Syllabus - Data Science, AI & Machine Learning

Data Science, Artificial Intelligence (AI), and Machine Learning (ML) are at the forefront of technological innovation, driving decision-making across industries. This comprehensive syllabus covers essential topics that will help you gain expertise in data analysis, predictive modeling, and developing AI-powered applications.

Session 1 – Introduction to Data Science

  • Overview of Data Science
  • Data Science Lifecycle
  • Introduction to Python for Data Science
  • Understanding Data Types and Structures
  • Data Preprocessing and Cleaning
  • Exploratory Data Analysis (EDA)

Session 2 – Statistical Foundations for Data Science

  • Basic Statistical Concepts
  • Probability Theory and Distributions
  • Hypothesis Testing and Confidence Intervals
  • Descriptive vs. Inferential Statistics
  • Correlation and Regression Analysis
  • Statistical Significance and p-values

Session 3 – Machine Learning Fundamentals

  • Introduction to Machine Learning
  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement
  • Linear Regression and Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • Model Evaluation and Validation

Session 4 – Advanced Machine Learning

  • Ensemble Methods: Bagging, Boosting, and Stacking
  • Clustering Techniques: K-Means, Hierarchical, DBSCAN
  • Dimensionality Reduction: PCA, LDA
  • Natural Language Processing (NLP)
  • Time Series Analysis and Forecasting
  • Neural Networks and Deep Learning Basics

Session 5 – Artificial Intelligence and Deep Learning

  • Introduction to Artificial Intelligence
  • Deep Learning Concepts and Architectures
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Generative Adversarial Networks (GANs)
  • AI in Practice: Case Studies and Applications

Session 6 – Capstone Project & Industry Applications

  • Project Planning and Design
  • Data Collection and Preparation
  • Model Development and Testing
  • Deploying Machine Learning Models
  • AI/ML in Industry: Healthcare, Finance, Retail, etc.
  • Presenting Findings and Business Insights