Data Science & Machine Learning with Python
Last Updated:
01 July 2025
Accreditation:
CPD Accredited
Prerequisites:
No formal qualification required (Anyone can participate)
Key features of the Data Science & Machine Learning with Python Course
- Recognised qualification upon successful completion of the course
- Study from anywhere, anytime, whenever it is convenient for you.
- Get 24/7 support from our Customer Success Team
- Affordable and engaging e-learning study materials
- Study at your own pace from a tablet, PC or smartphone
- Online tutor support when you are in need.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
Requirements
Our Data Science & Machine Learning with Python Course is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.
Career Path
After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.
Course Curriculum
91 Sections | 91 Lessons
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 2
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
Resource - Data Science & Machine Learning with Python
Order Your Certificates or Transcripts
Course Reviews
No reviews yet.
Key features of the Data Science & Machine Learning with Python Course
- Recognised qualification upon successful completion of the course
- Study from anywhere, anytime, whenever it is convenient for you.
- Get 24/7 support from our Customer Success Team
- Affordable and engaging e-learning study materials
- Study at your own pace from a tablet, PC or smartphone
- Online tutor support when you are in need.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
Requirements
Our Data Science & Machine Learning with Python Course is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.
Career Path
After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.
91 Sections | 91 Lessons
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 2
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
Resource - Data Science & Machine Learning with Python
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