• Fundamentals of Computing and Python Basics : Introduction to programming concepts, setting up a development environment (e.g., Jupyter Notebooks).
• Essential Mathematics for AI/ML : Basic linear algebra, calculus, probability, and statistics.
• Data Representation and Visualization : Using Pandas for data manipulation and Matplotlib for data visualization.
• Core AI Concepts and History : Defining AI, early developments, key breakthroughs in AI and ML.
• Introduction to Machine Learning : Understanding AI vs. ML, supervised vs. unsupervised learning, basic Python examples.
• Data Collection and Cleaning : Techniques for gathering and cleaning data for analysis.

• Exploratory Data Analysis (EDA) : Summarizing data, identifying patterns, and performing basic statistical tests.
• Basic Regression and Classification : Understanding and implementing linear regression and nearest neighbor classification.
• Evaluation Metrics and Model Validation : Applying train/test split, accuracy, RMSE, and cross-validation.
• Intro to Ethical AI : Addressing bias in data and responsible AI usage.
• Case Studies : Real-world applications of AI in healthcare, finance, and education.
• Recap of Machine Learning Workflow : Data preprocessing, feature engineering, model training, and evaluation.
• Regression Techniques : Multiple linear regression, polynomial regression, and regularization.
• Classification Algorithms : Logistic regression, decision trees, and ensemble methods.
• Advanced Unsupervised Learning : Clustering, dimensionality reduction, and anomaly detection.
• Neural Network Fundamentals : Perceptrons, activation functions, feed-forward networks, and backpropagation.
• Model Evaluation and Metrics : Precision, recall, F1-score, confusion matrix, and AUC-ROC.
• Techniques for Overcoming Overfitting : Regularization, dropout, data augmentation, and early stopping.
• Hyperparameter Tuning : Grid search, random search, and Bayesian optimization.
• Practical ML Pipeline Development : Building end-to-end ML pipelines and MLOps basics.

• Deep Learning Architectures : Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and sequence models.
• Advanced Neural Network Topics : Batch normalization, residual networks, and transfer learning techniques.
• Natural Language Processing (NLP) : Text preprocessing, word embeddings, and sequence-to-sequence models.
• Transformers and Language Models : BERT, GPT, attention mechanisms, and fine-tuning for tasks like text classification and summarization.
• Computer Vision Beyond Basics : Object detection, image segmentation, and pretrained models.
• Reinforcement Learning Foundations : Markov Decision Processes (MDPs), Q-learning, and policy gradients.
• Generative Models : GANs, autoencoders, and their applications in AI.
• Large-Scale Data Processing and Deployment : Distributed computing, cloud-based AI platforms, and Docker containerization.
• AutoML and Advanced Hyperparameter Tuning : Automated feature engineering and advanced search strategies.
• Frontiers of AI and Research Directions : Ethical AI, fairness in large models, AI governance, and current AI research trends.
• Capstone Project : Integration of multiple advanced AI techniques to develop a robust AI solution.

• Introduction to Artificial Intelligence
• Industrial applications of AI
• Bias and fairness in AI Systems

• Descriptive Statistics and Exploratory Data Analytics
• Statistical Inferences
• Linear Regression
• Logistic Regression Analysis
• Clustering and Introduction to Unsupervised Learning

• Sentiment Analysis
• Topic Modelling

Predict Attrition & Optimize Hiring

• Designing Interactive Dashboards on PowerBI
• HR metrics and Dashboards
• Workforce Predictive Modelling
• Turnover Predictive Modelling
• Managing Employee Performance using Analytics

Customer Segmentation & ROI

• Introduction to marketing analytics
• Segmentation and Positioning
• Product analytics
• Marketing mix modelling
• Consumer analytics

Supply Chain Forecasting

• Fundamentals of Operation Analytics
• Retail and e-commerce operations
• Product Process Matrix
• Theory of Constraints
• Quality Management (Lean & Six-Sigma)
• Forecasting