Introduction to AI and Machine Learning with Python Training Course

Day 1

Session 1: Introduction to AI and Machine Learning

• What is AI? Overview of AI applications
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Introduction to Python and Jupyter Notebook

Session 2: Understanding Machine Learning Pipelines

• The ML workflow: Data collection, preprocessing, model training, evaluation
• Overview of Python libraries for ML (NumPy, Pandas, Matplotlib, Scikit-learn)
• Hands-on exercise: Setting up a Python ML environment

Session 3: Working with Data for Machine Learning

• Loading and exploring datasets using Pandas
• Handling missing values and outliers
• Hands-on exercise: Data preprocessing and feature engineering

Session 4: Data Visualization and Feature Selection

• Understanding data distributions using Matplotlib and Seaborn
• Feature selection techniques for ML models
• Hands-on exercise: Visualizing and selecting important features

Day 2

Session 1: Supervised Learning – Regression Models

• Introduction to regression analysis
• Implementing Linear Regression in Scikit-learn
• Evaluating regression models (MSE, R-squared)
• Hands-on exercise: Predicting house prices using regression

Session 2: Supervised Learning – Classification Models

• Introduction to classification problems
• Implementing Logistic Regression and Decision Trees
• Evaluating classification models (Confusion Matrix, Accuracy, Precision, Recall)
• Hands-on exercise: Classifying customer purchase behavior

Session 3: Advanced Classification Techniques

• Understanding Support Vector Machines (SVM)
• Introduction to Random Forest and Ensemble Methods
• Hands-on exercise: Comparing multiple classification models

Session 4: Model Optimization and Hyperparameter Tuning

• Overfitting and underfitting in ML models
• Hyperparameter tuning using GridSearchCV
• Hands-on exercise: Optimizing ML models

Day 3

Session 1: Unsupervised Learning – Clustering Techniques

• Introduction to clustering and its applications
• Implementing K-Means Clustering in Scikit-learn
• Hands-on exercise: Segmenting customer data using clustering

Session 2: Principal Component Analysis (PCA) and Dimensionality Reduction

• Understanding dimensionality reduction
• Implementing PCA for high-dimensional data
• Hands-on exercise: Reducing features while preserving information

Session 3: Working with Real-World Datasets

• Applying ML models to business problems
• Hands-on exercise: Customer churn prediction case study

Session 4: Introduction to Neural Networks and Deep Learning

• Basics of Neural Networks
• Introduction to TensorFlow and Keras
• Hands-on exercise: Creating a simple neural network

Day 4

Session 1: Natural Language Processing (NLP) with Python

• Overview of NLP and text processing
• Tokenization, stemming, and sentiment analysis
• Hands-on exercise: Sentiment analysis using NLP

Session 2: Time Series Analysis and Forecasting

• Understanding time series data
• Implementing time series forecasting models
• Hands-on exercise: Stock price prediction

Session 3: Model Deployment and Automation

• Exporting ML models for deployment
• Automating ML workflows
• Hands-on exercise: Deploying a machine learning model

Session 4: Ethics and Bias in AI

• Understanding AI bias and fairness
• Ethical considerations in ML applications
• Discussion and case studies

Day 5

Session 1: Capstone Project – Defining a Business Problem

• Choosing a real-world dataset
• Defining a business problem to solve using ML
• Hands-on exercise: Data preparation for final project

Session 2: Implementing a Full ML Model

• Applying regression, classification, or clustering based on the problem
• Hands-on exercise: Training and evaluating the model

Session 3: Presenting Insights and Final Model Evaluation

• Visualizing results and creating a final report
• Hands-on exercise: Interpreting and presenting ML model findings

Session 4: Review and Q&A

• Recap of key concepts and best practices
• Open Q&A and discussion on advanced learning pathsCourse wrap-up and next steps