Overview
Data Science & Machine Learning - Boost your Machine Learning, statistics skills with real heart attack analysis project
Syllabus
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- Introduction to Machine Learning and Heart Disease
-- Overview of machine learning in healthcare
-- Understanding the dataset
- Python for Data Science
-- Python setup and essential libraries (Pandas, NumPy, Matplotlib, Seaborn)
-- Loading and exploring data
- Data Preprocessing
-- Handling missing values
-- Feature scaling and normalization
-- Categorical data encoding
- Exploratory Data Analysis (EDA)
-- Statistical summary and visualization
-- Feature correlation and importance
- Introduction to Statistics for Machine Learning
-- Basic statistical concepts
-- Probability and distributions
-- Hypothesis testing
- Supervised Learning: Classification
-- Overview of classification algorithms
-- Logistic regression
-- Decision trees and random forests
- Model Evaluation and Validation
-- Train-test split and cross-validation
-- Evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC
- Advanced Machine Learning Techniques
-- Hyperparameter tuning
-- Ensemble methods (Bagging, Boosting)
- Implementation with R and Python
-- Comparative analysis using R and Python
-- Code examples and best practices
- Project: Heart Attack Prediction Analysis
-- Project overview and objectives
-- Step-by-step implementation
-- Model deployment strategies
- Conclusion and Future Directions
-- Summary of key concepts
-- Exploring advanced topics: deep learning, model interpretability
-- Resources for continued learning
- Capstone Presentation
-- Preparing a project report
-- Presentation skills and peer feedback
Taught by
Oak Academy and OAK Academy Team
Tags
On-Demand
