Data Science & Machine Learning With Python

Learn Python for data analysis using NumPy and Pandas

Obtain a clear understanding of datasets visualisation

Become more proficient in principal component analysis

Gain knowledge about algorithm evaluation techniques

Improve your knowledge of performance improvement

Equip yourself with the skills for data preparations and data modelling

Web Developer

Software Engineer

Data Scientist

Machine Learning Engineer

Data Analyst

Course Overview & Table of Contents

Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types

Introduction to Machine Learning - Part 2 - Classifications and Applications

System and Environment preparation - Part 1

System and Environment preparation - Part 2

Learn Basics of python - Assignment

Learn Basics of python - Assignment

Learn Basics of python - Functions

Learn Basics of python - Data Structures

Learn Basics of NumPy - NumPy Array

Learn Basics of NumPy - NumPy Data

Learn Basics of NumPy - NumPy Arithmetic

Learn Basics of Matplotlib

Learn Basics of Pandas - Part 1

Learn Basics of Pandas - Part 2

Understanding the CSV data file

Load and Read CSV data file using Python Standard Library

Load and Read CSV data file using NumPy

Load and Read CSV data file using Pandas

Dataset Summary - Peek, Dimensions and Data Types

Dataset Summary - Class Distribution and Data Summary

Dataset Summary - Explaining Correlation

Dataset Summary - Explaining Skewness - Gaussian and Normal Curve

Dataset Visualization - Using Histograms

Dataset Visualization - Using Density Plots

Dataset Visualization - Box and Whisker Plots

Multivariate Dataset Visualization - Correlation Plots

Multivariate Dataset Visualization - Scatter Plots

Data Preparation (Pre-Processing) - Introduction

Data Preparation - Re-scaling Data - Part 1

Data Preparation - Re-scaling Data - Part 2

Data Preparation - Standardizing Data - Part 1

Data Preparation - Standardizing Data - Part 2

Data Preparation - Normalizing Data

Data Preparation - Binarizing Data

Feature Selection - Introduction

Feature Selection - Uni-variate Part 1 - Chi-Squared Test

Feature Selection - Uni-variate Part 2 - Chi-Squared Test

Feature Selection - Recursive Feature Elimination

Feature Selection - Principal Component Analysis (PCA)

Feature Selection - Feature Importance

Refresher Session - The Mechanism of Re-sampling, Training and Testing

Algorithm Evaluation Techniques - Introduction

Algorithm Evaluation Techniques - Train and Test Set

Algorithm Evaluation Techniques - K-Fold Cross Validation

Algorithm Evaluation Techniques - Leave One Out Cross Validation

Algorithm Evaluation Techniques - Repeated Random Test-Train Splits

Algorithm Evaluation Metrics - Introduction

Algorithm Evaluation Metrics - Classification Accuracy

Algorithm Evaluation Metrics - Log Loss

Algorithm Evaluation Metrics - Area Under ROC Curve

Algorithm Evaluation Metrics - Confusion Matrix

Algorithm Evaluation Metrics - Classification Report

Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction

Algorithm Evaluation Metrics - Mean Absolute Error

Algorithm Evaluation Metrics - Mean Square Error

Algorithm Evaluation Metrics - R Squared

Classification Algorithm Spot Check - Logistic Regression

Classification Algorithm Spot Check - Linear Discriminant Analysis

Classification Algorithm Spot Check - K-Nearest Neighbors

Classification Algorithm Spot Check - Naive Bayes

Classification Algorithm Spot Check - CART

Classification Algorithm Spot Check - Support Vector Machines

Regression Algorithm Spot Check - Linear Regression

Regression Algorithm Spot Check - Ridge Regression

Regression Algorithm Spot Check - Lasso Linear Regression

Regression Algorithm Spot Check - Elastic Net Regression

Regression Algorithm Spot Check - K-Nearest Neighbors

Regression Algorithm Spot Check - CART

Regression Algorithm Spot Check - Support Vector Machines (SVM)

Compare Algorithms - Part 1 : Choosing the best Machine Learning Model

Compare Algorithms - Part 2 : Choosing the best Machine Learning Model

Pipelines : Data Preparation and Data Modelling

Pipelines : Feature Selection and Data Modelling

Performance Improvement: Ensembles - Voting

Performance Improvement: Ensembles - Bagging

Performance Improvement: Ensembles - Boosting

Performance Improvement: Parameter Tuning using Grid Search

Performance Improvement: Parameter Tuning using Random Search

Export, Save and Load Machine Learning Models : Pickle

Export, Save and Load Machine Learning Models : Joblib

Finalizing a Model - Introduction and Steps

Finalizing a Classification Model - The Pima Indian Diabetes Dataset

Quick Session: Imbalanced Data Set - Issue Overview and Steps

Iris Dataset : Finalizing Multi-Class Dataset

Finalizing a Regression Model - The Boston Housing Price Dataset

Real-time Predictions: Using the Pima Indian Diabetes Classification Model

Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset

Real-time Predictions: Using the Boston Housing Regression Model