Machine Learning Optimization Using Genetic Algorithm

Machine Learning Optimization Using Genetic Algorithm, available at $89.99, has an average rating of 4.75, with 57 lectures, 3 quizzes, based on 361 reviews, and has 3048 subscribers.

You will learn about Apply Genetic Algorithm to optimize Machine Learning algorithms Apply Genetic Algorithm on Support Vector Machines and Multilayer Perceptron Neural Networks Apply Genetic Algorithm for Feature Selection Learn how to code Genetic Algorithm in Python from scratch This course is ideal for individuals who are Anyone who wants to learn Genetic Algorithm or Anyone who would like to optimize the functionality of their Machine Learning algorithms or Anyone who would like to learn feature selection or Anyone who wants to code Genetic Algorithm in Python It is particularly useful for Anyone who wants to learn Genetic Algorithm or Anyone who would like to optimize the functionality of their Machine Learning algorithms or Anyone who would like to learn feature selection or Anyone who wants to code Genetic Algorithm in Python.

Enroll now: Machine Learning Optimization Using Genetic Algorithm

Summary

Title: Machine Learning Optimization Using Genetic Algorithm

Price: $89.99

Average Rating: 4.75

Number of Lectures: 57

Number of Quizzes: 3

Number of Published Lectures: 57

Number of Published Quizzes: 3

Number of Curriculum Items: 60

Number of Published Curriculum Objects: 60

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

Apply Genetic Algorithm to optimize Machine Learning algorithms

Apply Genetic Algorithm on Support Vector Machines and Multilayer Perceptron Neural Networks

Apply Genetic Algorithm for Feature Selection

Learn how to code Genetic Algorithm in Python from scratch

Who Should Attend

Anyone who wants to learn Genetic Algorithm

Anyone who would like to optimize the functionality of their Machine Learning algorithms

Anyone who would like to learn feature selection

Anyone who wants to code Genetic Algorithm in Python

Target Audiences

Anyone who wants to learn Genetic Algorithm

Anyone who would like to optimize the functionality of their Machine Learning algorithms

Anyone who would like to learn feature selection

Anyone who wants to code Genetic Algorithm in Python

In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines (SVMs) and Multilayer Perceptron Neural Networks (MLP NNs). It is referred to as hyperparameter tuning or parameter tuning. You will also learn how to do feature selection using Genetic Algorithm.

Hyperparameter optimization will be done on two datasets:

A regression dataset for the prediction of cooling and heating loads of buildings

A classification dataset regarding the classification of emails into spam and non-spam

The SVM and MLP will be applied on the datasets without optimization and compare their results to after their optimization

Feature Selection will be done on one dataset:

Classification of benign tumors from malignant tumors in a breast cancer dataset

By the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your machine learning algorithms for maximum performance. You would have also learnt how to apply Genetic Algorithm for feature selection.

To sum up:

You will learn what hyperparameters are (sometimes referred to as parameters, though different)

You will learn Genetic Algorithm

You will use Genetic Algorithm to optimize the performance of your machine learning algorithms

Maximize your model’s accuracy and predictive abilities

Optimize the performance of SVMs and MLP Neural Networks

Apply feature selection to extract the features that are relevant to the predicted output

Get the best out of your machine learning model

Remove redundant features, which in return will reduce the time and complexity of your model

Understand what are the features that have a relationship to the output and which do not

You do not need to have a lot of knowledge and experience in optimization or Python programming – it helps, but not a must to succeed in this course.

This course will teach you how to optimize the functionality of your machine learning algorithms

Where every single line of code is explained thoroughly

The code is written in a simple manner that you will understand how things work and how to code Genetic Algorithm even with zero knowledge in Python

Basically, you can think of this as not only a course that teaches you how to optimize your machine learning model, but also Python programming!

Please feel free to ask me any question! Don’t like the course? Ask for a 30-day refund!!

Real Testaments –>

1) “This is my second course with Dana. This course is a combination of Metaheuristic and machine learning. It gives a wide picture of machine learning hyperparameter optimization. I recommend taking this course if you know basics of machine learning and you want to solve some problems using ML. By applying the techniques of GA optimization, you will have better performance of ML. The codes provided in this course are very straightforward and easy to understand. The course deserves five stars because of the lecture contents and examples. The instructor knowledgeable about? the topic and talented in programming.” Abdulaziz, 5 star rating

2) “An excellent course! Great for anyone interested in fine-tuning their machine-learning models. I really enjoyed the from scratch implementations and how well they are explained. These implementations from scratch help one understand the theory very well. An interesting thing to point out is that this course uses Metaheustistics to optimise machine-learning. However, you can use machine-learning classifiers to help your Metaheuristic predict good or bad regions.” Dylan, 5 star rating

3) “Very helpful, for application of optimization algorithm to optimize ML algorithm parameters and got to do this using python, wonderful.” Erigits, 5 star rating

4) “well explained course. The topic is not an easy one but to date the explanations have been clear. The course has an interesting spreadsheet project.” Martin, 5 star rating

5) “Thank you very much for this awesome course. Lots of new things learn from this course.” Md. Mahmudul, 5 star rating

Course Curriculum

Chapter 1: Introduction

Lecture 1: Promo Video

Lecture 2: Course Outline

Lecture 3: Support Vector Machine

Lecture 4: Neural Networks

Lecture 5: Optimization

Lecture 6: P vs. NP Problems Resource (IMPORTANT!!!)

Lecture 7: Metaheuristics

Chapter 2: Genetic Algorithm

Lecture 1: PLEASE READ

Lecture 2: Genetic Algorithm #1

Lecture 3: Genetic Algorithm #2

Lecture 4: Genetic Algorithm #3

Lecture 5: Genetic Algorithm – Pseudocode and Flowchart

Lecture 6: Genetic Algorithm – Methodology

Lecture 7: The Purpose of Genetic Algorithm

Chapter 3: Dataset

Lecture 1: Dataset

Lecture 2: Dataset

Chapter 4: Support Vector Machine Optimization for a Regression Problem

Lecture 1: Updated Course (IMPORTANT)

Lecture 2: SVM Optimization #1 – Objective Function Value #1

Lecture 3: SVM Optimization #2 – Objective Function Value #2

Lecture 4: SVM Optimization #3 – Objective Function Value #3

Lecture 5: SVM Optimization #4 – Objective Function Value #4

Lecture 6: SVM Optimization #5 – Objective Function Value #5 (and the Dataset)

Lecture 7: The Dataset

Lecture 8: SVM Optimization #6 – Objective Function Value #6

Lecture 9: SVM Optimization #7 – Objective Function Value #7

Lecture 10: SVM Optimization #8 – Selecting Parents #1

Lecture 11: SVM Optimization #9 – Selecting Parents #2

Lecture 12: SVM Optimization #10 – Selecting Parents #3

Lecture 13: SVM Optimization #11 – Selecting Parents #4

Lecture 14: SVM Optimization #12 – Crossover Operator #1

Lecture 15: SVM Optimization #13 – Crossover Operator #2

Lecture 16: SVM Optimization #14 – Crossover Operator #3

Lecture 17: SVM Optimization #15 – Crossover Operator #4

Lecture 18: SVM Optimization #16 – Mutation Operator #1

Lecture 19: SVM Optimization #17 – Mutation Operator #2

Lecture 20: SVM Optimization #18 – Mutation Operator #3

Lecture 21: SVM Optimization #19 – Functions and Packages

Lecture 22: SVM Optimization #20 – Optimizing SVM on the Dataset #1

Lecture 23: SVM Optimization #21 – Optimizing SVM on the Dataset #2

Lecture 24: SVM Optimization #22 – Optimizing SVM on the Dataset #3

Lecture 25: SVM Optimization #23 – Optimizing SVM on the Dataset #4

Lecture 26: SVM Optimization #24 – Optimizing SVM on the Dataset #5

Lecture 27: SVM Optimization #25 – Optimizing SVM on the Dataset #6

Lecture 28: SVM Optimization #26 – Optimizing SVM on the Dataset #7

Chapter 5: Multilayer Perceptron Neural Network Optimization for a Regression Problem

Lecture 1: Updated Course Reminder (IMPORTANT)

Lecture 2: MLP Optimization #1

Lecture 3: MLP Optimization #2

Lecture 4: MLP Optimization #3

Lecture 5: MLP Optimization #4

Lecture 6: MLP Optimization #5

Lecture 7: MLP Optimization #6

Chapter 6: Support Vector Machine Optimization for a Classification Problem

Lecture 1: SVM Optimization

Chapter 7: Feature Selection Using Genetic Algorithm

Lecture 1: Feature Selection #1

Lecture 2: Feature Selection #2

Lecture 3: Feature Selection #3

Chapter 8: BONUS OFFER!!

Lecture 1: Bonus Lecture: Discounted Coupons

Chapter 9: Appendix

Lecture 1: Machine Learning

Instructors

Curiosity for Data Science
Architect and Industrial Engineer

Rating Distribution

1 stars: 9 votes

2 stars: 12 votes

3 stars: 66 votes

4 stars: 106 votes

5 stars: 168 votes

Frequently Asked Questions

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