Machine Learning

Introduction & Overview of AI and Machine Learning

The landscape of AI, machine learning, deep learning, and generative AI: history, key concepts, real-world applications across industries (health, agriculture, finance, education), career paths, and responsible AI principles.

Essential Mathematics for Machine Learning

Linear algebra, calculus, probability, and statistics, taught practically so learners understand how ML algorithms work: vectors, matrices, gradients, distributions, and hypothesis testing.

Python Programming

Python fundamentals for AI work: syntax, data structures, functions, object-oriented programming, file handling, virtual environments, Git, and Jupyter/Google Colab.

Data Analytics with Python

Data sourcing, cleaning, wrangling, exploration, and visualization with Pandas, NumPy, Polars, Matplotlib, Seaborn, and Plotly, plus SQL basics for working with real-world datasets.

AI Prompt Engineering & AI Tools for Content Creation

Writing effective prompts and workflows for large language models, and using AI tools to create text, audio, image, and video content, including AI-assisted research, voice and music generation, and AI design and video tools. Covers evaluating output quality, copyright and ethics, and turning these skills into freelancing and business opportunities.

Machine Learning Foundations

Supervised and unsupervised learning with Scikit-learn: regression, classification, decision trees, ensemble methods (Random Forest, XGBoost, LightGBM), clustering, and dimensionality reduction, plus feature engineering, cross-validation, and hyperparameter tuning.

Model Evaluation, Explainability & Responsible AI

Metrics and validation strategies, handling imbalanced data, bias and fairness, model explainability (SHAP, LIME), data privacy, and responsible AI practices expected by modern employers and regulators.

Deep Learning with Neural Networks

Building and training neural networks with PyTorch (and an introduction to TensorFlow/Keras): CNNs, RNNs, optimization, regularization, transfer learning, and GPU training on cloud platforms.

Computer Vision

Image classification, object detection, and segmentation using modern architectures and pretrained models, with applications such as crop disease detection, medical imaging, and document and face analysis.

Natural Language Processing & Transformers

Text processing, embeddings, and transformer architectures, using the Hugging Face ecosystem for text classification, summarization, translation, and sentiment analysis, including applications for African and low-resource languages.

Generative AI & Large Language Models

How LLMs and diffusion models work, fine-tuning techniques (LoRA/PEFT), working with open-source and API-based models, multimodal models, and evaluating generative systems.

RAG, AI Agents & Agentic Workflows

Building retrieval-augmented generation (RAG) systems with vector databases and embeddings, plus AI agents that use tools, APIs, and multi-step reasoning to automate real tasks (using frameworks such as LangChain and LlamaIndex).

Time Series, Recommender Systems & Reinforcement Learning

Forecasting for sales, finance, and demand planning; building recommendation engines; and an introduction to reinforcement learning concepts and applications.

Big Data & Cloud for Machine Learning

Handling large-scale data with Apache Spark and cloud data platforms, and working with cloud ML services: AWS (SageMaker, Bedrock) and Google Cloud (Vertex AI), aligned with the certification tracks.

MLOps & Model Deployment

Taking models to production: experiment tracking (MLflow), Docker, REST APIs (FastAPI), CI/CD pipelines, monitoring, model versioning, and LLMOps for managing generative AI applications in production.

Building AI Applications

Creating user-facing AI products: chatbots, AI assistants, and dashboards using Streamlit, Gradio, and FastAPI, integrating models into web and mobile-friendly apps.

Machine Learning Projects & Capstone

End-to-end, real-world projects (e.g., predictive analytics, computer vision systems, LLM-powered assistants, recommendation systems) from problem definition to deployment, culminating in a capstone project presented to a review panel.

Portfolio & Personal Branding

Building a professional presence: well-documented GitHub repositories, LinkedIn optimization, a personal portfolio website, project case studies, technical writing, CV preparation, and interview and freelancing guidance.

Certification Preparation & Exam Registration Support

Guided preparation, practice exams, and exam strategy for AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer – Associate, and Google Cloud Professional Machine Learning Engineer. BISOFTECH assists with registration for any of these exams (fees paid by the learner) and supports learners until they sit for the exam.

Internship (6 Weeks)

A six-week placement applying machine learning skills to real projects with companies in Cameroon or abroad, gaining workplace experience, professional references, and portfolio-ready work.