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AI ML & Deep Learning Course Content

About AI ML & Deep Learning

AI, Machine Learning, and Deep Learning focus on building intelligent systems that learn from data, recognize patterns, automate decisions, and power applications like vision, speech recognition, recommendation systems, and predictive analytics across industries.
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Module 1: Introduction to AI, ML & DL

  • What is Artificial Intelligence
  • History & evolution of AI
  • AI vs Machine Learning vs Deep Learning
  • Applications of AI across industries
  • AI career paths & roles
  • Ethical AI & limitations

Module 2: Python for AI & ML

  • Python recap for AI
  • NumPy
  • Pandas
  • Matplotlib & Seaborn
  • Jupyter Notebook
  • Virtual environments & package management

Module 3: Mathematics for Machine Learning

  • Linear algebra
  • Vectors & matrices
  • Dot product
  • Probability basics
  • Statistics
  • Mean, median, variance
  • Calculus (conceptual)
  • Gradients
  • Optimization intuition

Module 4: Data Handling & Preprocessing

  • Types of data
  • Data collection techniques
  • Data cleaning
  • Handling missing values
  • Feature scaling
  • Encoding categorical data
  • Train-test split

Module 5: Exploratory Data Analysis (EDA)

  • Data visualization techniques
  • Univariate & multivariate analysis
  • Correlation analysis
  • Outlier detection
  • Insights generation

Module 6: Machine Learning Basics

  • Types of ML
    • Supervised
      • Unsupervised
      • Reinforcement
    • ML workflow
    • Model training & testing
    • Bias vs variance
    • Overfitting & underfitting

Module 7: Supervised Learning Algorithms

      • Linear regression
      • Multiple regression
      • Logistic regression
      • K-Nearest Neighbors (KNN)
      • Decision Trees
      • Random Forest
      • Support Vector Machines (SVM)

Module 8: Unsupervised Learning Algorithms

    • K-Means clustering
    • Hierarchical clustering
    • DBSCAN
    • Principal Component Analysis (PCA)
    • Association rules (Apriori basics)

    Module 9: Model Evaluation & Optimization

    • Accuracy, precision, recall, F1-score
    • Confusion matrix
    • ROC & AUC
    • Cross-validation
    • Hyperparameter tuning
    • GridSearch & RandomSearch

    Module 10: Feature Engineering

    • Feature selection techniques
    • Feature extraction
    • Dimensionality reduction
    • Handling imbalanced data
    • Feature importance

    Module 11: Introduction to Deep Learning

    • Why deep learning
    • Neural network basics
    • Perceptron
    • Activation functions
    • Loss functions
    • Backpropagation
    • Gradient descent

    Module 12: Deep Learning with TensorFlow & Keras

    • TensorFlow architecture
    • Keras API
    • Building neural networks
    • Model compilation
    • Model training & evaluation
    • Saving & loading models

    Module 13: Convolutional Neural Networks (CNN)

    • Image processing basics
    • Convolution & pooling
    • CNN architectures
    • Image classification
    • Transfer learning
    • Popular CNN models (conceptual)

    Module 14: Recurrent Neural Networks (RNN)

    • Sequential data
    • RNN architecture
    • LSTM
    • GRU
    • Time-series forecasting
    • Sequence modeling

    Module 15: Natural Language Processing (NLP)

    • Text preprocessing
    • Tokenization
    • Bag of Words
    • TF-IDF
    • Word embeddings
    • Text classification
    • Sentiment analysis

    Module 16: Reinforcement Learning (Intro)

    • Agent, environment & rewards
    • Markov Decision Process
    • Q-learning basics
    • Applications of RL

    Module 17: Model Deployment & MLOps Basics

    • Model serialization
    • Flask / FastAPI for ML
    • REST APIs for ML models
    • Cloud deployment overview
    • CI/CD basics for ML
    • Monitoring ML models

    Module 18: Responsible AI & Ethics

    • Bias & fairness
    • Explainable AI (XAI)
    • Data privacy
    • AI regulations
    • Model transparency

    Module 19: Hands-On Projects

    • House price prediction
    • Customer churn prediction
    • Spam email detection
    • Image classification using CNN
    • Sentiment analysis (NLP)
    • Time-series forecasting
    • Final capstone AI project

    Module 20: Career Preparation

      • AI/ML job roles
        • Data Scientist
        • ML Engineer
        • AI Engineer
      • Resume & portfolio building
      • Interview preparation
      • Certification roadmap
        • Google ML
        • AWS ML
        • TensorFlow Developer
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      AI, Machine Learning Master

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      AI, Machine Learning, and Deep Learning enable systems to learn from data, recognize patterns, and make intelligent, automated decisions efficiently.

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