01
Type of Machine Learning
Understand supervised, unsupervised and reinforcement learning
Courses Introduction to Machine Learning
Course Course
Humain Academy
Introduction to Machine Learning
Go from basic Python knowledge to building real machine learning models using real-world data in 6 weeks.
- Duration
- 45 total learning hours over 6 weeks
- Level
- Intermediate, A basic familiarity with Python programming is strongly recommended
- Format
- 12 live online lectures + Q&A clinics + self-study + assessments
- Delivery
- Online only, live video conferencing
About the Course
Introduction to Machine Learning
Take your first step into the world of artificial intelligence with this practical introduction to Machine Learning.
In this course, you’ll learn how machines learn from data and how to build your own predictive models using real-world datasets. Covering both theory and hands-on implementation, the course walks you through the full machine learning workflow, from data preparation to model evaluation.
This is the ideal next step for learners who already have basic Python knowledge and want to move into data science and AI.
Prerequisites
Who is this course for?
Open to all individuals aged 16+ with a basic familiarity with Python programming. No prior Machine Learning or data science experience is required.
Students & Graduates
Learners who have completed a Python basics course
Professionals Upskilling
Professionals looking to expand into machine learning
Entrepreneurs & Founders
Individuals interested in data science or AI or anyone curious about how intelligent systems are built
Learning Outcomes
Skills you'll demonstrate
02
ML Workflow
Learn the full process from data preparation to model deployment
03
Data Cleaning
Handle missing values and prepare datasets for analysis
04
Feature Engineering
Transform raw data into meaningful inputs for models
05
Regression Models
Build models for predicting continuous outcomes
06
Classification Models
Develop models for categorising data into classes
07
Clustering Methods
Group data using unsupervised learning techniques
08
Dimensionality Reduction
Simplify datasets while preserving important information
09
Performance Metrics
Measure and interpret model accuracy and effectiveness
10
Overfitting & Validation
Identify overfitting and apply cross-validation techniques
11
Neural Network Basics
Understand how neural networks are structured and trained
12
Deep Learning Introduction
Explore the foundations of modern AI systems
13
Python ML Tools
Use pandas and scikit-learn to build machine learning pipelines
14
End-to-End Project
Develop and evaluate a complete machine learning solution on real data
Curriculum Modules
1 Session 1 What is ML? Supervised, unsupervised & reinforcement learning
Understand what machine learning is, how it works, and explore the key types of learning used in real-world applications.
2 Session 2 The ML workflow: data, training, evaluation & deployment
Learn the end-to-end machine learning process, from collecting data to training models and deploying them in practice.
3 Session 3 Data preparation: cleaning & normalisation (Part 1)
Prepare raw data for analysis by handling missing values, correcting inconsistencies, and standardising datasets.
4 Session 4 Data preparation: feature engineering (Part 2)
Transform and select meaningful features to improve model performance and make data more usable for machine learning.
5 Session 5 Supervised learning: regression algorithms
Build models that predict continuous values and understand how regression techniques are applied in real scenarios.
6 Session 6 Supervised learning: classification algorithms
Learn how to create models that classify data into categories using common classification techniques.
7 Session 7 Unsupervised learning: clustering & dimensionality reduction
Explore how to uncover patterns in data without labels and simplify complex datasets for better analysis.
8 Session 8 Model evaluation: metrics, overfitting & cross-validation
Measure model performance, identify overfitting, and apply techniques to improve reliability and accuracy.
9 Session 9 Introduction to neural networks & deep learning
Discover how neural networks work and get an introduction to the concepts behind modern AI systems.
10 Session 10 ML in practice: scikit-learn & pandas
Use industry-standard Python libraries to build, train, and manage machine learning models efficiently.
11 Session 11 Mini project: building the pipeline (Part 1)
Start building a complete machine learning pipeline, from data preparation to initial model training.
12 Session 12 Mini project: evaluation & review (Part 2)
Finalise your project by evaluating performance, refining your model, and presenting your results.
FAQs
Do I need prior experience in Machine Learning?
No, this course is designed for beginners. While no prior Machine Learning knowledge is required, a basic understanding of Python is recommended to help you follow along more easily.
How practical is this course? Will I build real projects?
Yes, this is a hands-on course. You’ll work on practical exercises throughout and complete a final project where you build and evaluate a full machine learning model using real-world data.
What tools and technologies will I use?
You’ll primarily use Python along with industry-standard libraries such as pandas and scikit-learn to build and evaluate machine learning models.
Will I receive a certificate after completing the course?
Yes. You’ll receive a Certificate of Completion from Humain Academy once you successfully complete at least 70% of the course.
Start Your Machine Learning Journey Today
Enroll now or request information about upcoming sessions.
Introduction to Machine Learning
Request details