Machine Learning Top 5 Models Implementation "A-Z"

Machine Learning Top 5 Models Implementation "A-Z", available at $19.99, has an average rating of 4.31, with 20 lectures, based on 8 reviews, and has 41 subscribers.

You will learn about From Dataset to Machine Learning 5 Models scenarios Implementation Understanding the dataset Data Analysis (missing values, outliers, outliers detection techniques, correlation) Feature engineering Selecting algorithms Training the baseline Understanding the testing matrix (ROC, AUC, Accuracy, Kappa…) Testing the baseline model Problems with the existing approach Cross validation, Grid search, Models parameters tuning Models optimization, Ensembles and much more …. This course is ideal for individuals who are For all students willing to have a career in machine learning It is particularly useful for For all students willing to have a career in machine learning.

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Summary

Title: Machine Learning Top 5 Models Implementation "A-Z"

Price: $19.99

Average Rating: 4.31

Number of Lectures: 20

Number of Published Lectures: 20

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

From Dataset to Machine Learning 5 Models scenarios Implementation
Understanding the dataset
Data Analysis (missing values, outliers, outliers detection techniques, correlation)
Feature engineering
Selecting algorithms
Training the baseline
Understanding the testing matrix (ROC, AUC, Accuracy, Kappa…)
Testing the baseline model
Problems with the existing approach
Cross validation, Grid search, Models parameters tuning
Models optimization, Ensembles
and much more ….

Who Should Attend

For all students willing to have a career in machine learning

Target Audiences

For all students willing to have a career in machine learning

One case study, five models from data preprocessing to implementation with Python, with some examples where no coding is required.

We will cover the following topics in this case study

Problem Statement�

Data�

Data Preprocessing 1

Understanding Dataset

Data change and Data Statistics

Data Preprocessing 2

Missing values

Replacing missing values

Correlation Matrix

Data Preprocessing 3

Outliers

Outliers Detection Techniques

Percentile-based outlier detection

Mean Absolute Deviation (MAD)-based outlier detection

Standard Deviation (STD)-based outlier detection

Majority-vote based outlier detection

Visualizing outlier

Data Preprocessing 4

Handling outliers

Feature Engineering

Models Selected

·K-Nearest Neighbor (KNN)

·Logistic regression

·AdaBoost

·GradientBoosting

·RandomForest

·Performing the Baseline Training

Understanding the testing matrix

·The Mean accuracy of the trained models

·The ROC-AUC score

ROC

AUC

Performing the Baseline Testing

Problems with this Approach

Optimization Techniques

·Understanding key concepts to optimize the approach

Cross-validation

The approach of using CV

Hyperparameter tuning

Grid search parameter tuning

Random search parameter tuning

Optimized Parameters Implementation

·Implementing a cross-validation based approach

·Implementing hyperparameter tuning

·Implementing and testing the revised approach

·Understanding problems with the revised approach

Implementation of the revised approach

·Implementing the best approach

Log transformation of features

Voting-based ensemble ML model

·Running ML models on real test data

Best approach & Summary

Examples with No Code

Downloads – Full Code

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Data Preprocessing

Lecture 1: Introducing the problem statement

Lecture 2: Introduction To "No Code"

Lecture 3: Data Preprocessing 1

Lecture 4: Data Preprocessing 2

Lecture 5: Data Preprocessing 3

Lecture 6: Data Preprocessing 4

Lecture 7: Feature Engineering for the Baseline Model

Lecture 8: Feature Engineering for the Baseline Model v2

Lecture 9: Data Preprocessing with "No Code"

Chapter 3: Models Selection

Lecture 1: Training Baseline and Understanding the Testing Matrix

Lecture 2: Compare ROC Models "No Code"

Lecture 3: Baseline Model Testing "No Code"

Lecture 4: Testing the Baseline

Lecture 5: Problems with the Existing Approach

Chapter 4: Optimization & Implementation

Lecture 1: Optimization the Existing Approach

Lecture 2: Implementing the Revised Approach

Chapter 5: Best Approach Implementation & Summary

Lecture 1: Best Approach

Chapter 6: More Examples on "No Coding"

Lecture 1: Example1-Parameter Optimization

Lecture 2: Example2-Cross Validation

Instructors

Amine Mehablia
Cybersecurity, Machine Learning and Blockchain

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 0 votes
5 stars: 6 votes

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