Data Science & Machine Learning with Python Course

Data Science & Machine Learning with Python

Master in-demand data skills with our Python Data Science & Machine Learning course. Learn to clean data, build predictive models, and create interactive visual dashboards. No prior experience required-start your journey today.

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Course curriculum

1. Who is This Course For?
2. Data Science + Machine Learning Marketplace
3. Data Science Job Opportunities
4. Data Science Job Roles
5. What is a Data Scientist?
6. How To Get a Data Science Job
7. Data Science Projects Overview
1. Why We Use Python?
2. What is Data Science?
3. What is Machine Learning?
4. Machine Learning Concepts & Algorithms
5. What is Deep Learning?
6. Machine Learning vs Deep Learning
1. What is Programming?
2. Why Python for Data Science?
3. What is Jupyter?
4. What is Google Colab?
5. Python Variables, Booleans and None
6. Getting Started with Google Colab
7. Python Operators
8. Python Numbers & Booleans
9. Python Strings
10. Python Conditional Statements
11. Python For Loops and While Loops
12. Python Lists
13. More about Lists
14. Python Tuples
15. Python Dictionaries
16. Python Sets
17. Compound Data Types & When to use each one?
18. Python Functions
19. Object Oriented Programming in Python
1. Python Data Science Handbook
1. Intro To Statistics
2. Descriptive Statistics
3. Measure of Variability
4. Measure of Variability Continued
5. Measures of Variable Relationship
6. Inferential Statistics
7. Measure of Asymmetry
8. Sampling Distribution
1. What Exactly is Probability?
2. Expected Values
3. Relative Frequency
4. Hypothesis Testing Overview
1. Intro NumPy Array Data Types
2. NumPy Arrays
3. NumPy Arrays Basics
4. NumPy Array Indexing
5. NumPy Array Computations
6. Broadcasting
1. Introduction to Pandas
2. Introduction to Pandas Continued
1. Python Data analysis book
1. Data Visualization Overview
2. Different Data Visualization Libraries in Python
3. Python Data Visualization Implementation
1. Introduction To Machine Learning
1. Exploratory Data Analysis
1. Feature Scaling
2. Data Cleaning
1. Feature Engineering
1. Linear Regression Intro
2. Gradient Descent
3. Linear Regression + Correlation Methods
4. Linear Regression Implementation
5. Logistic Regression
1. KNN Overview
2. parametric vs non-parametric models
3. EDA on Iris Dataset
4. The KNN Intuition
5. Implement the KNN algorithm from scratch
6. Compare the result with the sklearn library
7. Hyperparameter tuning using the cross-validation
8. The decision boundary visualization
9. Manhattan vs Euclidean Distance
10. Feature scaling in KNN
11. Curse of dimensionality
12. KNN use cases
13. KNN pros and cons
1. Decision Trees Section Overview
2. EDA on Adult Dataset
3. What is Entropy and Information Gain?
4. The Decision Tree ID3 algorithm from scratch Part 1
5. The Decision Tree ID3 algorithm from scratch Part 2
6. The Decision Tree ID3 algorithm from scratch Part 3
7. ID3 – Putting Everything Together
8. Evaluating our ID3 implementation
9. Compare with sklearn implementation
10. Visualizing the tree
11. Plot the features importance
12. Decision Trees Hyper-parameters
13. Pruning
14. [Optional] Gain Ration
15. Decision Trees Pros and Cons
16. [Project] Predict whether income exceeds $50K/yr – Overview
1. Ensemble Learning Section Overview
2. What is Ensemble Learning?
3. What is Bootstrap Sampling?
4. What is Bagging?
5. Out-of-Bag Error (OOB Error)
6. Implementing Random Forests from scratch Part 1
7. Implementing Random Forests from scratch Part 2
8. Compare with sklearn implementation
9. Random Forests Hyper-Parameters
10. Random Forests Pros and Cons
11. What is Boosting?
12. AdaBoost Part 1
13. AdaBoost Part 2
1. SVM Outline
2. SVM intuition
3. Hard vs Soft Margins
4. C hyper-parameter
5. Kernel Trick
6. SVM – Kernel Types
7. SVM with Non-linear Dataset
8. SVM with Linear Dataset (Iris)
9. SVM with Regression
10. [Project] Voice Gender Recognition using SVM
1. Unsupervised Machine Learning Intro
2. Representation of Clusters
3. Data Standardization
1. PCA Section Overview
2. What is PCA?
3. PCA Drawbacks
4. PCA Algorithm Steps (Mathematics)
5. Covariance Matrix vs SVD
6. PCA – Main Applications
7. PCA – Image Compression
8. PCA Data Preprocessing
9. PCA – Biplot and the Screen Plot
10. PCA – Feature Scaling and Screen Plot
11. PCA – Supervised vs Unsupervised
12. PCA – Visualization
1. Importing Data
2. Jupyter Notebook
3. Matplotlib
4. NumPy Basics
5. Pandas
6. Python Basics
7. Scikit-Learn
8. Seaborn
9. Supervised Learning
10. Unsupervised Learning
11. Python For Data Science Cheat Sheet
1. 600 Data Science Questions & Answers
1. Creating A Data Science Resume
2. Data Science Cover Letter
3. How to Contact Recruiters
4. Getting Started with Freelancing
5. Top Freelance Websites
6. Personal Branding
7. Networking Do’s and Don’ts
8. Importance of a Website
1. Cold Email Template & Examples
2. Cover Letter Template & Samples
3. Networking Guide
4. Recruiter Reach Out Template and Examples
5. Resume Checklist

About this course

£54.99
159 lessons
22.5 hours of video content

Did you know?

Over 90% of the world’s data was created in just the last two years alone. By mastering Python for data science, you aren’t just learning a programming language; you are acquiring the superpower to decode the fastest-growing resource on the planet and build predictive models that actively shape the future of global industries.

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FAQ about the Data Science & Machine Learning with Python course

Do I need prior programming or mathematical experience to take this course?

Not at all. This course is specifically designed to be accessible for complete beginners. We guide you from basic computer literacy and fundamental Python programming all the way through to advanced statistical testing and machine learning algorithms.
Why is Python chosen for this Data Science and Machine Learning course over other languages?

Python is the undisputed industry standard for data science. Its clean syntax makes it incredibly easy to learn for beginners, while its ecosystem features powerful libraries like NumPy, Pandas, and scikit-learn that enable scaling into sophisticated deep learning architectures.
What is the difference between Data Science, Machine Learning, and Deep Learning?

Data Science is the overarching umbrella that covers data cleaning, analytics, and visualisation. Machine Learning is a subset of data science that uses algorithms to allow computers to learn from data to make predictions. Deep Learning is an even deeper subfield of machine learning that utilises multi-layered neural networks to mimic human decision-making. This course thoroughly clarifies these boundaries and trains you across both Data Science and Machine Learning disciplines.

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