Description

Overview of Data Science & Machine Learning with Python

The digital revolution is changing how businesses work in the UK, and Data Science & Machine Learning with Python is playing a big role in this change. From helping stores predict customer behaviour to improving hospital systems, machine learning is now part of everyday business. Big companies like DeepMind, Babylon Health, HSBC, and Tesco are all hiring people who know Data Science & Machine Learning with Python. There are more than 50,000 job openings in this field, and salaries range from £35,000 to £120,000 per year.

This course is built for beginners who want to build strong skills in Data Science & Machine Learning with Python. You will learn how to use popular tools like NumPy, Pandas, and Matplotlib. The course teaches important topics such as regression, classification, ensemble models, and how to measure the success of your models. You will also work on real-world data, learn how to clean and prepare data, and use different techniques to improve model performance.

By the end of the course, you will be able to handle large amounts of data, build predictive models, and provide useful insights to help businesses make smart decisions. This Data Science & Machine Learning with Python course gives you the practical experience you need to work in industries like healthcare, banking, retail, and manufacturing across the UK.

What You Will Learn

Learn the basics of Python programming made for data science work

Use machine learning to build models for predicting and classifying data

Work with real data using tools like NumPy, Pandas, and Matplotlib

Check how well your models work using testing methods and performance scores

Clean and prepare data using feature selection and preprocessing steps

Build and use machine learning models that can make live predictions

Who Should Take This Course?

Beginners with no coding experience who want to start a career in data science — this course starts from scratch

Career changers working in finance, marketing, or operations who want to switch to a tech role using data

Students and graduates from maths, statistics, economics, or engineering backgrounds looking to add hands-on Python and machine learning skills

Business analysts or Excel users who want to move beyond spreadsheets and start using machine learning for better predictions

Self-taught coders who know a little Python and want structured training in data science and real-world projects

Process of Evaluation

After studying the Data Science & Machine Learning with Python Course, your skills and knowledge will be tested with an MCQ exam or assignment. You have to get a score of 60% to pass the test and get your certificate.

Certificate of Achievement

Certificate of Completion - Digital / PDF Certificate

After completing the Data Science & Machine Learning with Python Course, you can order your CPD Accredited Digital / PDF Certificate for £5.99. (Each)

Certificate of Completion - Hard copy Certificate

You can get the CPD Accredited Hard Copy Certificate for £12.99. (Each)

Shipping Charges:

Inside the UK: £3.99

International: £10.99

Requirements

You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course.

Career Path

This Data Science & Machine Learning with Python Course can help you work in roles like:

Data Scientist – £45,000 to £85,000 per year

Machine Learning Engineer – £50,000 to £95,000 per year

Data Analyst – £28,000 to £55,000 per year

Business Intelligence Analyst – £35,000 to £65,000 per year

Python Developer – £40,000 to £75,000 per year

Course Curriculum

Course Overview & Table of Contents
	Course Overview & Table of Contents	00:09:00
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
	Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types	00:05:00
Introduction to Machine Learning - Part 2 - Classifications and Applications
	Introduction to Machine Learning - Part 2 - Classifications and Applications	00:06:00
System and Environment preparation - Part 1
	System and Environment preparation - Part 1	00:04:00
System and Environment preparation - Part 2
	System and Environment preparation - Part 2	00:06:00
Learn Basics of python - Assignment
	Learn Basics of python - Assignment 1	00:10:00
Learn Basics of python - Assignment
	Learn Basics of python - Assignment 2	00:09:00
Learn Basics of python - Functions
	Learn Basics of python - Functions	00:04:00
Learn Basics of python - Data Structures
	Learn Basics of python - Data Structures	00:12:00
Learn Basics of NumPy - NumPy Array
	Learn Basics of NumPy - NumPy Array	00:06:00
Learn Basics of NumPy - NumPy Data
	Learn Basics of NumPy - NumPy Data	00:08:00
Learn Basics of NumPy - NumPy Arithmetic
	Learn Basics of NumPy - NumPy Arithmetic	00:04:00
Learn Basics of Matplotlib
	Learn Basics of Matplotlib	00:07:00
Learn Basics of Pandas - Part 1
	Learn Basics of Pandas - Part 1	00:06:00
Learn Basics of Pandas - Part 2
	Learn Basics of Pandas - Part 2	00:07:00
Understanding the CSV data file
	Understanding the CSV data file	00:09:00
Load and Read CSV data file using Python Standard Library
	Load and Read CSV data file using Python Standard Library	00:09:00
Load and Read CSV data file using NumPy
	Load and Read CSV data file using NumPy	00:04:00
Load and Read CSV data file using Pandas
	Load and Read CSV data file using Pandas	00:05:00
Dataset Summary - Peek, Dimensions and Data Types
	Dataset Summary - Peek, Dimensions and Data Types	00:09:00
Dataset Summary - Class Distribution and Data Summary
	Dataset Summary - Class Distribution and Data Summary	00:09:00
Dataset Summary - Explaining Correlation
	Dataset Summary - Explaining Correlation	00:11:00
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
	Dataset Summary - Explaining Skewness - Gaussian and Normal Curve	00:07:00
Dataset Visualization - Using Histograms
	Dataset Visualization - Using Histograms	00:07:00
Dataset Visualization - Using Density Plots
	Dataset Visualization - Using Density Plots	00:06:00
Dataset Visualization - Box and Whisker Plots
	Dataset Visualization - Box and Whisker Plots	00:05:00
Multivariate Dataset Visualization - Correlation Plots
	Multivariate Dataset Visualization - Correlation Plots	00:08:00
Multivariate Dataset Visualization - Scatter Plots
	Multivariate Dataset Visualization - Scatter Plots	00:05:00
Data Preparation (Pre-Processing) - Introduction
	Data Preparation (Pre-Processing) - Introduction	00:09:00
Data Preparation - Re-scaling Data - Part 1
	Data Preparation - Re-scaling Data - Part 1	00:09:00
Data Preparation - Re-scaling Data - Part 2
	Data Preparation - Re-scaling Data - Part 2	00:09:00
Data Preparation - Standardizing Data - Part 1
	Data Preparation - Standardizing Data - Part 1	00:07:00
Data Preparation - Standardizing Data - Part 2
	Data Preparation - Standardizing Data - Part 2	00:04:00
Data Preparation - Normalizing Data
	Data Preparation - Normalizing Data	00:08:00
Data Preparation - Binarizing Data
	Data Preparation - Binarizing Data	00:06:00
Feature Selection - Introduction
	Feature Selection - Introduction	00:07:00
Feature Selection - Uni-variate Part 1 - Chi-Squared Test
	Feature Selection - Uni-variate Part 1 - Chi-Squared Test	00:09:00
Feature Selection - Uni-variate Part 2 - Chi-Squared Test
	Feature Selection - Uni-variate Part 2 - Chi-Squared Test	00:10:00
Feature Selection - Recursive Feature Elimination
	Feature Selection - Recursive Feature Elimination	00:11:00
Feature Selection - Principal Component Analysis (PCA)
	Feature Selection - Principal Component Analysis (PCA)	00:09:00
Feature Selection - Feature Importance
	Feature Selection - Feature Importance	00:06:00
Refresher Session - The Mechanism of Re-sampling, Training and Testing
	Refresher Session - The Mechanism of Re-sampling, Training and Testing	00:12:00
Algorithm Evaluation Techniques - Introduction
	Algorithm Evaluation Techniques - Introduction	00:07:00
Algorithm Evaluation Techniques - Train and Test Set
	Algorithm Evaluation Techniques - Train and Test Set	00:11:00
Algorithm Evaluation Techniques - K-Fold Cross Validation
	Algorithm Evaluation Techniques - K-Fold Cross Validation	00:09:00
Algorithm Evaluation Techniques - Leave One Out Cross Validation
	Algorithm Evaluation Techniques - Leave One Out Cross Validation	00:05:00
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
	Algorithm Evaluation Techniques - Repeated Random Test-Train Splits	00:07:00
Algorithm Evaluation Metrics - Introduction
	Algorithm Evaluation Metrics - Introduction	00:09:00
Algorithm Evaluation Metrics - Classification Accuracy
	Algorithm Evaluation Metrics - Classification Accuracy	00:08:00
Algorithm Evaluation Metrics - Log Loss
	Algorithm Evaluation Metrics - Log Loss	00:03:00
Algorithm Evaluation Metrics - Area Under ROC Curve
	Algorithm Evaluation Metrics - Area Under ROC Curve	00:06:00
Algorithm Evaluation Metrics - Confusion Matrix
	Algorithm Evaluation Metrics - Confusion Matrix	00:10:00
Algorithm Evaluation Metrics - Classification Report
	Algorithm Evaluation Metrics - Classification Report	00:04:00
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
	Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction	00:06:00
Algorithm Evaluation Metrics - Mean Absolute Error
	Algorithm Evaluation Metrics - Mean Absolute Error	00:07:00
Algorithm Evaluation Metrics - Mean Square Error
	Algorithm Evaluation Metrics - Mean Square Error	00:03:00
Algorithm Evaluation Metrics - R Squared
	Algorithm Evaluation Metrics - R Squared	00:04:00
Classification Algorithm Spot Check - Logistic Regression
	Classification Algorithm Spot Check - Logistic Regression	00:12:00
Classification Algorithm Spot Check - Linear Discriminant Analysis
	Classification Algorithm Spot Check - Linear Discriminant Analysis	00:04:00
Classification Algorithm Spot Check - K-Nearest Neighbors
	Classification Algorithm Spot Check - K-Nearest Neighbors	00:05:00
Classification Algorithm Spot Check - Naive Bayes
	Classification Algorithm Spot Check - Naive Bayes	00:04:00
Classification Algorithm Spot Check - CART
	Classification Algorithm Spot Check - CART	00:04:00
Classification Algorithm Spot Check - Support Vector Machines
	Classification Algorithm Spot Check - Support Vector Machines	00:05:00
Regression Algorithm Spot Check - Linear Regression
	Regression Algorithm Spot Check - Linear Regression	00:08:00
Regression Algorithm Spot Check - Ridge Regression
	Regression Algorithm Spot Check - Ridge Regression	00:03:00
Regression Algorithm Spot Check - Lasso Linear Regression
	Regression Algorithm Spot Check - Lasso Linear Regression	00:03:00
Regression Algorithm Spot Check - Elastic Net Regression
	Regression Algorithm Spot Check - Elastic Net Regression	00:02:00
Regression Algorithm Spot Check - K-Nearest Neighbors
	Regression Algorithm Spot Check - K-Nearest Neighbors	00:06:00
Regression Algorithm Spot Check - CART
	Regression Algorithm Spot Check - CART	00:04:00
Regression Algorithm Spot Check - Support Vector Machines (SVM)
	Regression Algorithm Spot Check - Support Vector Machines (SVM)	00:04:00
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
	Compare Algorithms - Part 1 : Choosing the best Machine Learning Model	00:09:00
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
	Compare Algorithms - Part 2 : Choosing the best Machine Learning Model	00:05:00
Pipelines : Data Preparation and Data Modelling
	Pipelines : Data Preparation and Data Modelling	00:11:00
Pipelines : Feature Selection and Data Modelling
	Pipelines : Feature Selection and Data Modelling	00:10:00
Performance Improvement: Ensembles - Voting
	Performance Improvement: Ensembles - Voting	00:07:00
Performance Improvement: Ensembles - Bagging
	Performance Improvement: Ensembles - Bagging	00:08:00
Performance Improvement: Ensembles - Boosting
	Performance Improvement: Ensembles - Boosting	00:05:00
Performance Improvement: Parameter Tuning using Grid Search
	Performance Improvement: Parameter Tuning using Grid Search	00:08:00
Performance Improvement: Parameter Tuning using Random Search
	Performance Improvement: Parameter Tuning using Random Search	00:06:00
Export, Save and Load Machine Learning Models : Pickle
	Export, Save and Load Machine Learning Models : Pickle	00:10:00
Export, Save and Load Machine Learning Models : Joblib
	Export, Save and Load Machine Learning Models : Joblib	00:06:00
Finalizing a Model - Introduction and Steps
	Finalizing a Model - Introduction and Steps	00:07:00
Finalizing a Classification Model - The Pima Indian Diabetes Dataset
	Finalizing a Classification Model - The Pima Indian Diabetes Dataset	00:07:00
Quick Session: Imbalanced Data Set - Issue Overview and Steps
	Quick Session: Imbalanced Data Set - Issue Overview and Steps	00:09:00
Iris Dataset : Finalizing Multi-Class Dataset
	Iris Dataset : Finalizing Multi-Class Dataset	00:09:00
Finalizing a Regression Model - The Boston Housing Price Dataset
	Finalizing a Regression Model - The Boston Housing Price Dataset	00:08:00
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
	Real-time Predictions: Using the Pima Indian Diabetes Classification Model	00:07:00
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
	Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset	00:03:00
Real-time Predictions: Using the Boston Housing Regression Model
	Real-time Predictions: Using the Boston Housing Regression Model	00:08:00
Resources
	Resources - Data Science & Machine Learning with Python	00:00:00

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Data Science & Machine Learning with Python

By IOMH - Institute of Mental Health

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