- Course Overview
What this course is about
From data to model. From model to insight.
This course takes you from Python basics to working machine learning modelsthrough supervised learning, classification, regression, and clustering across six real-world project builds that develop the portfolio your first ML role requires.
You won't just study algorithms. You'll apply them to real datasets, evaluate their performance, and present results with the analytical rigour that data science teams respect.
At ITLearnner, we focus on clarity, structure, and confidence. Every session builds the machine learning capabilityand the portfolio evidence to prove it.
Learning Objective :
By the end of this course, you will be able to
By the end, you can.
- Prepare and clean real-world datasets using pandas and NumPy
- Train and evaluate supervised learning models with scikit-learn
- Apply classification and regression algorithms to real business problems
- Visualise data and model results using matplotlib and seaborn
- Present a complete ML project portfolio at interview
- Who this course is for:
Target Audience
Who this course is for
Target audience
For career starters ready to stop being curious about AI and start building machine learning models with real datasets.
- Career starters and graduates targeting data science or junior ML roles who want a project portfolio not just course completion certificates.
- Python developers who want to add machine learning to their skillset classification, regression, and clustering applied to real datasets they can present.
- Professionals curious about AI who want to move from awareness to applied capability and open roles that curiosity alone cannot unlock.
If you see yourself in one of these, this course is built for you.
What you need
Pre-Requisites
What you need.
- Python fundamentals variables, functions, loops, and basic OOP
- No prior machine learning experience required
- A laptop with Python, Jupyter Notebook, and internet access
AI and Machine Learning Fundamentals
24 Hrs
Duration
1.5 hrs
Sessions
Online, Live
Delivery
Curriculum
01
The Machine Learning Landscape
Learn the ML landscape supervised, unsupervised, and reinforcement learning categories and the real problem types each addresses and apply that understanding to map a set of business problems to the correct ML approach.
02
Python for ML
Learn Python’s ML data toolkit NumPy array operations, pandas data manipulation, and matplotlib visualisation and apply them to load, explore, and summarise a real dataset you’ll use throughout the course.
03
Data Preparation
Learn data preparation fundamentals handling missing values, encoding categorical variables, and applying feature scaling and apply them to transform raw real-world data into a format your models can learn from.
04
Classification
Learn classification algorithms decision trees, random forests, and logistic regression and apply them to train, predict, and compare classifiers on a real labelled dataset.
05
Regression
Learn regression modelling linear regression, polynomial regression, and model evaluation with RMSE and R² and apply them to predict continuous outcomes from a real dataset with real business meaning.
06
Model Evaluation
Learn the full model evaluation toolkit accuracy, precision, recall, F1, confusion matrix, and cross-validation and apply them to evaluate your models like someone who understands what the numbers actually mean.
07
Clustering and Unsupervised Learning
Learn unsupervised learning K-means clustering, hierarchical clustering, and PCA dimensionality reduction and apply them to discover structure in unlabelled data and visualise the patterns found.
08
Final ML Project
Learn how to structure, execute, and present a complete end-to-end ML project from raw data through feature engineering, modelling, and evaluation and apply all course learning to a capstone presented to the cohort.
Frequently asked questions
1
Who is this course designed for?
This course is designed for career starters and graduates targeting data science or ML roles, Python developers wanting to add machine learning to their skillset, and professionals who want to move from AI curiosity to real capability.
2
What prior experience do I need?
Python fundamentals variables, functions, loops, and basic OOP. No prior machine learning experience is required.
3
What will I be able to do by the end?
You will be able to prepare and clean real-world datasets with pandas, train and evaluate classification and regression models with scikit-learn, visualise results, apply clustering and unsupervised learning, and present a complete end-to-end ML project portfolio.
4
How are the sessions structured?
Eight modules, 1.5 hours per session, 16 sessions over eight weeks. Six complete ML projects are produced across the course.
5
What is EngagePro?
After each session you receive algorithm summaries, pandas cheat sheets, and evaluation metric guides. Each task involves applying the module's algorithm to a real-world dataset. Dr Amara Osei reviews every submission with a model quality assessment and data pipeline feedback.
6
What tools do I need?
A laptop with Python, Jupyter Notebook, scikit-learn, pandas, and matplotlib installed. All are free.
7
Will I receive a certificate?
Yes the ITLearnner AI and Machine Learning Fundamentals Certificate on completing the course and project portfolio.
Learning Approaches
We recognize that everyone learns differently, so we offer flexible learning formats to fit your needs:
One-on-One Training
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Personalized, instructor-led coaching tailored to your learning speed.
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Best for career-specific coaching or specialized training needs.
Small Batch Classes (2-5 learners)
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Interactive, discussion-based learning in small groups.
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Encourages collaboration, teamwork, and peer-to-peer engagement.
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