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AI and Machine Learning Fundamentals

Real AI starts with real data. Real results start here.

Machine learning fundamentals for career starters — Python, scikit-learn, real datasets, and a complete ML project portfolio.

AI and Machine Learning Fundamentals course — Python and scikit-learn for career starters

01

Course Overview

From data to model. From model to insight.

This course takes you from Python basics to working machine learning models—through 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 capability—and the portfolio evidence to prove it.

AI and Machine Learning Fundamentals

16

Total Sessions

​

Level

1.5 hrs

Per Session

Hands-on Activity

02

Course Objectives:

03

Learning Outcomes:

By the end, you can.
  1. Prepare and clean real-world datasets using pandas and NumPy
  2. Train and evaluate supervised learning models with scikit-learn
  3. Apply classification and regression algorithms to real business problems
  4. Visualise data and model results using matplotlib and seaborn
  5. Present a complete ML project portfolio at interview

05

Target Audience

For career starters entering AI and data science.
  • Career starters and graduates targeting data science or ML roles
  • Python developers wanting to add machine learning to their skillset
  • Professionals curious about AI who want to move from curiosity to capability

06

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

Curriculum

01
The Machine Learning Landscape

Supervised, unsupervised, reinforcement — what ML actually is and what problems it actually solves

02
Python for ML

NumPy, pandas, matplotlib — the data toolkit that every ML engineer uses every day

03
Data Preparation

Clean it, encode it, scale it — the work that makes or breaks every model

04
Classification

Decision trees, random forests, and logistic regression applied to a real classification dataset

05
Regression

Predict continuous values — linear regression, polynomial regression, and model evaluation metrics

06
Model Evaluation

Accuracy, precision, recall, F1, confusion matrices — evaluate like someone who understands the numbers

07
Clustering and Unsupervised Learning

K-means, hierarchical clustering, and PCA — find patterns in data without labels

08
Final ML Project

A complete end-to-end machine learning project from raw data to evaluated model — presented to the cohort

Career Relevance

AI is not a trend. It is an economic shift. The professionals who can build, evaluate, and deploy models are among the highest-valued in technology.

  • Junior Data Scientist — The entry-level role this course directly prepares for
  • ML Engineer — Train and deploy AI that actually works at production scale
  • Data Analyst — ML skills make every analyst dramatically more valuable
  • AI Product Manager — Own AI product decisions with technical credibility
  • Research Scientist — The academic and industry research path beyond this foundation

scikit-learn, pandas, NumPy, classification, regression, clustering, model evaluation, ML portfolio

The ML foundation you build here is the same one that career-defining roles are built on.

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.

Learning Approaches

We recognize that everyone learns differently, so we offer flexible learning formats to fit your needs:

One-on-One Training

  • Personalized, instructor-led coaching tailored to your learning speed.

  • Best for career-specific coaching or specialized training needs.

Small Batch Classes (2-5 learners)

  • Interactive, discussion-based learning in small groups.

  • Encourages collaboration, teamwork, and peer-to-peer engagement.

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