Machine Learning Specialization – Dataset Nexus Tech

Machine Learning Specialization

Machine learning is transforming Nigeria from fraud detection at Paystack to credit scoring at Carbon to recommendation systems at Jumia. But while everyone discusses AI hype, companies desperately need engineers who can actually build, deploy, and maintain ML systems in production. In 16 weeks, become the rare professional who doesn’t just understand algorithms, you ship models that solve real business problems.

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₦500,000

Also available: 4 monthly payments of ₦130,000

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Choose Your Cohort

Select the learning schedule that best fits your availability. All cohorts receive the same industry-leading curriculum and career support.

July Cohort

Scientific Software Engineering • SSE-C2

⭐ 4.9 • 1,247 enrolled

Apply your scientific training to software development, your physics, chemistry, or biology background becomes your competitive advantage

24 Weeks Starts 27-Jul-2026

₦500,000

Enrollement

ⓘ Early bird pricing ends soon

January Cohort (2027)

Scientific Software Engineering • SSE-C3

⭐ 4.9 • 1,247 enrolled

Apply your scientific training to software development, your physics, chemistry, or biology background becomes your competitive advantage

24 Weeks Starts 18-Jan-2027

₦500,000

Enrollement

ⓘ Early bird pricing ends soon

Who This Bootcamp Is For

This program is designed for driven individuals ready to invest in their future

Requirements Checklist

Basic computer literacy (can use Excel, browse the web)

Access to a laptop and stable internet connection

20-25 hours per week to dedicate to learning

Growth mindset and willingness to be challenged

No prior programming experience required

Requirements Checklist

You're looking for 'get rich quick' shortcuts

You can't commit 20+ hours weekly for 16 weeks

You're not willing to struggle through difficult problems

You expect to learn without putting in the work

You're not ready to invest in your own growth

16-Week Curriculum Overview

In 16 weeks, you’ll master the intersection of Machine learning the skills that make you irreplaceable.

Module 1: Advanced Supervised Learning

Ensemble methods deep-dive: Random Forests, Gradient Boosting, XGBoost, LightGBM

Hyperparameter tuning at scale: Grid search, random search, Bayesian optimization

Feature engineering for production: handling missing data, encoding strategies, feature scaling

Real-world application: Build a credit scoring model for Nigerian microfinance using 50,000+ loan records with XGBoost, achieving 85%+ accuracy

Module 2: Deep Learning Fundamentals with TensorFlow/Keras

Neural network architectures: feedforward, activation functions, backpropagation

Preventing overfitting: dropout, regularization, early stopping, batch normalization

Transfer learning and pre-trained models

Training optimization: learning rates, optimizers (Adam, SGD), batch size selection

Real-world application: Build image classification model for Nigerian product categorization using transfer learning from ResNet

Module 3: Computer Vision

Convolutional Neural Networks (CNNs) architecture

Image preprocessing and augmentation

Object detection fundamentals

Working with image datasets at scale

Real-world application: Build a Nigerian ID card verification system detecting fake documents, like systems used by fintech companies

Module 4: Natural Language Processing

Text preprocessing for Nigerian English and pidgin

Word embeddings: Word2Vec, GloVe, fastText

Transformer architecture introduction

Sentiment analysis and text classification

Real-world application: Build customer support ticket classification system for Nigerian e-commerce platform, automatically routing complaints

Module 5: ML System Design & Architecture

Designing ML systems for production

Batch vs. real-time inference

Model serving patterns and trade-offs

Scalability considerations

Real-world application: Design end-to-end fraud detection system architecture for Nigerian payment processor

Module 6: Model Deployment & Serving

RESTful API development with Flask/FastAPI

Model serialization: pickle, joblib, ONNX

Docker containerization for ML models

Cloud deployment: AWS SageMaker, Google Cloud AI Platform

Load balancing and horizontal scaling

Real-world application: Deploy your credit scoring model as production API serving 1,000+ predictions per hour

Module 7: MLOps & ML Pipelines

ML pipeline orchestration with Airflow

Automated model training and retraining

Continuous Integration/Continuous Deployment for ML (CI/CD)

Version control for models and data (DVC, MLflow)

Experiment tracking and reproducibility

Real-world application: Build automated ML pipeline that retrains fraud detection model weekly with new data

Module 8: Model Monitoring & Maintenance

Monitoring model performance in production

Detecting data drift and concept drift

A/B testing for model comparison

Model debugging and troubleshooting

Alerting and incident response for ML systems

Real-world application: Implement comprehensive monitoring for your deployed model, tracking accuracy degradation and data distribution shifts

Module 9: Feature Stores & Data Pipelines for ML

Building feature engineering pipelines

Feature stores for production ML (Feast)

Managing training vs. serving skew

Online vs. offline feature computation

Real-world application: Build feature store serving consistent features to training and inference for recommendation system

Module 10: Recommendation Systems

Collaborative filtering: user-based and item-based

Matrix factorization techniques

Deep learning for recommendations

Handling cold start problem

Evaluation metrics for recommenders

Real-world application: Build product recommendation engine for Nigerian e-commerce platform like Jumia or Konga

Module 11: Time Series Forecasting & Anomaly Detection

Time series fundamentals: stationarity, seasonality, trends

Classical methods: ARIMA, SARIMA, Prophet

Deep learning for time series: LSTMs, Transformers

Anomaly detection techniques

Real-world application: Build sales forecasting model for Nigerian retail chain and anomaly detection for transaction fraud

Module 12: Advanced NLP & Transformers

BERT, GPT architecture deep-dive

Fine-tuning pre-trained language models

Working with Nigerian languages and code-switching

Named Entity Recognition (NER) for Nigerian context

Real-world application: Fine-tune BERT for Nigerian news classification and sentiment analysis on Naija Twitter

Module 13: Reinforcement Learning Fundamentals

Markov Decision Processes (MDPs)

Q-learning and Deep Q-Networks (DQN)

Policy gradient methods

Applications in recommendations and decision systems

Real-world application: Build dynamic pricing system for ride-hailing app using RL principles

Module 14: ML for Edge & Mobile Devices

Model compression and quantization

TensorFlow Lite and mobile deployment

Optimization for low-resource environments

Real-world application: Deploy lightweight ML model for Nigerian mobile app with limited device capabilities

Module 15: Advanced Topics & Industry Best Practices

Ethical AI and bias in ML models

Fairness, accountability, and transparency

Handling imbalanced datasets

Active learning and human-in-the-loop systems

Cost optimization for ML infrastructure

Module 16: Capstone Project & Technical Interviewing

Build complete end-to-end ML system

System design interviews for ML roles

ML coding challenges and case studies

Portfolio development for ML engineers

Negotiating ML engineer compensation (₦800k-₦1.5M range)

Key Technologies

Python

SQL

TensorFlow

Scikit-learn

Git

AWS

Learn from Industry Experts

Get trained by professionals who’ve built real data science solutions for top
African companies

Former Senior Data Scientist at Flutterwave with 8+ years experience. Built ML systems processing millions of transactions daily. Passionate about training the next generation of African data scientists.

Tech recruiter turned career coach with 200+ successful placements in Nigerian tech companies. Former talent lead at Andela. Expert in resume optimization and interview preparation.

Career Outcomes

Our graduates don’t just learn data science, they launch careers at
top companies.

Key Technologies

Success Stories

I went from unemployed graduate to Data Analyst at Flutterwave in 4 months. The hands-on projects gave me real portfolio pieces that impressed every interviewer.

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Your Next Career Starts 5 October 2026. Do Not Miss It.

Second cohort now open — 26 spots remaining at this price. Secure yours before enrollment closes.

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₦500,000

Also available: 4 monthly payments of ₦130,000

₦350k–₦800k

5 Real Projects

Alumni Network

Career Support

Choose Your Cohort

Who This Bootcamp Is For

Recent Graduates

Underemployed Professionals

Career Switchers

Parents & Sponsors

Requirements Checklist

Basic computer literacy (can use Excel, browse the web)

Access to a laptop and stable internet connection

20-25 hours per week to dedicate to learning

Growth mindset and willingness to be challenged

No prior programming experience required

Requirements Checklist

You're looking for 'get rich quick' shortcuts

You can't commit 20+ hours weekly for 16 weeks

You're not willing to struggle through difficult problems

You expect to learn without putting in the work

You're not ready to invest in your own growth

16-Week Curriculum Overview

Phase 1

Module 1: Advanced Supervised Learning

Ensemble methods deep-dive: Random Forests, Gradient Boosting, XGBoost, LightGBM

Hyperparameter tuning at scale: Grid search, random search, Bayesian optimization

Feature engineering for production: handling missing data, encoding strategies, feature scaling

Real-world application: Build a credit scoring model for Nigerian microfinance using 50,000+ loan records with XGBoost, achieving 85%+ accuracy

Module 2: Deep Learning Fundamentals with TensorFlow/Keras

Neural network architectures: feedforward, activation functions, backpropagation

Preventing overfitting: dropout, regularization, early stopping, batch normalization

Transfer learning and pre-trained models

Training optimization: learning rates, optimizers (Adam, SGD), batch size selection

Real-world application: Build image classification model for Nigerian product categorization using transfer learning from ResNet

Module 3: Computer Vision

Convolutional Neural Networks (CNNs) architecture

Image preprocessing and augmentation

Object detection fundamentals

Working with image datasets at scale

Real-world application: Build a Nigerian ID card verification system detecting fake documents, like systems used by fintech companies

Module 4: Natural Language Processing

Text preprocessing for Nigerian English and pidgin

Word embeddings: Word2Vec, GloVe, fastText

Transformer architecture introduction

Sentiment analysis and text classification

Real-world application: Build customer support ticket classification system for Nigerian e-commerce platform, automatically routing complaints

Phase 2

Module 5: ML System Design & Architecture

Designing ML systems for production

Batch vs. real-time inference

Model serving patterns and trade-offs

Scalability considerations

Real-world application: Design end-to-end fraud detection system architecture for Nigerian payment processor

Module 6: Model Deployment & Serving

RESTful API development with Flask/FastAPI

Model serialization: pickle, joblib, ONNX

Docker containerization for ML models

Cloud deployment: AWS SageMaker, Google Cloud AI Platform

Load balancing and horizontal scaling

Real-world application: Deploy your credit scoring model as production API serving 1,000+ predictions per hour

Module 7: MLOps & ML Pipelines

ML pipeline orchestration with Airflow

Automated model training and retraining

Continuous Integration/Continuous Deployment for ML (CI/CD)

Version control for models and data (DVC, MLflow)

Experiment tracking and reproducibility

Real-world application: Build automated ML pipeline that retrains fraud detection model weekly with new data

Module 8: Model Monitoring & Maintenance

Monitoring model performance in production

Detecting data drift and concept drift

A/B testing for model comparison

Model debugging and troubleshooting

Alerting and incident response for ML systems

Real-world application: Implement comprehensive monitoring for your deployed model, tracking accuracy degradation and data distribution shifts

Module 9: Feature Stores & Data Pipelines for ML

Building feature engineering pipelines

Feature stores for production ML (Feast)