COURSE OUTLINE

Day 1: Data Science Foundations

Focus: Understanding the data science workflow

• Introduction to Data Science and Machine Learning
• Data science lifecycle (problem definition to deployment) 
• Data collection and data types 
• Data preprocessing techniques (cleaning, handling missing values) 
• Exploratory Data Analysis (EDA) 
• Data visualization for insights 
• Hands-on: Performing EDA on a real-world dataset

Day 2: Statistics for Machine Learning

Focus: Building statistical foundations for ML

• Introduction to probability theory 
• Random variables and distributions 
• Descriptive vs inferential statistics 
• Hypothesis testing concepts 
• Confidence intervals and significance testing 
• Correlation and covariance analysis 
• Hands-on: Applying statistical methods using Python

Day 3: Supervised Learning

Focus: Learning from labeled data

• Introduction to supervised learning 
• Regression techniques (Linear, Multiple Regression) 
• Classification techniques (Logistic Regression, Decision Trees, KNN) 
• Train-test split and cross-validation 
• Model evaluation metrics (accuracy, precision, recall, F1-score) 
• Bias-variance tradeoff 
• Hands-on: Building supervised learning models using Scikit-learn

Day 4: Unsupervised Learning

Focus: Discovering hidden patterns in data

• Introduction to unsupervised learning 
• Clustering techniques (K-Means, Hierarchical clustering) 
• Dimensionality reduction (PCA) 
• Pattern recognition in datasets 
• Anomaly detection basics 
• Use cases of unsupervised learning in business 
• Hands-on: Clustering and PCA implementation

Day 5: Advanced Machine Learning

Focus: Improving model performance and deployment readiness

• Introduction to ensemble learning (Bagging, Boosting, Random Forest) 
• Gradient Boosting Machines (XGBoost overview) 
• Feature engineering and feature selection 
• Hyperparameter tuning techniques 
• Model optimization strategies 
• Introduction to model deployment concepts 
• Hands-on: Improving model accuracy using ensemble methods

Day 6: Capstone Project

Focus: End-to-end machine learning implementation

• Defining a real-world machine learning problem 
• Data preparation and feature engineering 
• Model selection and training 
• Model evaluation and optimization 
• Interpretation of results and insights 
• Basic deployment workflow overview 
• Final project presentation and assessment 
• Feedback and learning review session