.png)
5 Reviews
35 Students
Data Science & Machine Learning with Python
Data Science & Machine Learning with Python is a comprehensive discipline that merges statistical analysis, data processing, and predictive modelling to extract insights from raw data. This course, designed around applications, introduces learners to fundamental concepts of machine learning, classifications, and algorithms while building a strong foundation in Python programming. From understanding NumPy, Pandas, and Matplotlib to implementing classification, regression, and evaluation metrics, participants will explore every step of data preparation and analysis. By the end, learners can work confidently with datasets, apply feature selection, optimise models, and deliver predictions effectively.
Overview
Picture yourself standing at the edge of a world where every decision, from predicting customer behaviour to improving healthcare outcomes, is powered by data. The Data Science & Machine Learning with Python course is your gateway into this exciting future. By combining the flexibility of Python with advanced machine learning methods, the programme helps you turn raw data into meaningful solutions. Whether you’re new to the field or seeking to advance your career, this journey equips you with tools that businesses worldwide are eager to adopt.
Step into a curriculum designed to take you from the ground up. You’ll begin by exploring the foundations of machine learning—its concepts, classifications, and diverse applications—before moving into Python essentials like NumPy, Pandas, and Matplotlib. Along the way, you’ll practise preparing datasets, visualising patterns, and applying algorithms such as logistic regression, decision trees, and support vector machines. With projects, you’ll gain the confidence to test, evaluate, and fine-tune models, while learning how to apply advanced strategies like ensemble methods, boosting, and parameter optimisation.
What makes this data science & machine learning with python course online truly powerful is its focus on application. By working through authentic datasets like the Boston Housing and Pima Indian Diabetes collections, you’ll experience the complete cycle of building, training, and deploying predictive models. With guidance on performance metrics, correlation, and cross-validation, you’ll be ready to create accurate, data-driven insights. This isn’t just a course—it’s your path to becoming a highly sought-after professional in a rapidly evolving field.
Learning outcome
- Apply Python libraries such as NumPy, Pandas, and Matplotlib for data handling and visualisation.
- Perform feature selection, preprocessing, and dataset preparation for reliable model building.
- Evaluate machine learning algorithms with metrics including accuracy, log loss, and R-squared.
- Develop predictive models for classification, regression, and multi-class data.
- Optimise machine learning performance with ensembles and hyperparameter tuning.
Certificate of Achievement
Quality Licence Scheme Endorsed Certificate
Upon completing the final assessment, you can apply for the Quality Licence Scheme Endorsed Certificate of Achievement. Endorsed certificates can be ordered and delivered to your home by post for only £129.
Order Your QLS Certificate
An extra £10 postage charge will be required for students leaving overseas.
Skill Up Recognised Certificate
Upon successful completion of the Data Science & Machine Learning with Python course, you have the opportunity to request a Skill Up Recognised Certificate. This certificate holds significant value, and its validation will endure throughout your lifetime.
- 1. PDF Certificate + PDF Transcript: £14.99
- 2. Hardcopy Certificate + Hardcopy Transcript: £19.99
- 3. Delivery Charge: £10.00 (Applicable for International Students)
Why should I take this course?
- Individuals seeking to pursue careers in python for data science and machine learning.
- Professionals eager to enhance decision-making through predictive analytics.
- Graduates preparing for roles in analytics, AI, or data-led industries.
- Learners interested in a structured data science & machine learning with python course online.
- Anyone aiming to apply statistical modelling and Python coding to solve problems.
Career Path
- Data Scientist: £45,000 – £80,000 per year
- Machine Learning Engineer: £50,000 – £90,000 per year
- Data Analyst: £40,000 – £55,000 per year
- Business Intelligence Analyst: £45,000 – £60,000 per year
- Python Developer: £45,000 – £65,000 per year
- AI Research Associate: £40,000 – £75,000 per year
Requirements
- Basic understanding of programming concepts.
- Eagerness to learn Data Science and Machine Learning.
- A computer with internet access.
Frequestly Asked Questions
Data Science is a broad field focused on analysing data to generate meaningful insights, make decisions, and solve business problems using statistics, programming, and data tools. Machine Learning is a key subset of Data Science that uses algorithms to learn patterns from data and make predictions without being explicitly programmed. Machine Learning often drives AI features like recommendation engines and predictive models.
No formal degree is strictly required — many professionals enter these fields via online courses, bootcamps, hands-on projects, and self-study using Python, R, and real datasets. Building a portfolio of practical projects often matters as much (or more) than academic qualifications when applying for jobs in the UK tech market.
Key skills include:
– Programming: Python (dominant) or R for analytics & modelling
– Statistics & Maths: Probability, linear algebra, hypothesis testing
– Machine Learning: Algorithms such as regression, classification, clustering
– Data Handling: SQL, data cleaning, big data tools
– ML Frameworks: scikit-learn, TensorFlow, PyTorch
These skills help you handle data end-to-end — from preparation to model deployment.
Machine Learning helps in diverse applications including:
– Predicting trends like customer behaviour
– Image and speech recognition
– Automating decision processes and optimising business workflows
– Personalised recommendations on platforms like shopping or streaming apps
These models improve over time by learning from new data.
The UK tech sector continues to see strong demand for data professionals — especially those skilled in AI, ML and data infrastructure. Employers value practical experience with data tools, ML pipelines, and the ability to communicate insights effectively to business teams. Continuous learning is essential due to fast-evolving tools and methodologies.
Curriculum
-
Course Overview & Table of Contents
00:09:00
-
Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types
00:05:00
-
Introduction to Machine Learning – Part 2 – Classifications and Applications
00:06:00
-
System and Environment preparation – Part 1
00:08:00
-
System and Environment preparation – Part 2
00:06:00
-
Learn Basics of python – Assignment 2
00:09:00
-
Learn Basics of python – Functions
00:04:00
-
Learn Basics of python – Data Structures
00:12:00
-
Learn Basics of NumPy – NumPy Array
00:06:00
-
Learn Basics of NumPy – NumPy Data
00:08:00
-
Learn Basics of NumPy – NumPy Arithmetic
00:04:00
-
Learn Basics of Matplotlib
00:07:00
-
Learn Basics of Pandas – Part 1
00:06:00
-
Learn Basics of Pandas – Part 2
00:07:00
-
Understanding the CSV data file
00:09:00
-
Load and Read CSV data file using Python Standard Library
00:09:00
-
Load and Read CSV data file using NumPy
00:04:00
-
Load and Read CSV data file using Pandas
00:05:00
-
Dataset Summary – Peek, Dimensions and Data Types
00:09:00
-
Dataset Summary – Class Distribution and Data Summary
00:09:00
-
Dataset Summary – Explaining Correlation
00:11:00
-
Dataset Summary – Explaining Skewness – Gaussian and Normal Curve
00:07:00
-
Dataset Visualization – Using Histograms
00:07:00
-
Dataset Visualization – Using Density Plots
00:06:00
-
Dataset Visualization – Box and Whisker Plots
00:05:00
-
Multivariate Dataset Visualization – Correlation Plots
00:08:00
-
Multivariate Dataset Visualization – Scatter Plots
00:05:00
-
Data Preparation (Pre-Processing) – Introduction
00:09:00
-
Data Preparation – Re-scaling Data – Part 1
00:09:00
-
Data Preparation – Re-scaling Data – Part 2
00:09:00
-
Data Preparation – Standardizing Data – Part 1
00:07:00
-
Data Preparation – Standardizing Data – Part 2
00:04:00
-
Data Preparation – Normalizing Data
00:08:00
-
Data Preparation – Binarizing Data
00:06:00
-
Feature Selection – Introduction
00:07:00
-
Feature Selection – Uni-variate Part 1 – Chi-Squared Test
00:09:00
-
Feature Selection – Uni-variate Part 2 – Chi-Squared Test
00:10:00
-
Feature Selection – Recursive Feature Elimination
00:11:00
-
Feature Selection – Principal Component Analysis (PCA)
00:09:00
-
Feature Selection – Feature Importance
00:07:00
-
Refresher Session – The Mechanism of Re-sampling, Training and Testing
00:12:00
-
Algorithm Evaluation Techniques – Introduction
00:07:00
-
Algorithm Evaluation Techniques – Train and Test Set
00:11:00
-
Algorithm Evaluation Techniques – K-Fold Cross Validation
00:09:00
-
Algorithm Evaluation Techniques – Leave One Out Cross Validation
00:05:00
-
Algorithm Evaluation Techniques – Repeated Random Test-Train Splits
00:07:00
-
Algorithm Evaluation Metrics – Introduction
00:09:00
-
Algorithm Evaluation Metrics – Classification Accuracy
00:08:00
-
Algorithm Evaluation Metrics – Log Loss
00:03:00
-
Algorithm Evaluation Metrics – Area Under ROC Curve
00:06:00
-
Algorithm Evaluation Metrics – Classification Report
00:04:00
-
Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction
00:06:00
-
Algorithm Evaluation Metrics – Mean Absolute Error
00:07:00
-
Algorithm Evaluation Metrics – Mean Square Error
00:03:00
-
Algorithm Evaluation Metrics – R Squared
00:04:00
-
Classification Algorithm Spot Check – Logistic Regression
00:12:00
-
Classification Algorithm Spot Check – Linear Discriminant Analysis
00:04:00
-
Classification Algorithm Spot Check – K-Nearest Neighbors
00:05:00
-
Classification Algorithm Spot Check – Naive Bayes
00:04:00
-
Classification Algorithm Spot Check – CART
00:04:00
-
Classification Algorithm Spot Check – Support Vector Machines
00:05:00
-
Regression Algorithm Spot Check – Linear Regression
00:08:00
-
Regression Algorithm Spot Check – Ridge Regression
00:03:00
-
Regression Algorithm Spot Check – Lasso Linear Regression
00:03:00
-
Regression Algorithm Spot Check – Elastic Net Regression
00:02:00
-
Regression Algorithm Spot Check – K-Nearest Neighbors
00:06:00
-
Regression Algorithm Spot Check – CART
00:04:00
-
Regression Algorithm Spot Check – Support Vector Machines (SVM)
00:04:00
-
Compare Algorithms – Part 1 : Choosing the best Machine Learning Model
00:09:00
-
Compare Algorithms – Part 2 : Choosing the best Machine Learning Model
00:05:00
-
Pipelines : Data Preparation and Data Modelling
00:11:00
-
Pipelines : Feature Selection and Data Modelling
00:10:00
-
Performance Improvement: Ensembles – Voting
00:07:00
-
Performance Improvement: Ensembles – Bagging
00:08:00
-
Performance Improvement: Ensembles – Boosting
00:05:00
-
Performance Improvement: Parameter Tuning using Grid Search
00:08:00
-
Performance Improvement: Parameter Tuning using Random Search
00:06:00
-
Export, Save and Load Machine Learning Models : Pickle
00:10:00
-
Export, Save and Load Machine Learning Models : Joblib
00:06:00
-
Finalizing a Model – Introduction and Steps
00:07:00
-
Finalizing a Classification Model – The Pima Indian Diabetes Dataset
00:07:00
-
Quick Session: Imbalanced Data Set – Issue Overview and Steps
00:09:00
-
Iris Dataset : Finalizing Multi-Class Dataset
00:09:00
-
Finalizing a Regression Model – The Boston Housing Price Dataset
00:08:00
-
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
00:07:00
-
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
00:03:00
-
Real-time Predictions: Using the Boston Housing Regression Model
00:08:00
-
Resources – Data Science & Machine Learning with Python
-
Submit Your Assignment & Order QLS Certificate
-
Skill Up Recognised Certificate
-
Order CPDQS Certificate
Offer Ends in
All
Courses for £49-
Duration:10 hours, 24 minutes
-
Access:1 Year
-
Units:93
