Complete Guide to Machine Learning: AI, Python & R + ChatGPT Reward [2024]

This course is both enjoyable and dynamic, offering a comprehensive dive into Machine Learning. Here’s how it’s structured:

Part 1 – Data Preprocessing

– Covering techniques to prepare data for analysis.

Part 2 – Regression

– Simple Linear Regression

– Multiple Linear Regression

– Polynomial Regression

– Support Vector Regression (SVR)

– Decision Tree Regression

– Random Forest Regression

Part 3 – Classification

– Logistic Regression

– K-Nearest Neighbors (K-NN)

– Support Vector Machines (SVM)

– Kernel SVM

– Naive Bayes

– Decision Tree Classification

– Random Forest Classification

Part 4 – Clustering

– K-Means

– Hierarchical Clustering

Part 5 – Association Rule Learning

– Apriori

– Eclat

Part 6 – Reinforcement Learning

– Upper Confidence Bound

– Thompson Sampling

Part 7 – Natural Language Processing

– Introduction to the Bag-of-words model and NLP algorithms

Part 8 – Deep Learning

– Artificial Neural Networks

– Convolutional Neural Networks

Part 9 – Dimensionality Reduction

– Principal Component Analysis (PCA)

– Linear Discriminant Analysis (LDA)

– Kernel PCA

Part 10 – Model Selection & Boosting

– k-fold Cross Validation

– Parameter Tuning

– Grid Search

– XGBoost

Each section within a part is standalone, allowing you to focus on specific areas according to your career needs. The course emphasizes practical exercises based on real-life case studies, ensuring hands-on experience in building models.

Furthermore, it provides Python and R code templates for download, enabling you to use them in your own projects.