AI and ML Training

 

🤖 Artificial Intelligence (AI) and Machine Learning (ML) Syllabus

📅 Duration: 16–24 Weeks (Approx. 4–6 Months)

🎯 Goal: Build a strong understanding of AI and ML concepts, tools, and real-world applications using Python.

📘 Module 1: Introduction to AI and ML (1 Week)
• What is Artificial Intelligence?
• Difference: AI vs ML vs DL vs Data Science
• History and Evolution of AI
• Applications of AI (Healthcare, Finance, Robotics, NLP)
• Types of AI: Narrow, General, Super
• Types of ML: Supervised, Unsupervised, Reinforcement Learning
• Tools & Languages: Python, Jupyter, Colab, Anaconda

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🐍 Module 2: Python for AI & ML (2 Weeks)
• Python Basics: Syntax, variables, loops, functions
• Data Structures: Lists, Tuples, Dictionaries, Sets
• File handling
• NumPy for numerical computing
• Pandas for data manipulation
• Matplotlib and Seaborn for visualization
• Basic OOP in Python

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📊 Module 3: Mathematics for AI/ML (2 Weeks)
• Linear Algebra: Vectors, Matrices, Matrix operations, Eigenvalues & Eigenvectors
• Calculus: Derivatives, Partial Derivatives, Gradient Descent
• Probability & Statistics:
  • Bayes Theorem
  • Probability distributions (Normal, Binomial, Poisson)
  • Mean, Median, Variance, Standard Deviation
  • Hypothesis testing and p-values

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📁 Module 4: Data Preprocessing & EDA (1–2 Weeks)
• Data cleaning: handling missing values, duplicates
• Feature selection & engineering
• Outlier detection
• Scaling: Normalization, Standardization
• Encoding: One-hot, Label Encoding
• Train-test split, cross-validation
• Visualization techniques (pair plot, heatmaps, histograms)

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🤖 Module 5: Machine Learning Algorithms (4 Weeks)
✅ Supervised Learning:
• Linear Regression (simple, multiple)
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• Naive Bayes
• Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC, Confusion Matrix
✅ Unsupervised Learning:
• K-Means Clustering
• Hierarchical Clustering
• PCA (Principal Component Analysis)
• Anomaly Detection

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🧠 Module 6: Deep Learning with Neural Networks (3–4 Weeks)
• What is Deep Learning?
• Artificial Neural Networks (ANN)
• Architecture: Input, Hidden, Output layers
• Activation functions: ReLU, Sigmoid, Softmax, Tanh
• Forward & Backpropagation
• Loss functions
• Gradient Descent optimization
• TensorFlow & Keras Introduction
• Model creation, compilation, training, and evaluation
• Saving and loading models

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🖼️ Module 7: Advanced Deep Learning (2–3 Weeks)
• Convolutional Neural Networks (CNNs): Convolutions, Pooling, Filters
• Image classification with CNN
• Transfer learning
• Recurrent Neural Networks (RNNs), LSTM, GRU
• Sequence modeling, time-series data
• Autoencoders
• Hyperparameter Tuning: Grid Search, Random Search
• Early stopping, Dropout

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📚 Module 8: Natural Language Processing (NLP) (2 Weeks)
• Text preprocessing: Tokenization, Stemming, Lemmatization
• Bag-of-Words, TF-IDF
• Sentiment Analysis
• Word Embeddings: Word2Vec, GloVe
• Sequence models for text (RNN, LSTM)
• Text generation using LSTM or transformers (intro only)

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🧠 Module 9: Reinforcement Learning (1–2 Weeks)
• Introduction to RL
• Terminology: Agent, Environment, Actions, Rewards
• Q-learning
• Exploration vs Exploitation
• Markov Decision Process (MDP)
• Intro to Deep Q-Networks (DQN)

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⚙️ Module 10: AI Tools & Frameworks
• Python Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn
• Deep Learning: TensorFlow, Keras, PyTorch (intro)
• Data Handling: OpenCV (vision), NLTK/SpaCy (NLP)
• Model Deployment Tools:
  • Flask for serving models
  • Streamlit for UI
  • Google Colab / Jupyter for development

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🚀 Module 11: Model Deployment & MLOps (1–2 Weeks)
• Saving and loading ML models (pickle, joblib)
• Creating REST APIs using Flask or FastAPI
• Streamlit for interactive dashboards
• Introduction to Docker & Git for version control
• Hosting models: Heroku, Render, Hugging Face

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🎓 Module 12: Final Project (2–4 Weeks)
• Build and deploy an end-to-end AI/ML project using real-world datasets
• Example Projects:
  • Movie Recommendation System
  • Real Estate Price Predictor
  • Fake News Detector (NLP)
  • COVID-19 Detection from X-ray Images (CNN)
  • Stock Price Prediction (Time-series)
  • Chatbot (NLP + DL)
Include:
• Data collection & cleaning
• Model building & evaluation
• Deployment with user interface
• GitHub documentation

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