AI/ML Course IN AI AND ML ( S-AI-103 )

Month 1: Python Fundamentals and Introduction to Machine Learning

- Python Basics 

  - Functions and If-Else Statements  

  - Control Flow Statements  

  - Lists & List Comprehension  

  - Sets, Dictionaries & Dictionary Comprehension  

  - Working with Classes  

  - Inheritance and Polymorphism  

- Introduction to Machine Learning  

  - Overview of Machine Learning Concepts and Applications  

  - Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning  

  - Machine Learning Workflow and Model Evaluation  

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Month 2: Data Preprocessing and Exploratory Data Analysis (EDA)

- Data Preprocessing  

  - Data Cleaning: Handling Missing Values, Outliers, and Inconsistent Data  

  - Feature Selection and Feature Engineering Techniques  

  - Data Scaling, Normalization, and Transformation  

- Exploratory Data Analysis (EDA) 

  - Overview of Data Preprocessing and EDA Concepts  

  - Importance of Data Preprocessing and EDA in the Data Analysis Pipeline  

  - Key Steps Involved in Data Preprocessing and EDA  

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Month 3: Supervised Learning – Regression

- Introduction to Supervised Learning  

  - Overview of Supervised Learning and its Applications  

  - Difference Between Features, Labels, and Target Variables  

  - Training and Testing Data Split for Model Evaluation  

- Linear Regression 

  - Simple Linear Regression with One Input Feature  

  - Multiple Linear Regression with Multiple Input Features  

  - Model Evaluation Metrics: Mean Squared Error, R-Squared  

- Logistic Regression  

  - Binary Logistic Regression for Classification Problems  

  - Multinomial Logistic Regression for Multi-Class Classification  

  - Model Interpretation and Decision Boundaries  

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Month 4: Supervised Learning – Classification Models

- Decision Trees and Random Forests  

  - Basics of Decision Trees and Tree-Based Modeling  

  - Ensemble Learning with Random Forests  

  - Feature Importance and Tree Visualization  

- K-Nearest Neighbors (K-NN)

  - Distance Metrics for k-NN Classification and Regression  

  - Choosing the Optimal Value of k  

  - Pros and Cons of k-NN Algorithm  

- Support Vector Machines (SVM)  

  - Maximum Margin Classification with Linear SVM  

  - Non-Linear Classification with Kernel SVM  

  - SVM Hyperparameter Tuning and Kernel Selection  

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Month 5: Advanced Machine Learning and Regularization

- Naive Bayes Classifiers

  - Introduction to Bayes' Theorem and Conditional Probability  

  - Naive Bayes Assumptions and Classifier Formulation  

  - Application to Text Classification and Spam Filtering  

- Overfitting and Regularization

  - Bias-Variance Tradeoff and Model Complexity  

  - Regularization Techniques: L1 and L2 Regularization  

  - Validation Curves and Learning Curves for Model Assessment  

- Evaluation Metrics and Techniques

  - Confusion Matrix, Accuracy, Precision, Recall, F1 Score  

  - Receiver Operating Characteristic (ROC) Curves  

  - Cross-Validation Techniques: k-Fold, Stratified, and Leave-One-Out  

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Month 6: Artificial Intelligence and Deep Learning

- Introduction to AI and Neural Networks 

  - Overview of AI and Its Applications  

  - Introduction to Artificial Neural Networks  

  - Perceptron and Activation Functions  

  - Backpropagation Algorithm  

  - Building a Basic Feedforward Neural Network  

- Recurrent Neural Networks (RNN) and Natural Language Processing (NLP)

  - Basic RNN, LSTM, and GRU Architectures  

  - Sequence Modeling and Time Series Analysis  

  - Text Preprocessing and Word Embeddings  

  - Named Entity Recognition (NER) and Sequence-to-Sequence Models  

- Convolutional Neural Networks (CNN) and Computer Vision

  - Convolutional Layers, Pooling Layers, and Fully Connected Layers  

  - Image Classification with CNN  

  - Transfer Learning and Pre-Trained Models  

  - Image Preprocessing and Feature Extraction with OpenCV  

- Generative Adversarial Networks (GANs)

  - GAN Architecture and Training Process  

  - Deep Convolutional GANs (DCGAN)  

  - Conditional GAN and Semi-Supervised Learning  

  - StyleGAN and Image Synthesis  

- Project and Practical Applications

  - Capstone Project: Applying AI Techniques to Real-World Problems

• Mathematics Foundation:
  Understand key concepts in linear algebra, calculus, probability, and statistics. These are the building blocks for understanding machine learning algorithms and neural networks.

• Programming Skills:
  Learn Python, which is the most popular language for AI/ML. Familiarize yourself with libraries like NumPy, pandas, and matplotlib for data handling and visualization.

• Machine Learning Basics:
  Study the fundamental concepts of supervised and unsupervised learning, common algorithms (e.g., linear regression, decision trees, k-means), and evaluation metrics.

• Practical Application:
  Practice using frameworks like TensorFlow or PyTorch for building models. Use platforms like Kaggle, Google Colab, or local datasets to implement projects and solve real-world problems.