Course Curriculum

What is Machine Learning? 00:02:00
Applications of Machine Learning 00:02:00
Machine learning Methods 00:01:00
What is Supervised learning? 00:01:00
What is Unsupervised learning? 00:01:00
Supervised learning vs Unsupervised learning 00:04:00
Introduction 00:01:00
Python Libraries for Machine Learning 00:02:00
Setting up Python 00:02:00
What is Jupyter? 00:02:00
Anaconda Installation Windows Mac and Ubuntu 00:04:00
Implementing Python in Jupyter 00:01:00
Managing Directories in Jupyter Notebook 00:03:00
Introduction to regression 00:02:00
How Does Linear Regression Work? 00:02:00
Line representation 00:01:00
Implementation in Python: Importing libraries & datasets 00:02:00
Implementation in Python: Distribution of the data 00:02:00
Implementation in Python: Creating a linear regression object 00:03:00
Understanding Multiple linear regression 00:02:00
Implementation in Python: Exploring the dataset 00:04:00
Implementation in Python: Encoding Categorical Data 00:05:00
Implementation in Python: Splitting data into Train and Test Sets 00:02:00
Implementation in Python: Training the model on the Training set 00:01:00
Implementation in Python: Predicting the Test Set results 00:03:00
Evaluating the performance of the regression model 00:01:00
Root Mean Squared Error in Python 00:03:00
Introduction to classification 00:01:00
K-Nearest Neighbors algorithm 00:01:00
Example of KNN 00:01:00
K-Nearest Neighbours (KNN) using python 00:01:00
Implementation in Python: Importing required libraries 00:01:00
Implementation in Python: Importing the dataset 00:02:00
Implementation in Python: Splitting data into Train and Test Set 00:03:00
Implementation in Python: Feature Scaling 00:01:00
Implementation in Python: Importing the KNN classifier 00:02:00
Implementation in Python: Results prediction & Confusion matrix 00:02:00
Introduction to decision trees 00:01:00
What is Entropy? 00:01:00
Exploring the dataset 00:01:00
Decision tree structure 00:01:00
Implementation in Python: Importing libraries & datasets 00:01:00
Implementation in Python: Encoding Categorical Data 00:03:00
Implementation in Python: Splitting data into Train and Test Sets 00:01:00
Implementation in Python: Results Prediction & Accuracy 00:03:00
Introduction 00:01:00
Implementation steps 00:01:00
Implementation in Python: Importing libraries & datasets 00:02:00
Implementation in Python: Splitting data into Train and Test Sets 00:01:00
Implementation in Python: Pre-processing 00:02:00
Implementation in Python: Training the model 00:01:00
Implementation in Python: Results prediction & Confusion matrix 00:02:00
Logistic Regression vs Linear Regression 00:02:00
Introduction to clustering 00:01:00
Use cases 00:01:00
K-Means Clustering Algorithm 00:01:00
Elbow method 00:02:00
Steps of the Elbow method 00:01:00
Implementation in python 00:04:00
Hierarchical clustering 00:01:00
Density-based clustering 00:02:00
Implementation of k-means clustering in Python 00:01:00
Importing the dataset 00:03:00
Visualizing the dataset 00:02:00
Defining the classifier 00:02:00
3D Visualization of the clusters 00:03:00
Number of predicted clusters 00:02:00
Introduction 00:01:00
Collaborative Filtering in Recommender Systems 00:01:00
Content-based Recommender System 00:01:00
Implementation in Python: Importing libraries & datasets 00:03:00
Merging datasets into one dataframe 00:01:00
Sorting by title and rating 00:04:00
Histogram showing number of ratings 00:01:00
Frequency distribution 00:01:00
Jointplot of the ratings and number of ratings 00:01:00
Data pre-processing 00:02:00
Sorting the most-rated movies 00:01:00
Grabbing the ratings for two movies 00:01:00
Correlation between the most-rated movies 00:02:00
Sorting the data by correlation 00:01:00
Filtering out movies 00:01:00
Sorting values 00:01:00
Repeating the process for another movie 00:02:00
Conclusion 00:01:00

Course Overview 

Python for Machine Learning: The Complete Beginner’s Course is a comprehensive Python for Machine Learning Course designed to help beginners build a strong foundation in Python programming, machine learning, and data science. Whether you’re looking to start a career in artificial intelligence, data analytics, or software development, this course provides a practical, step-by-step introduction to one of the most valuable skills in modern technology.

You’ll begin by learning the fundamentals of Python for Machine Learning, including Python syntax, variables, functions, and programming basics. As you progress, you’ll work with essential libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn to analyse data, create visualisations, build machine learning models, and evaluate their performance. Through hands-on exercises, you’ll gain practical experience with real-world datasets and core machine learning algorithms.

Whether you’re an aspiring data scientist, machine learning engineer, Python developer, or technology enthusiast, this course equips you with the essential knowledge to build AI-powered solutions and continue your learning journey. No previous programming or machine learning experience is required, just curiosity and a willingness to learn through professional Python for Machine Learning Training.

Learning Outcomes

  • Write basic Python code confidently, even with no prior programming experience
  • Use libraries like NumPy and Pandas to clean, organise, and manage data
  • Create clear visualisations to explore patterns and trends within datasets
  • Explain core machine learning concepts, including how models learn from data
  • Build simple machine learning models using tools like Scikit-learn
  • Evaluate model performance to understand accuracy and improve results
  • Apply Python for machine learning skills confidently to real beginner-level projects

 

Your Benefits by Learning with ‘Cambridge Open Academy’:

  • Accreditation: Showcase your ability with our accredited Python for Machine Learning: The Complete Beginner’s Course to potential employers.
  • Free Certificate: Get a Free Digital Certificate upon successful completion of the Python for Machine Learning: The Complete Beginner’s Course.
  • Flexibility: Learn virtually from anywhere, anytime at your own pace and convenience.
  • Advance Your Career: Upskill to impress your employers and land your dream job or long-awaited promotion.
  • Immediately Applicable Coursework: Keep up with the latest skill trend by putting your skillsets to work.
  • Affordability: Save big with our online Python for Machine Learning: The Complete Beginner’s Course as it not only suits your professional needs but also fits within your budget.
  • Tutor Support: Get tutor support on weekdays, 9-5 am, and our dedicated 24/7 customer support.
  • Lifetime Access: Achieve lifetime access to the top-notch expertly crafted course materials.

Our Specialised Delivery Method:

  • Interactive Learning Materials: The course modules were created using an EdTech industry-recognised tool to keep you engaged at all times. With this tool, you get interactive, engaging and top-notch course content. In our courses, you can take advantage of features like—
    • Drop Down Menu
    • Drag and Drop
    • Flash Card
    • Label Graphic
    • Timeline View
  • Responsiveness: In light of contemporary mobile and point-of-need learning trends, our courses are designed to be intrinsically dynamic and provide you the ultimate eLearning solution. These courses will adapt to any gadget without any extra software.
  • Learner-Friendly Navigation: Our courses are also quite simple to navigate for any learner. These courses are designed with simplicity and a modern flow that appeals to a wide range of learning audiences, regardless of their technical background and gadgets.
  • Elegant Outline: Our courses are visually appealing, where you can —
    • Track your progress on the left-side navigation toolbar.
    • Engage in drag-and-drop sorting activities and use the multiple response question to test your understanding of course topics.

With your newly acquired skills from this course can help you

  • Increase Your Hireability.
  • Make Yourself a Valuable Asset
  • Get Your Long-awaited Promotion.
  • Boost Your Pay-scale
  • Better Your Productivity

Certification:

Once you have successfully completed the Python for Machine Learning: The Complete Beginner’s Course course, you will receive a PDF certificate completely free of cost as a proof of your accomplishment. The hardcopy certificate is also available for the cost of £9.99. UK students are required to pay a £10 as a delivery fee, while international students have to pay £19.99 for the shipment of a hardcopy certificate to their designated address.

Who is this course for?

This Python for Machine Learning: The Complete Beginner’s Course course is developed for people who wish to excel in their professional and personal life. Learn from industry leaders and interact with a global network of experts by enrolling in this Python for Machine Learning: The Complete Beginner’s Course course.

Requirements

Enrolling in our Python for Machine Learning: The Complete Beginner’s Course course does not require any prior knowledge or experience. All that is required is an internet-connected gadget and a passion to learn.

Career Path

  • Junior Machine Learning Engineer

    Builds, trains, and deploys machine learning models using Python libraries such as scikit-learn, TensorFlow, and PyTorch for practical applications.

    Salary: £45,000 – £65,000 per year

  • Junior Data Scientist

    Analyses data, develops predictive models using Python, and extracts insights to support business and research decisions.

    Salary: £38,000 – £60,000 per year

  • Python ML Developer / AI Programmer

    Writes Python code to create machine learning solutions, automate workflows, and integrate AI models into software applications.

    Salary: £40,000 – £70,000 per year

  • Data Analyst (Machine Learning Focus)

    Uses Python for data analysis, visualisation, and introductory machine learning techniques to generate actionable insights and reports.

    Salary: £28,000 – £50,000 per year

  • AI Research Assistant / Graduate ML Engineer

    Supports research teams and companies by prototyping machine learning models and experimenting with AI algorithms in academic or industry settings.

    Salary: £35,000 – £55,000 per year

  • MLOps / Machine Learning Engineer (Entry Level)

    Helps deploy, monitor, and maintain machine learning models in production using Python tools and cloud platforms.

    Salary: £50,000 – £80,000 per year

Certification:​

Once you have successfully completed this course, you can order a certificate as proof of your achievement.

A PDF certificate is available for £5.99. You can also request a hardcopy certificate for £9.99.

Delivery of the hardcopy certificate is free within the United Kingdom. For international learners, an additional delivery charge will apply based on the destination country.

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