philtsmith570/Machine_Learning_A-Z

SDS Machine Learning A-Z

Machine Learning A-Z

Master Machine Learning Algorithms

About this course

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts.

Instructors:

• Kirill Eremenko - CEO at SDS
• Hadelin de Ponteves - AI Entrepreneur

Introduction

Course Description

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:

Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, 
          Decision Tree Regression, Random Forest Regression
Part 3 - Classification: Logistic Regression, K-NN, 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: Bag-of-words model and algorithms for NLP
Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Programming Assignments

• Course 1: Neural Networks and Deep Learning

  • Part 1 - Data Preprocessing
    • Preprocessing Imputer Function - Data Preprocessing
  • Part 2 - Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Linear Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
  • Part 3 - Classification
    • Logistic Regression
    • K-Nearest Neighbor (K-NN)
    • Support Vector Machine (SVM)
    • Kernel SVM
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
  • Part 4 - Clustering
    • K-Means Clustering
    • Hierarchical Clustering
  • Part 5 - Association Rule Learning
    • Apriori
  • Part 6 - Reinforcement Learning
    • Upper Confidence Bound (UCB)
    • Thompson Sampling
  • Part 7 - Natural Language Processing
    • Natural Language Processing
  • 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 and Boosting (XGBoost)
    • Model Selection - Grid Search
    • Model Selection - K-fold Cross Validation
    • XGBoost

Course Milestones

• Course completed: 10/28/18!
• Course Certificate