Artificial Intelligence & Machine Learning

Course Outline

Module 1: Foundations of Machine Learning with Python

  • ntroduction to Machine Learning and AI

    • Overview of AI and ML Concepts
    • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
    • Key Python Libraries: NumPy, Pandas, and Matplotlib
  • Data Preprocessing and Exploration

    • Importing and Cleaning Data
    • Exploratory Data Analysis (EDA) with Pandas and Matplotlib
    • Feature Engineering and Selection
  • Supervised Learning Algorithms

    • Linear Regression and Logistic Regression
    • Decision Trees and Random Forests
    • Evaluation Metrics: Accuracy, Precision, Recall, and F1 Score

Module 2: Advanced Machine Learning Techniques

    • Unsupervised Learning Algorithms

      • Clustering Techniques: K-Means, Hierarchical Clustering
      • Dimensionality Reduction: PCA and t-SNE
      • Anomaly Detection
    • Introduction to Neural Networks and Deep Learning

      • Basics of Neural Networks: Neurons, Activation Functions, and Layers
      • Building Neural Networks with TensorFlow/Keras
      • Deep Learning Architectures: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
    • Natural Language Processing (NLP) with Python

      • Text Preprocessing: Tokenization, Stemming, Lemmatization
      • Sentiment Analysis and Text Classification
      • Word Embeddings: Word2Vec and GloVe

Module 3: Applied AI and Machine Learning Projects

    • Reinforcement Learning

      • Introduction to Reinforcement Learning Concepts
      • Q-Learning and Deep Q-Networks (DQN)
      • Applications in Gaming and Robotics
    • AI in Computer Vision

      • Image Processing Techniques with OpenCV
      • Object Detection and Image Classification with CNNs
      • Transfer Learning and Pre-trained Models
    • Deployment and Production

      • Model Deployment: Saving and Loading Models with Pickle and Joblib
      • Creating APIs for ML Models using Flask
      • Monitoring and Updating Deployed Models

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