A Course You'll Actually Finish
MACHINE LEARNING
A rigorous, hands-on AI and Machine Learning course that equips students with the mathematical foundations, programming skills, and practical expertise to build, train, evaluate, and deploy intelligent systems — covering supervised learning, deep learning, NLP, computer vision, generative AI, and MLOps using Python and industry-standard libraries.
About this Course
Beginner to Pro
96 Chapters
Hands-on Exercises
5 months intensive training
1-2 months project work
7 months of free workspace
Certificate Included
Say goodbye to scattered tutorials and endless distractions. This course offers a clear, structured learning path that seamlessly combines theory with hands-on practice.
About the Course
What You'll Learn
What You'll Build
Curriculum
Target Audience
Prerequisites
Reviews
Perfect fit
About the Course
Highly Practical
What You'll Learn
- Build the mathematical foundations for ML — linear algebra, calculus, probability, and statistics applied to AI
- Master Python for AI — NumPy, Pandas, Matplotlib, and Scikit-learn for data manipulation, visualisation, and model building
- Implement and evaluate core supervised learning algorithms — regression, classification, decision trees, random forests, XGBoost, and SVMs
- Apply unsupervised learning — K-means clustering, hierarchical clustering, PCA, UMAP, and anomaly detection
- Build, train, and optimise deep neural networks with TensorFlow and Keras — understanding forward propagation, backpropagation, and gradient descent
- Design and train CNNs for image classification and computer vision using transfer learning with pre-trained models
- Build sequence models using RNNs, LSTMs, and the Transformer architecture for NLP and time series forecasting
- Fine-tune pre-trained large language models using Hugging Face Transformers and PEFT/LoRA for custom NLP tasks
- Build generative AI applications — RAG systems, AI agents, and LLM pipelines using LangChain and the OpenAI API
- Deploy ML models as production REST APIs using FastAPI and Docker, with experiment tracking via MLflow
Learn by Doing
What You'll Build
Throughout this course, students build a progressive portfolio of AI and ML projects demonstrating increasing sophistication. Projects built during the course include: a house price prediction regression model, a telecom customer churn classification system, a customer segmentation model using K-means clustering, a custom image classifier using CNN transfer learning, a sentiment analysis model for Nigerian social media text, and a retrieval-augmented generation (RAG) document Q&A system. The capstone project is a complete, end-to-end AI application solving a real problem in a Nigerian or African business context. The capstone must include a clearly defined problem and dataset, a full model development pipeline from preprocessing to training and evaluation, a comparison of at least three modelling approaches with documented metrics tracked in MLflow, a fine-tuned or prompt-engineered language model component where applicable, and a deployed FastAPI prediction endpoint containerised with Docker and hosted on a live public URL. Students present their complete AI systems in a structured technical review session, explaining their modelling decisions, evaluation results, limitations, and deployment architecture as a professional AI engineer would in a real workplace.
A Well-Structured Course
Course Content
16 Modules, 96 Chapters
Target Student
Who Is This For?
- Data analytics professionals who want to advance from analytics and statistics into machine learning and deep learning
- Software developers and engineers with Python experience who want to specialise in AI and ML engineering
- Recent graduates from computer science, mathematics, statistics, or engineering targeting AI and data science roles
- Researchers and academics who want practical, implementation-focused AI skills to complement their theoretical background
- Entrepreneurs and startup founders who want to build AI-powered products and need genuine technical depth
- Professionals in finance, healthcare, agriculture, and logistics seeking to apply ML to domain-specific problems
- Students who have completed the Bitnox Data Analytics course and are ready to advance to the frontier of applied AI
- Anyone with strong mathematical curiosity and Python programming foundation passionate about building intelligent systems
A Complete Beginner-Friendly Course
Prerequisites
Success Stories
What Our Students Are Saying
Don't just take our word for it—listen to the stories of students who have enhanced their skills and transformed their careers through this course.
Kick-start Your Tech Career Now
Stop spending time on lengthy, disorganized courses and scattered tutorials. With this course, you'll receive concise, targeted lessons that deliver practical skills efficiently. Enroll now and get started immediately!
MACHINE LEARNING
- 5 months intensive training
- Everything you need to know
- Instructor-led sessions
- Morning class session
- Afternoon class session
- Physical and virtual classes
- Practical, hands-on exercises
- 7 months of free workspace
- 1 - 2 months project work
- Certificate of graduation
Frequently Asked Questions
Bitnox Tech Training is a professional training arm of Bitnox Technology Solutions Ltd., offering career-focused programs in tech and digital skills. We provide both online and physical (instructor-led) training tailored for students, working professionals, and beginners.
To ensure quality and readiness, we review every application to confirm if the student meets the course requirements. This allows us to better support your learning journey.
Yes, if you pay via bank transfer, you'll be required to upload the receipt for verification.