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Home> Data> Data Science> Authoring Machine Learning Models from Scratch
Authoring Machine Learning Models from Scratch
Authoring Machine Learning Models from Scratch

Authoring Machine Learning Models from Scratch: A Step-by-Step Guide to Understanding Machine Learning Algorithms in Python

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Profile Icon Mike West
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€16.99
Full star iconFull star iconFull star iconHalf star iconEmpty star icon3.5(2 Ratings)
VideoNov 20211hr 31mins1st Edition
Video
€16.99
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Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Mike West
Arrow right icon
€16.99
Full star iconFull star iconFull star iconHalf star iconEmpty star icon3.5(2 Ratings)
VideoNov 20211hr 31mins1st Edition
Video
€16.99
Subscription
Free Trial
Renews at €18.99p/m
Video
€16.99
Subscription
Free Trial
Renews at €18.99p/m

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Key benefits

  • Know how top machine learning algorithms work internally
  • Learn to configure machine learning algorithms to get the most out of them
  • Understand the myriad of micro-decisions that a machine learning library has hidden from you in practice

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

Who is this book for?

This course is for developers, machine learning engineers, and data scientists who want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Additionally, you should have a solid background in Python.

What you will learn

  • Develop a baseline expectation of performance for a given problem
  • Learn to code the functions of the most used tools in machine learning
  • Gain insight into who real-world machine learning models are written
  • Gain a deep appreciation for how the algorithm works
  • Implement and apply a suite of linear machine learning algorithms
  • Implement and apply a suite of advanced non-linear ML algorithms

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date :Nov 23, 2021
Length:1hr 31mins
Edition :1st
Language :English
ISBN-13 :9781803238272
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Languages :
Concepts :
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Product Details

Publication date :Nov 23, 2021
Length:1hr 31mins
Edition :1st
Language :English
ISBN-13 :9781803238272
Category :
Languages :
Concepts :
Tools :

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Authoring Machine Learning Models from Scratch
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Table of Contents

4 Chapters
IntroductionChevron down iconChevron up icon
Introduction
What is this Course...Exactly?
Course Outcomes
Course Structure
What is an Algorithm in Programming?
Data PreparationChevron down iconChevron up icon
Loading Data from a CSV File
Scale Your Data: Normalization
Scale Your Data: Standardization
Algorithm Evaluation Methods
Train-Test Split
K-Fold Cross-Validation Defined
K-Fold Cross-Validation
Choosing a Resampling Method
Evaluation Metrics
Classification Accuracy
Confusion Matrix
Regression Metrics
Baseline Models
Random Prediction Algorithm
Zero Rule Algorithm
Linear AlgorithmsChevron down iconChevron up icon
Algorithm Test Harness - Train-Test-Split
Algorithm Test Harness - K-Fold
Simple Linear Regression
Simple Linear Regression Case Study: Part 1
Simple Linear Regression Case Study: Part 2
Multivariate Linear Regression Case Study
Demo: Multivariate Linear Regression Case Study
Demo: Linear Regression on Wine Quality Dataset
Logistic Regression Defined
Demo: Logistic Regression: Make Predictions
Demo: Logistic Regression: Estimating Coefficients
Demo: Logistic Regression: Diabetes Dataset
Perceptron
Demo: Perceptron: Make Predictions
Demo: Perceptron: Training Weights
Demo: Perceptron: Sonar Dataset
Non-Linear RegressionChevron down iconChevron up icon
Classification and Regression Trees
Demo: CART: Creating the Gini Index
Demo: CART: Creating the Splits
Demo: CART: Evaluating the Splits
CART: Building the Tree
Demo: CART: Recursive Splitting
Demo: CART: Assembling the Tree
Demo: CART: CART to Banknote Dataset
Naïve Bayes
Demo: Naïve Bayes: Separate by Class
Demo: Naïve Bayes: Summarize the Dataset
Demo: Naïve Bayes: Summarize Data by Class

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Customer reviews

Rating distribution
Full star iconFull star iconFull star iconHalf star iconEmpty star icon3.5
(2 Ratings)
5 star50%
4 star0%
3 star0%
2 star50%
1 star0%
Carlo EstopiaFeb 18, 2024
Full star iconFull star iconFull star iconFull star iconFull star icon5
Feefo Verified reviewFeefo
eikeMay 14, 2025
Full star iconFull star iconEmpty star iconEmpty star iconEmpty star icon2
First, let me say that this is a refreshing kind of video lecture. Most machine learning tutorials say: "use this library and copy this code, and it works", here you see what the libraries do.But, it is a lot of content, which is quickly explained. Each video is about 2 minutes long, but to fully understand each, you need to watch it more than once and do additional research.Additionally, Chapter 4 just ends, there is no complete explanation on Naive Bayes.
Subscriber reviewPackt

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About the author

Profile icon Mike West
Mike West
Mike West is the founder of LogikBot. He has worked with databases for over two decades. He has worked for or consulted with over 50 different companies as a full-time employee or consultant. These were Fortune 500 as well as several small to mid-size companies. Some include Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light, and Northrup Grumman.Over the last five years, Mike has transitioned to the exciting world of applied machine learning. He is excited to show you what he has learned and help you move into one of the single-most important fields in this space.
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