Data Science & Machine Learning with Python
Last Updated:
01 July 2025
Number of Students:
6 Students
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
9m
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
5m
Introduction to Machine Learning - Part 2 - Classifications and Applications
6m
System and Environment preparation - Part 1
8m
System and Environment preparation - Part 2
6m
Learn Basics of python - Assignment 2
9m
Learn Basics of python - Functions
4m
Learn Basics of python - Data Structures
12m
Learn Basics of NumPy - NumPy Array
6m
Learn Basics of NumPy - NumPy Data
8m
Learn Basics of NumPy - NumPy Arithmetic
4m
Learn Basics of Matplotlib
7m
Learn Basics of Pandas - Part 1
6m
Learn Basics of Pandas - Part 2
6m
Understanding the CSV data file
9m
Load and Read CSV data file using Python Standard Library
9m
Load and Read CSV data file using NumPy
4m
Load and Read CSV data file using Pandas
6m
Dataset Summary - Peek, Dimensions and Data Types
9m
Dataset Summary - Class Distribution and Data Summary
9m
Dataset Summary - Explaining Correlation
11m
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
7m
Dataset Visualization - Using Histograms
7m
Dataset Visualization - Using Density Plots
6m
Dataset Visualization - Box and Whisker Plots
5m
Multivariate Dataset Visualization - Correlation Plots
8m
Multivariate Dataset Visualization - Scatter Plots
5m
Data Preparation (Pre-Processing) - Introduction
9m
Data Preparation - Re-scaling Data - Part 1
9m
Data Preparation - Re-scaling Data - Part 2
9m
Data Preparation - Standardizing Data - Part 1
7m
Data Preparation - Standardizing Data - Part 2
4m
Data Preparation - Normalizing Data
8m
Data Preparation - Binarizing Data
6m
Feature Selection - Introduction
7m
Feature Selection - Uni-variate Part 1 - Chi-Squared Test
9m
Feature Selection - Uni-variate Part 2 - Chi-Squared Test
10m
Feature Selection - Recursive Feature Elimination
11m
Feature Selection - Principal Component Analysis (PCA)
9m
Feature Selection - Feature Importance
7m
Refresher Session - The Mechanism of Re-sampling, Training and Testing
12m
Algorithm Evaluation Techniques - Introduction
7m
Algorithm Evaluation Techniques - Train and Test Set
11m
Algorithm Evaluation Techniques - K-Fold Cross Validation
9m
Algorithm Evaluation Techniques - Leave One Out Cross Validation
5m
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
7m
Algorithm Evaluation Metrics - Introduction
9m
Algorithm Evaluation Metrics - Classification Accuracy
8m
Algorithm Evaluation Metrics - Log Loss
3m
Algorithm Evaluation Metrics - Area Under ROC Curve
6m
Algorithm Evaluation Metrics - Confusion Matrix
10m
Algorithm Evaluation Metrics - Classification Report
4m
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
6m
Algorithm Evaluation Metrics - Mean Absolute Error
7m
Algorithm Evaluation Metrics - Mean Square Error
3m
Algorithm Evaluation Metrics - R Squared
4m
Classification Algorithm Spot Check - Logistic Regression
12m
Classification Algorithm Spot Check - Linear Discriminant Analysis
4m
Classification Algorithm Spot Check - K-Nearest Neighbors
5m
Classification Algorithm Spot Check - Naive Bayes
4m
Classification Algorithm Spot Check - CART
4m
Classification Algorithm Spot Check - Support Vector Machines
5m
Regression Algorithm Spot Check – Linear Regression
8m
Regression Algorithm Spot Check - Ridge Regression
3m
Regression Algorithm Spot Check - Lasso Linear Regression
3m
Regression Algorithm Spot Check - Elastic Net Regression
2m
Regression Algorithm Spot Check - K-Nearest Neighbors
6m
Regression Algorithm Spot Check - CART
4m
Regression Algorithm Spot Check - Support Vector Machines (SVM)
4m
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
9m
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
5m
Pipelines : Data Preparation and Data Modelling
11m
Pipelines : Feature Selection and Data Modelling
10m
Performance Improvement: Ensembles - Voting
7m
Performance Improvement: Ensembles - Bagging
8m
Performance Improvement: Ensembles - Boosting
5m
Performance Improvement: Parameter Tuning using Grid Search
8m
Performance Improvement: Parameter Tuning using Random Search
6m
Export, Save and Load Machine Learning Models : Pickle
10m
Export, Save and Load Machine Learning Models : Joblib
6m
Finalizing a Model - Introduction and Steps
7m
Finalizing a Classification Model - The Pima Indian Diabetes Dataset
7m
Quick Session: Imbalanced Data Set - Issue Overview and Steps
9m
Iris Dataset : Finalizing Multi-Class Dataset
9m
Finalizing a Regression Model - The Boston Housing Price Dataset
8m
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
7m
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
3m
Real-time Predictions: Using the Boston Housing Regression Model
8m
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
9m
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
5m
Introduction to Machine Learning - Part 2 - Classifications and Applications
6m
System and Environment preparation - Part 1
8m
System and Environment preparation - Part 2
6m
Learn Basics of python - Assignment 2
9m
Learn Basics of python - Functions
4m
Learn Basics of python - Data Structures
12m
Learn Basics of NumPy - NumPy Array
6m
Learn Basics of NumPy - NumPy Data
8m
Learn Basics of NumPy - NumPy Arithmetic
4m
Learn Basics of Matplotlib
7m
Learn Basics of Pandas - Part 1
6m
Learn Basics of Pandas - Part 2
6m
Understanding the CSV data file
9m
Load and Read CSV data file using Python Standard Library
9m
Load and Read CSV data file using NumPy
4m
Load and Read CSV data file using Pandas
6m
Dataset Summary - Peek, Dimensions and Data Types
9m
Dataset Summary - Class Distribution and Data Summary
9m
Dataset Summary - Explaining Correlation
11m
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
7m
Dataset Visualization - Using Histograms
7m
Dataset Visualization - Using Density Plots
6m
Dataset Visualization - Box and Whisker Plots
5m
Multivariate Dataset Visualization - Correlation Plots
8m
Multivariate Dataset Visualization - Scatter Plots
5m
Data Preparation (Pre-Processing) - Introduction
9m
Data Preparation - Re-scaling Data - Part 1
9m
Data Preparation - Re-scaling Data - Part 2
9m
Data Preparation - Standardizing Data - Part 1
7m
Data Preparation - Standardizing Data - Part 2
4m
Data Preparation - Normalizing Data
8m
Data Preparation - Binarizing Data
6m
Feature Selection - Introduction
7m
Feature Selection - Uni-variate Part 1 - Chi-Squared Test
9m
Feature Selection - Uni-variate Part 2 - Chi-Squared Test
10m
Feature Selection - Recursive Feature Elimination
11m
Feature Selection - Principal Component Analysis (PCA)
9m
Feature Selection - Feature Importance
7m
Refresher Session - The Mechanism of Re-sampling, Training and Testing
12m
Algorithm Evaluation Techniques - Introduction
7m
Algorithm Evaluation Techniques - Train and Test Set
11m
Algorithm Evaluation Techniques - K-Fold Cross Validation
9m
Algorithm Evaluation Techniques - Leave One Out Cross Validation
5m
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
7m
Algorithm Evaluation Metrics - Introduction
9m
Algorithm Evaluation Metrics - Classification Accuracy
8m
Algorithm Evaluation Metrics - Log Loss
3m
Algorithm Evaluation Metrics - Area Under ROC Curve
6m
Algorithm Evaluation Metrics - Confusion Matrix
10m
Algorithm Evaluation Metrics - Classification Report
4m
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
6m
Algorithm Evaluation Metrics - Mean Absolute Error
7m
Algorithm Evaluation Metrics - Mean Square Error
3m
Algorithm Evaluation Metrics - R Squared
4m
Classification Algorithm Spot Check - Logistic Regression
12m
Classification Algorithm Spot Check - Linear Discriminant Analysis
4m
Classification Algorithm Spot Check - K-Nearest Neighbors
5m
Classification Algorithm Spot Check - Naive Bayes
4m
Classification Algorithm Spot Check - CART
4m
Classification Algorithm Spot Check - Support Vector Machines
5m
Regression Algorithm Spot Check – Linear Regression
8m
Regression Algorithm Spot Check - Ridge Regression
3m
Regression Algorithm Spot Check - Lasso Linear Regression
3m
Regression Algorithm Spot Check - Elastic Net Regression
2m
Regression Algorithm Spot Check - K-Nearest Neighbors
6m
Regression Algorithm Spot Check - CART
4m
Regression Algorithm Spot Check - Support Vector Machines (SVM)
4m
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
9m
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
5m
Pipelines : Data Preparation and Data Modelling
11m
Pipelines : Feature Selection and Data Modelling
10m
Performance Improvement: Ensembles - Voting
7m
Performance Improvement: Ensembles - Bagging
8m
Performance Improvement: Ensembles - Boosting
5m
Performance Improvement: Parameter Tuning using Grid Search
8m
Performance Improvement: Parameter Tuning using Random Search
6m
Export, Save and Load Machine Learning Models : Pickle
10m
Export, Save and Load Machine Learning Models : Joblib
6m
Finalizing a Model - Introduction and Steps
7m
Finalizing a Classification Model - The Pima Indian Diabetes Dataset
7m
Quick Session: Imbalanced Data Set - Issue Overview and Steps
9m
Iris Dataset : Finalizing Multi-Class Dataset
9m
Finalizing a Regression Model - The Boston Housing Price Dataset
8m
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
7m
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
3m
Real-time Predictions: Using the Boston Housing Regression Model
8m
Resource - Data Science & Machine Learning with Python
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