*Python Introduction
*Flow Control
*Sequences, Dictionaries, and Sets
*OOPs – Class and Object
*OOPs - Inheritance
*OOPs – Polymorphism
*OOPs – Abstraction
*Python with String
*Exception Handling
*Python with Thread
*Python with Database

*Introduction to Statistics and Its Role in AI/ML
*Types of Data (Numerical, Categorical, etc.)
*Measures of Central Tendency (Mean, Median, Mode)
*Measures of Dispersion (Variance, Standard Deviation)
*Data Visualization Techniques (Histogram, Box Plot)
*Introduction to Probability Theory

*Probability Distributions (Normal, Binomial, Poisson)
*Sampling Techniques (Random, Stratified)
*Central Limit Theorem & Its Significance
*Confidence Intervals & Margin of Error
*Hypothesis Testing (t-test, ANOVA, Chi-Square)
*P-Values, Significance Levels, Type I & II Errors

*Regression Analysis (Linear, Logistic)
*Correlation vs Causation
*Bayesian Statistics & Bayesian Inference
*Dimensionality Reduction (PCA, t-SNE)
*Feature Engineering & Selection Techniques
*Time Series Analysis (ARIMA, Exponential Smoothing)

*Statistical Methods in AI (Markov Chains, Hidden Markov Models)
*Experimental Design & A/B Testing
*Model Evaluation Metrics (ROC, Precision-Recall, R-Squared)

*Introduction to Data Mining & Its Role in AI/ML
*Data Types and Data Sources
*Data Preprocessing (Cleaning, Normalization)
*Exploratory Data Analysis (EDA)
*Feature Engineering and Selection
*Introduction to Database & SQL for Data Mining

*Supervised vs Unsupervised Learning
*Clustering Techniques (K-Means, DBSCAN, Hierarchical)
*Association Rule Mining (Apriori, FP-Growth)
*Classification Techniques (Decision Trees, Naïve Bayes)
*Anomaly Detection & Outlier Analysis
*Text Mining & Natural Language Processing (NLP)

*Regression Analysis & Prediction Models
*Deep Learning for Data Mining (Neural Networks)
*Graph Mining & Social Network Analysis
*Web Scraping & Web Mining
*Big Data Mining (Hadoop, Spark)
*Time Series Data Mining

*Advanced Dynamic Programming
*Randomized Algorithms & Monte Carlo Methods
*Evolutionary Algorithms (Genetic Algorithms, Simulated Annealing)
*Neural Network Algorithms & Optimization
*Reinforcement Learning Algorithms (Q-Learning, Deep Q Networks)
*Probabilistic Algorithms (Bayesian Networks, Markov Chains)

*Algorithms in AI & Data Science
*Algorithmic Ethics & Bias in AI
*Model Optimization & Hyperparameter Tuning