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Instructor
Kasra Manshael, Instructor - Feature Engineering in Machine Learning

Kasra Manshael

Has more than 10 years of experience working with and teaching Machine Learning, pattern recognition, and data mining. Kasra earned graduate degrees in Electrical, Biomedical Engineering, & Theoretical Computer Science. He has experience working as a Lead Data Scientist in different industries, from automation to life science at Areto Consulting GMBH.

Instructor: Kasra Manshael

This course will give the students a comprehensive overview on Feature Engineering strategies, a practical hands-on style of learning for theoretical concepts, a rich and comprehensive introduction to proper references including literature, keywords and notable related scientists to follow, and explore pros & cons and hidden tips on algorithms in practice.

  • Get a comprehensive overview on Feature Engineering strategies.
  • Hands-on style of learning for theoretical concepts.
  • Instructor has 10 years experience working with and teaching Machine Learning, Pattern Recognition, and Data Mining.

Duration: 5h 03m

Course Description

This course will explain all the following: What is Feature Engineering? Features in Machine Learning Transformation and Extraction of Features Model Selection: A Quick Review Linear Sparse Models Information Theory Meets Machine Learning Discretization of Numerical Features Dimensionality Reduction

What am I going to get from this course?

  • A comprehensive overview on Feature Engineering strategies including ones you may even have not heard before.
  • A practical hands-on style of learning for theoretical concepts.
  • A rich and comprehensive introduction to proper references including literature, keywords and notable related scientists to follow. 
  • Exploring cons & pros and hidden tips on algorithms in practice.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Background in Math
  •     Linear Algebra
  •     Probability Theory & Statistics
  •     Discrete Mathematics
  •     Calculus
Background in Machine Learning
  •     Supervised & Unsupervised Learning  
Familiarity with Python Programming
  •     Sci-kit Learn, Numpy, Matplotlib, etc.
     

Who should take this course? Who should not?

People interested in a career in Machine Learning can take this course, but should have all the prerequisites. 
 

Curriculum

Module 1: What is Feature Engineering

Lecture 1 Introduction
Lecture 2 You Do Feature Engineering Every Day!
Lecture 3 Features Are Not Always There!
Lecture 4 Underfitting, Fitting, and Overfitting
Lecture 5 Learning in High-Dimensional Setting 1
Lecture 6 Learning In High-Dimensional Setting 2
Lecture 7 Leanring in High-Dimensional Setting 3

Module 2: Data Preparation

Lecture 8 Missing Values
Lecture 9 Handling Missing Values - Imputation
Lecture 10 Handling Missing Values - Imputation 2
Lecture 11 Handling Missing Values - Imputation 3
Lecture 12 Feature Transformation - Diversity of Features
Lecture 13 Discretization of Numerical Features
Lecture 14 An Information Theoretic Method
Lecture 15 An Information theoretic Method 2
Lecture 16 Feature Scaling

Module 3: Feature Selection

Lecture 17 Why Feature Selection
Lecture 18 Causation
Lecture 19 Numerical-Numerical Association
Lecture 20 Spearman Rank Correlation
Lecture 21 Maximal Information Coefficient
Lecture 22 Categorical-Categorical Association
Lecture 23 Categorical Dependence
Lecture 24 Numerical-Categorical Association
Lecture 25 F-Value for Classification
Lecture 26 Sparse Linear Models
Lecture 27 Step-Wise Feature Selection
Lecture 28 A Note on Model Selection
Lecture 29 Ridge Regression

Module 4: Dimension Reduction

Lecture 30 Principal Component Analysis
Lecture 31 Nonnegative Matrix Factorization 1
Lecture 32 Nonnegative Matrix Factorization
Lecture 33 Locally Linear Embedding
Lecture 34 LLE

Module 5: Feature Selection

Lecture 35 Working With Text Data
Lecture 36 Terminology
Lecture 37 Text Preprocessing
Lecture 38 Text to Numbers 1
Lecture 39 Text to Numbers 2
Lecture 40 Text to Numbers 3
Lecture 41 Working With Time Series
Lecture 42 Time Series 1
Lecture 43 Time Series 2
Lecture 44 Time Series 3
Lecture 45 Time Series 4
Lecture 46 Time Series 5
Lecture 47 Time Series 6
Lecture 48 Time Series 7
Lecture 49 Time Series 8
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