Course Overview
Machine Learning (ML) is a key domain of both artificial intelligence and computer science that aims at developing programs which learn from past experiences without any explicit programming.
How It Works
- Data Collection and Data Pre-processing: Acquisition of raw data (numbers, words, pictures, sounds), as well as cleaning and preprocessing in order to remove errors and biases..
- Learning Algorithm: Feeding data into algorithms for discovering hidden patterns and updating internal weights of the machine.
- Prediction & Evaluation: Evaluating the learned algorithm with new data in order to measure its accuracy and generalize the outcome.
Main Categories of ML (Machine Learning)
- Supervised Learning: Learns from examples and tries to predict outcomes or classify objects (regression and classification problems)..
- Unsupervised Learning: Detects hidden patterns and structures in unlabeled data (clustering problem).
- Reinforcement Learning: Is aimed at finding optimal solutions through trial-and-error approach.
Who This Course Is For:
Open to school students, college undergraduates, and career switchers. No prior coding required.
What You Will Learn
Course Key Information
2 Hours
Beginner
Open to school students, college undergraduates, and career switchers. No prior coding required.
Technologies Covered
Tools & Platforms Used:
Course Outcomes
- Core Math and Statistics - Linear Algebra, Calculus and Probability
- Programming and Data Tools - Python, NumPy and Pandas, Data Visualization
- Types of Machine Learning - Supervised Learning, Unsupervised Learning and Reinforcement Learning
- Model Evaluation and Tuning - Metrics and Optimisation
What You Will Learn
Technologies Covered
Tools & Platforms Used:
Lesson Plans & Curriculum
All chapters and lessons are publicly accessible.
Chapter 1 — Introduction to Machine Learning
5 Lessons • Introduction to Machine Learning and its applications
Chapter 2 — Types of Machine Learning
5 Lessons • Supervised, unsupervised and reinforcement learning
Chapter 3 — Python for Machine Learning
5 Lessons • Python programming concepts used in Machine Learning
Chapter 4 — Data Preprocessing
5 Lessons • Preparing and transforming data for machine learning
Chapter 5 — Regression Algorithms
5 Lessons • Understanding regression algorithms and prediction
Chapter 6 — Classification Algorithms
5 Lessons • Classification techniques and machine learning models
Chapter 7 — Clustering Algorithms
5 Lessons • Introduction to clustering and unsupervised learning
Chapter 8 — Model Evaluation
5 Lessons • Evaluating machine learning models and performance
Chapter 9 — Feature Engineering and Model Improvement
5 Lessons • Feature engineering and techniques for improving models
Chapter 10 — Machine Learning Projects
5 Lessons • Practical Machine Learning projects
Complete Your Project & Get Certified
Complete the final project, upload your work, apply for evaluation, and receive your certificate after approval.
1. Upload Your Project
Submit your completed project.
2. Apply
Apply for evaluation and certification.
3. Get Certificate
Receive your certificate after approval.
Course Completion Certificate
Shareable digital credential upon course completion
Complete the course requirements to receive your course completion certificate.
Frequently Asked Questions
What prior math or coding knowledge do I need?
Evidence: Most beginner programs require high-school level algebra, basic probability, and fundamental Python programming (loops, functions, and arrays). Advanced linear algebra and calculus are typically taught or reviewed within the curriculum
Is this course beginner-friendly?
Introductory specializations (such as those on Coursera's Machine Learning Courses) start from the ground up, assuming zero prior machine learning experience, though basic programming familiarity is expected
Are the classes live or self-paced?
Most online ML programs are entirely self-paced with pre-recorded video lectures, allowing you to study on your own schedule, supplemented by optional live Q&A sessions or discussion forums
How many hours per week should I dedicate?
Expect to spend 5 to 10 hours per week to comfortably complete the video modules, programming assignments, and quizzes over a span of several weeks.
Will I receive a certificate upon completion?
Yes, verified digital certificates are usually awarded after passing all graded assignments, coding projects, and final evaluations
What software or hardware do I need?
You only need a standard computer (Windows, Mac, or Linux) with a stable internet connection. Most coding labs run via cloud-based environments like Google Colab or Jupyter Notebooks, meaning a high-end GPU machine is rarely required for introductory coursework.
What programming language or framework in ML
PyTorch (Python), TensorFlow & Keras (Python), Scikit-learn, JAX (Python), Hugging Face Transformers (Python)
Start for Free
Access this course for free.
What's Included in this Course:
- 10 Course Chapters
- 50 Lessons
- Interactive Lesson Exercises & Notes
- Official Verified Digital Certificate
- Lifetime Access to All Course Materials