**Machine****Learning****Top**with this Online Training**Machine Learning Top 5 Models Implementation "A**-Z". Start training yourself now.by Amine Mehablia

What you'll 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 ....

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**

Who this course is for:

- For all students willing to have a career in machine learning

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