Machine Learning 101 with Sklearn and Stats Models

Course Overview

Machine Learning is one of the fundamental skills you need to become a data scientist. It is the stepping stone that will help you understand deep learning an

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Last updated Fri, 04-Jun-2021
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Course overview

Course Overview

Machine Learning is one of the fundamental skills you need to become a data scientist. It is the stepping stone that will help you understand deep learning and modern data analysis techniques. Take advantage of not one but two of the best machine learning libraries – StatsModels and sklearn. Learn linear regression, logistic regression and cluster analysis.

 

Target Audience

  • Aspring data scientists
  • Anyone who wants to get acquainted with the fundamental machine learning models
  • Anyone who is just getting started with ML and wants to gradually build up valuable skills.

 

Learning Objectives

  • You will gain confidence when working with 2 of the leading ML packages - statsmodels and sklearn.
  • You will learn how to perform a linear regression.
  • You will become familiar with the ins and outs of a logistic regression.
  • You will excel at carrying out cluster analysis (both flat and hierarchical).
  • You will learn how to apply your skills to real-life business cases.
  • You will be able to comprehend the underlying ideas behind ML models.

 

Business Outcomes

Get people started with machine learning.

 

What will i learn?

Requirements
Curriculum for this course
77 Lessons 5 hrs
Machine Learning 101 with Sklearn and Stats Models
1 Lessons 05:00:00 Hours
  • Machine Learning 101 with Sklearn and Stats Models
    Preview 05:00:00
Introduction
1 Lessons
  • What does the course cover
    Preview .
Setting up the environment
6 Lessons
  • Setting up the environment - an introduction (do not skip, please)
    Preview .
  • Why Python and why Jupyter?
    Preview .
  • Installing Anaconda
    Preview .
  • The Jupyter dashboard (part 1)
    Preview .
  • The Jupyter dashboard (part 2)
    Preview .
  • Installing sklearn
    Preview .
Linear regression with stats models
22 Lessons
  • Multiple linear regression
    Preview .
  • Making predictions with the linear regression
    Preview .
  • Dealing with categorical data
    Preview .
  • No multicollinearity
    Preview .
  • No autocorrelation
    Preview .
  • Normality and homoscedasticity
    Preview .
  • No endogeneity
    Preview .
  • Linearity
    Preview .
  • OLS assumptions
    Preview .
  • Test for the significance of the model (F-test)
    Preview .
  • Adjusted R-squared
    Preview .
  • Introduction to Regression Analysis
    Preview .
  • R-squared
    Preview .
  • OLS
    Preview .
  • Decomposition of variability
    Preview .
  • How to interpret the regression table
    Preview .
  • Using Searborn for graphs
    Preview .
  • First regression in Python
    Preview .
  • Python packages installation
    Preview .
  • Geometrical representation
    Preview .
  • Correlation vs regression
    Preview .
  • The linear regression model
    Preview .
Linear regression with sklearn
13 Lessons
  • What is sklearn
    Preview .
  • Game plan for sklearn
    Preview .
  • Simple linear regression
    Preview .
  • Simple linear regression - summary table
    Preview .
  • Multiple linear regression
    Preview .
  • Adjusted R-squared
    Preview .
  • Feature Selection through p-values
    Preview .
  • Creating a summary table
    Preview .
  • Feature Scaling
    Preview .
  • Feature Selection through standardization
    Preview .
  • Making predictions with standardized coefficients
    Preview .
  • Underfitting and overfitting
    Preview .
  • Training and testing
    Preview .
Linear regression - practical example
5 Lessons
  • Linear regression - practical example (part 1)
    Preview .
  • Linear regression - practical example (part 2)
    Preview .
  • Linear regression - practical example (part 3)
    Preview .
  • Linear regression - practical example (part 4)
    Preview .
  • Linear regression - practical example (part 5)
    Preview .
Logistic regression
11 Lessons
  • A simple example in Python
    Preview .
  • Introduction to logistic regression
    Preview .
  • Logistic vs logit function
    Preview .
  • Building a logistic regression
    Preview .
  • An invaluable coding tip
    Preview .
  • Understanding the logistic regression tables
    Preview .
  • What do the odds actually mean
    Preview .
  • Binary predictors in a logistic regression
    Preview .
  • Calculating the accuracy of the model
    Preview .
  • Underfitting and overfitting
    Preview .
  • Testing the model
    Preview .
Cluster analysis
14 Lessons
  • Introduction to Cluster Analysis
    Preview .
  • Some examples of clusters
    Preview .
  • Difference between classificatio and clustering
    Preview .
  • Math prerequisites
    Preview .
  • K-means clustering
    Preview .
  • A simple example of clustering
    Preview .
  • Clustering categorical data
    Preview .
  • How to choose the number of clusters
    Preview .
  • Pros and Cons of K-means and clustering
    Preview .
  • To standardize or to not standardize
    Preview .
  • Relationship between clustering and regression
    Preview .
  • Market segmentation with cluster analysis (Part1)
    Preview .
  • Simple market segmentation_part2
    Preview .
  • How is clustering useful
    Preview .
Cluster analysis - additional topics
3 Lessons
  • Types of clustering
    Preview .
  • Dendrogram
    Preview .
  • Heatmaps using Seaborn
    Preview .
Additional Resources
1 Lessons
  • Exercises
    Preview .
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About instructor
Includes:
  • 5 hrs On demand videos
  • 77 Lessons
  • Access on mobile and tv
  • Full lifetime access