Data and AI

Python for Machine Learning

Build the numerical Python foundation (NumPy, data structures, math intuition) real machine learning work depends on

24h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You can work confidently with NumPy arrays and vectorised operations, understand the core linear algebra and probability concepts machine learning genuinely depends on, and read ML code and documentation without being blocked by unfamiliar numerical Python patterns.

Job titles this qualifies you for

Machine Learning EngineerAI EngineerLLM 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
NumPy Arrays and Vectorised Operations
45 min
2
Linear Algebra Intuition for Machine Learning
40 min
Stage 2 — Applied
1
Probability and Statistics for ML Intuition
35 min
2
Reading and Navigating ML Code and Documentation
30 min
Tier 2 Proof Submission

Python for Machine Learning Portfolio

Demonstrate genuine NumPy fluency, linear algebra and probability intuition, and ML code navigation skill.

1.A NumPy performance and broadcasting-compatibility explanation
2.A dot product and matrix multiplication shape-requirement explanation
3.An overfitting-versus-underfitting explanation with a train/test split justification
4.A code-reading approach and a shape-error debugging first step
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