bolt Elite Certification

AI and Machine Learning

Become a job-ready AI and Generative AI developer. Learn machine learning, NLP, and LLMs, then deploy real-world intelligent systems.

Duration 3 Months (12 Weeks)

Contact Hours 144 Hrs

Level Intermediate

Format Full time

Enroll in Program Browse Courses

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Course Introduction

This advanced course trains learners to become proficient AI and Generative AI developers through in-depth coverage of machine learning, natural language processing, and foundation models. It combines supervised and unsupervised learning with modern tools such as OpenAI GPT, Meta LLaMA, xAI’s Grok, and Hugging Face Transformers. Learners graduate prepared to deploy intelligent systems in real-world settings.

Learning Outcomes

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Develop, evaluate, and deploy AI and machine learning systems

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Manipulate and visualize data using NumPy, Pandas, and EDA techniques

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Implement supervised and unsupervised learning models

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Build deep learning systems using CNNs and computer vision

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Develop NLP systems using Transformers and Large Language Models (LLMs)

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Architect Generative AI solutions with RAG and vector databases

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Deploy production-grade AI via APIs (FastAPI/Flask) and Docker

Target Audience

group ICT graduates with programming and statistical foundations
group Aspiring AI engineers, ML specialists, NLP developers, and GenAI builders
group Freelancers seeking to offer AI-driven automation services
group Early-career professionals targeting roles in GenAI, NLP, or applied ML

Enroll in Program

3 Months (12 Weeks) Duration.

12-Week Learning Roadmap

From Python foundations to deployed GenAI systems

AI and Python Foundations

AI evolution and ML lifecycle, Data manipulation (NumPy/Pandas) and EDA

Mathematical Foundations & Supervised Learning

Statistics, linear algebra, and probability, Regression, classification, and gradient descent

Model Evaluation & Unsupervised Learning

Metrics, cross-validation, and hyperparameter tuning, Clustering and dimensionality reduction

Neural Networks & Computer Vision

Deep learning fundamentals and MLPs, CNNs, transfer learning, and object detection

NLP Foundations

Classical pipelines and modern tokenization, Multilingual processing and transformer baselines

Transformers & Large Language Models

Self-attention mechanisms and LLM evaluation, Fine-tuning vs. prompting vs. RAG

Generative AI Systems

Vector databases and embedding strategies, Multi-agent systems and hallucination analysis

Deployment & AI Ethics

FastAPI/Flask, Docker, and model versioning, AI safety, bias, and guardrails

Industry-Aligned Capstone Project (Weeks 9–11)

Problem scoping and data engineering, Model development and deep error analysis, System optimization, latency scaling, and documentation

Career Support & Freelancing Growth (Week 12)

LinkedIn optimization and portfolio development, Fiverr/Upwork strategies and mock interviews, Final live project showcase and graduation

Tools & Platforms

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Python, Jupyter, Google Colab

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Scikit-learn, TensorFlow, PyTorch

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NLTK, spaCy, Hugging Face, OpenAI GPT, LLaMA, Claude, Grok

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LangChain, LlamaIndex, Pinecone, Gradio

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Flask, FastAPI, Streamlit

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Matplotlib, Seaborn, Plotly

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GitHub, Weights & Biases, Notion

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Hugging Face Spaces, Vercel, Render

Capstone Project

Each learner develops a production-grade AI solution for a real industry problem, from data collection through to a live demonstration.

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Data Phase: gather realistic, noisy datasets and define success metrics

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Development Phase: build baseline and advanced models, then optimize

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Deployment Phase: address cost and latency, and prepare for deployment

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Final Presentation: live demonstration of the complete solution

Ready to lead the Digital Revolution?

Join the next cohort and master the technologies shaping the future of global enterprise.

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