  • Containerization with Docker
  • Deployment on Cloud Platforms (AWS, Azure, GCP)
  1. Capstone Projects
  • Building an AI-Powered Recommendation System
  • Developing a Real-Time Object Detection App
  • Implementing a Sentiment Analysis Tool
  • Creating a Predictive Analytics Dashboard
  1. Certification and Career Support
  • Preparing for AI & ML Certifications (e.g., TensorFlow, AWS Machine Learning)
  • Resume Building and Interview Preparation
  • AI/ML Job Roles: Data Scientist, AI Engineer, ML Developer

For practical training and hands-on experience in AI and ML, consider Disha Computer Institution Hubli, known for its government-approved courses, skilled faculty, and placement support to build a lucrative career in 2025 and beyond.