Data and AI

Machine Learning Foundations with scikit-learn

Build, evaluate, and genuinely understand classical machine learning models using scikit-learn's consistent API

24h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You can build, train, and evaluate classification and regression models with scikit-learn, apply proper cross-validation and metric selection, and use feature engineering and preprocessing pipelines that avoid genuine data leakage.

Job titles this qualifies you for

Machine Learning EngineerAI Engineer
Market intelligence
Demand
High
Remote
70% remote roles
Nigeria salary
Not a primary local hiring category
Remote (USD)
$101,500–$155,000/year for the typical remote range (25th-75th percentile); averages reported between $128,769 and $195,475/year depending on source; specialised skills (AI agent architecture, LLM fine-tuning) can increase pay up to 25%

Lessons

Stage 1 — Foundation
1
The scikit-learn API and Core Algorithms
45 min
2
Proper Model Evaluation and Cross-Validation
40 min
Stage 2 — Applied
1
Feature Engineering and Preprocessing Pipelines
40 min
2
Avoiding Common Modelling Pitfalls
30 min
Tier 2 Proof Submission

Machine Learning Foundations Portfolio

Demonstrate genuine algorithm understanding, evaluation rigour, feature engineering discipline, and pitfall-avoidance judgement.

1.A random forest versus decision tree explanation
2.An accuracy-versus-recall scenario for an imbalanced classification problem
3.A data leakage explanation and an encoding choice with reasoning
4.A target leakage example and an imbalanced-data technique
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