Advanced Topics
Why?
This course brings together all the data science concepts you've learned and applies them to real machine learning problems. You'll work with actual datasets using Python and pandas, build predictive models, and learn the complete workflow that data scientists use in industry. It's the practical application of everything from statistics to programming, giving you hands-on experience with the tools and techniques that employers actually want.
What?
A practical machine learning course using Python, pandas, and scikit-learn. You'll learn data preprocessing (handling missing values, outliers, encoding), feature selection techniques, and use various classification algorithms including decision trees, random forest, KNN, SVM, and logistic regression. The course covers the complete machine learning workflow from data loading to model evaluation.
Curriculum:
Pandas Essentials
Installing and importing pandas, reading different file formats (CSV, Excel, JSON), data exploration methods, handling missing data, column management, data selection and filtering, basic data analysis and manipulation techniques.
Outliers & Normalization
Understanding outliers and their impact on models, detection methods using IQR and Z-Score, visualization techniques for outlier detection, normalization and standardization methods (Min-Max, Z-Score), when to apply different scaling techniques.
Encoding Techniques
Converting categorical data to numerical format, manual mapping techniques, label encoding for ordinal data, one-hot encoding for nominal data, handling the results and avoiding common pitfalls with encoding.
Feature Selection
Why feature selection matters, data preparation steps, model-based feature importance using logistic regression, random forest, and decision trees, correlation analysis, statistical methods like SelectKBest, removing low variance features, PCA for dimensionality reduction.
Classification Algorithms
Implementation of major classification algorithms: Decision Trees, Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM), and Logistic Regression. Understanding when to use each algorithm and their strengths/weaknesses.
Machine Learning Workflow
Complete end-to-end machine learning process: data loading and preparation, train-test split, model training and prediction, model evaluation using accuracy, precision, recall, and confusion matrix. Best practices for real-world projects.
Association Rules Mining
Introduction to market basket analysis, Apriori algorithm implementation, understanding support, confidence, and lift metrics, finding association rules in transactional data, practical applications in recommendation systems.
Notes
This is a hands-on course with lots of Python coding. You'll work with real datasets and implement everything from scratch using pandas and scikit-learn. Make sure you're comfortable with Python basics before taking this course.
