MACHINE LEARNING WITH R

All Zoom lectures are monitored by trained staff.

Mr. Clem Wang has been a Data Scientist for 15 years, working at both large companies like Yahoo and Microsoft, and a bunch of startups. He’s used both Python and R professionally. In a previous life, he’s been involved with QA’ing compilers and interpreters, so he has some insights in the inner workings of R and Python.

Students will be awarded different certifications in each batch.

Adults

Module 1 – Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modeling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning

Module 2 – Supervised Learning I
K-Nearest Neighbors
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees

Module 3 – Supervised Learning II
Regression Algorithms
Model Evaluation
Model Evaluation: Overfitting & Underfitting
Understanding Different Evaluation Models

Module 4 – Unsupervised Learning
K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters – Single Linkage Clustering
Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
Density-Based Clustering

Module 5 – Dimensionality Reduction & Collaborative Filtering
Dimensionality Reduction: Feature Extraction & Selection
Collaborative Filtering & Its Challenge

500$