Authoring Machine Learning Models from Scratch

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Mike West

1:31:35

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00001 Introduction.mp4

01:23

00002 What is this Course...Exactly.mp4

01:08

00003 Course Outcomes.mp4

01:17

00004 Course Structure.mp4

01:08

00005 What is an Algorithm in Programming.mp4

02:35

00006 Loading Data from a CSV File.mp4

03:46

00007 Scale Your Data - Normalization.mp4

02:16

00008 Scale Your Data - Standardization.mp4

02:12

00009 Algorithm Evaluation Methods.mp4

00:55

00010 Train-Test Split.mp4

02:19

00011 K-Fold Cross-Validation Defined.mp4

01:42

00012 K-Fold Cross-Validation.mp4

01:31

00013 Choosing a Resampling Method.mp4

01:10

00014 Evaluation Metrics.mp4

01:14

00015 Classification Accuracy.mp4

01:16

00016 Confusion Matrix.mp4

02:42

00017 Regression Metrics.mp4

02:24

00018 Baseline Models.mp4

01:04

00019 Random Prediction Algorithm.mp4

01:32

00020 Zero Rule Algorithm.mp4

02:37

00021 Algorithm Test Harness - Train-Test-Split.mp4

03:19

00022 Algorithm Test Harness - K-Fold.mp4

02:06

00023 Simple Linear Regression.mp4

01:59

00024 Simple Linear Regression Case Study - Part 1.mp4

02:24

00025 Simple Linear Regression Case Study - Part 2.mp4

02:03

00026 Multivariate Linear Regression Case Study.mp4

01:28

00027 Demo - Multivariate Linear Regression Case Study.mp4

03:30

00028 Demo - Linear Regression on Wine Quality Dataset.mp4

01:59

00029 Logistic Regression Defined.mp4

02:41

00030 Demo - Logistic Regression - Make Predictions.mp4

01:26

00031 Demo - Logistic Regression - Estimating Coefficients.mp4

02:14

00032 Demo - Logistic Regression - Diabetes Dataset.mp4

01:50

00033 Perceptron.mp4

01:27

00034 Demo - Perceptron - Make Predictions.mp4

02:03

00035 Demo - Perceptron - Training Weights.mp4

01:46

00036 Demo - Perceptron - Sonar Dataset.mp4

01:52

00037 Classification and Regression Trees.mp4

02:21

00038 Demo - CART - Creating the Gini Index.mp4

02:23

00039 Demo - CART - Creating the Splits.mp4

01:06

00040 Demo - CART - Evaluating the Splits.mp4

01:55

00041 CART - Building the Tree.mp4

01:54

00042 Demo - CART - Recursive Splitting.mp4

01:45

00043 Demo - CART - Assembling the Tree.mp4

01:32

00044 Demo - CART - CART to Banknote Dataset.mp4

01:27

00045 Naive Bayes.mp4

01:15

00046 Demo - Naive Bayes - Separate by Class.mp4

01:50

00047 Demo - Naive Bayes - Summarize the Dataset.mp4

02:28

00048 Demo - Naive Bayes - Summarize Data by Class.mp4

01:21

Authoring-Machine-Learning-Models-from-Scratch-main.zip

Description

A complete guide to learning the details of machine learning algorithms by implementing them from scratch in Python. You will discover how to load data, evaluate models, and implement a suite of top machine learning algorithms using step-by-step tutorials.

Machine learning algorithms do have a lot of math and theory under the covers, but you do not need to know why algorithms work to be able to implement them and apply them to achieve real and valuable results.

In this course, you will learn how to load from CSV files and prepare data for modeling; how to select algorithm evaluation metrics and resampling techniques for a test harness; how to develop a baseline expectation of performance for a given problem; how to implement and apply a suite of linear machine learning algorithms; how to implement and apply a suite of advanced nonlinear machine learning algorithms; how to implement and apply ensemble machine learning algorithms to improve performance.

This course will be an invaluable guide to understanding real-world machine learning models and help you understand the code behind math.

By the end of this course, you will gain insight into real-world machine learning models and learn how to code the functions of the most used tools in machine learning.

The complete code bundle for this course is available at https://github.com/PacktPublishing/Authoring-Machine-Learning-Models-from-Scratch

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Machine Learning

Mike West

Instructor's Courses

Mike has Bachelor of Science degrees in Business and Psychology. He started his career as a middle school psychologist prior to moving into the information technology space. His love of computers resulted in him spending many additional hours working on computers while studying for his master's degree in Statistics. His current areas of interests include Machine Learning, Data Engineering and SQL Server. When not working, Mike enjoys spending time with his family and traveling.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.