Description

Complete Machine Learning & Artificial Intelligence Syllabus from DWANI

1. Introduction to AI and ML

         Overview of AI, ML, and Data Science

         Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)

          Key applications of AI and ML (Healthcare, Finance, Autonomous Systems, etc.)

1. Mathematical Foundations

           Linear Algebra: Matrices, Vectors, Eigenvalues, and Eigenvectors

           Probability and Statistics: Basic Probability, Bayes Theorem, Distributions

           Calculus: Derivatives, Gradients, Chain Rule

1. Programming for AI/ML

           Python Programming Basics

           Libraries: NumPy, Pandas, Matplotlib, and Scikit-learn

1. Data Preprocessing

           Data Collection and Cleaning

           Feature Engineering and Selection

           Train-Test Split, Cross-Validation

1. Supervised Learning Basics

           Linear Regression

           Logistic Regression

           Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)

1. Unsupervised Learning Basics

          K-means Clustering

          Principal Component Analysis (PCA)

          Association Rule Learning

1. Tools

          Jupyter Notebooks

          Introduction to TensorFlow or PyTorch