Python for Data, ML & AI

Module 1: Python Programming Essentials

• Variables, data types, control structures, functions
• Lists, dictionaries, sets, tuples operations
• File handling, exception management

Module 2: NumPy for Numerical Computing

• Array creation, indexing, broadcasting
• Mathematical operations, linear algebra
• Random number generation, statistical functions

Module 3: Pandas for Data Analysis

• Series, DataFrames creation and manipulation
• Data cleaning, missing values, duplicates
• Grouping, merging, pivot tables

Module 4: Data Visualization with Matplotlib & Seaborn

• Line plots, histograms, scatter plots
• Statistical visualizations, heatmaps
• Subplots, customization, saving figures

Module 5: Statistics for Data Science

• Descriptive statistics, probability distributions
• Hypothesis testing, p-values, confidence intervals
• Correlation, ANOVA, chi-square tests

Module 6: Machine Learning Fundamentals

• Supervised vs unsupervised learning overview
• Train-test split, cross-validation
• Overfitting, underfitting, bias-variance tradeoff

Module 7: Regression Algorithms

• Linear regression, multiple regression
• Polynomial regression, regularization (Ridge, Lasso)
• Model evaluation (R², RMSE, MAE)

Module 8: Classification Algorithms

• Logistic regression, decision trees, random forests
• SVM, KNN, Naive Bayes classifiers
• Precision, recall, F1-score, ROC curves

Module 9: Unsupervised Learning

• K-means clustering, hierarchical clustering
• PCA, t-SNE dimensionality reduction
• Anomaly detection techniques

Module 10: Ensemble Methods & Model Optimization

• Bagging, boosting (XGBoost, LightGBM)
• Grid search, random search hyperparameter tuning
• Feature selection and engineering

Module 11: Deep Learning Foundations

• Neural networks, activation functions, backpropagation
• TensorFlow/Keras, PyTorch basics
• CNNs for image classification

Module 12: Natural Language Processing (NLP)

• Text preprocessing, tokenization, stemming
• Bag of words, TF-IDF, word embeddings
• Sentiment analysis, topic modeling

Module 13: Time Series Analysis

• Stationarity, decomposition, autocorrelation
• ARIMA, SARIMA models
• Prophet forecasting library

Module 14: Model Deployment & MLOps

• Flask/FastAPI for model serving
• Docker containerization
• Model monitoring, versioning

Module 15: AI Ethics & Advanced Topics

• Bias detection, fairness metrics
• Explainable AI (SHAP, LIME)
• Reinforcement learning introduction

Experience in the Industry Gain knowledge from experts with practical Python for Data, ML & AI project experience in a variety of sectors.

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