2 Months
Duration
Physical/Online
Classes
Weekday/Weekend
Mon, Wed & Fri (Saturdays)
10am - 1pm (WAT)
Time
Generative AI Training Outcome
Generative AI architectures
Understand the theoretical foundations of various generative AI architectures
Text, image, audio and code
Implement and train generative models for text, image, audio, and code generation
Fine-tune pre-trained models
Fine-tune pre-trained models for specific applications and domains
Prompt engineering
Design effective prompts and instructions for large language models
Understand AI ethics
Apply ethical considerations and responsible AI practices
Build portfolio
Build a portfolio of generative AI projects demonstrating technical proficiency
** The projects can be changed anytime
Training Benefits
Here are some of the benefits of learning at Tech365
- Beginner-friendly
- Project-based training
- Excellent support
- Job alerts
- Career guidance and CV review
- Certificate of completion
- Flexible payment plan
- Conducive learning environment
- Unlimited internet access
- Remote Internship with top firms
Generative AI Outline
Understand the theoretical foundations of various generative AI architectures. Implement and train generative models for text, image, audio, and code generation
Module 1: Introduction to Generative AI
Fundamentals of AI and Machine Learning
- History and evolution of AI
- Types of machine learning (supervised, unsupervised, reinforcement)
- Introduction to neural networks
Generative AI Landscape
- What makes AI “generative”
- Overview of generative model types
- Key applications and use cases
- Ethical considerations and responsible AI
Setting up development environments
- Python fundamentals for AI
- Introduction to Jupyter notebooks
- Setting up cloud-based GPU environments
Module 2: Deep Learning Essentials
Neural Network Architecture
- Layers, activations, and backpropagation
- Optimization algorithms
- Loss functions
Frameworks and Tools
- PyTorch fundamentals
- TensorFlow/Keras overview
- Data preparation pipelines
Building your first neural network
- Image classification model
- Training and evaluation metrics
Module 3: Core Generative Models
Autoencoders
- Architecture and working principles
- Variational Autoencoders (VAEs)
- Applications and limitations
Generative Adversarial Networks (GANs)
- Generator and discriminator architecture
- Training dynamics and challenges
- Types of GANs and their applications
Implementing a simple GAN
- MNIST digit generation
- Training stabilization techniques
Module 4: Natural Language Processing Foundations
Text Representation
- Tokenization methods
- Word embeddings (Word2Vec, GloVe)
- Contextual embeddings
Language Models
- N-gram models
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) networks
Project
- Build a text generation model using RNNs/LSTMs
Module 5: Transformer Architecture
Attention Mechanisms
- Self-attention and multi-head attention
- Positional encoding
- The Transformer model architecture
Transformer-based Models
- BERT and GPT architecture overview
- Encoder vs. Decoder models
- Bi-directional vs. auto-regressive models
Fine-tuning a pre-trained transformer
- Using Hugging Face’s
- Transformers library
Transfer learning for text classification
Module 6: Diffusion Models
Foundations of Diffusion Models
- Forward and reverse diffusion processes
- Noise scheduling and sampling strategies
- Comparison with GANs and VAEs
Applications in Image Generation
- Unconditional generation
- Text-to-image conditioning
Image inpainting and editing
Implementing a small diffusion model
- Training on a simple dataset
- Image generation and evaluation
Module 7: Multimodal Generative AI
Text-to-Image Models
- CLIP embeddings and contrastive learning
- Stable Diffusion architecture
- Prompt engineering techniques
Text-to-Audio and Text-to-Video
- Audio generation architectures
- Video diffusion models
- Challenges in temporal generation
Working with multimodal models
- Generating images from text descriptions
- Modifying generation with prompts
Module 8: Large Language Models Deep Dive
LLM Architecture and Training
- Scaling laws and emergent abilities
- Pre-training and instruction tuning
- RLHF and alignment techniques
Fine-tuning and Adaptation
- Parameter-efficient fine-tuning (LoRA, QLoRA)
- Instruction fine-tuning
- Quantization techniques
Fine-tuning an open-source LLM
- Dataset preparation
- Training and evaluation
Module 9: AI Agents and Retrieval-Augmented Generation
LLMs as Agents
- Tool use and function calling
- Planning and reasoning frameworks
- Multi-agent systems
Retrieval-Augmented Generation (RAG)
- Vector databases and embeddings
- Retrieval strategies
- Generation with retrieved context
Building a RAG system
- Vector embedding of a document corpus
- Implementing retrieval and generation
Module 10: Code Generation Models
- Code representation techniques
- Architectures for code generation
- Evaluation metrics for code models
Applications of Code Models
- Code completion and synthesis
- Code explanation and documentation
- Bug detection and fixing
Building a code assistant
- Fine-tuning a model for specific programming tasks
- Evaluation and testing
Module 11: Reinforcement Learning for Generative AI
- Policy optimization
- Reinforcement learning from human feedback (RLHF)
- Constitutional AI and alignment
Student Feedback
Training Fees
Artificial Intelligence
Covers all the Data Analytics, machine learning and Artificial intelligence. 5 months
N1,000,000
$1,000
Frequently Asked Questions (FAQs)
As more companies are embracing artificial intelligence, the need for generative AI is on the increase. The demand is growing rapidly across industries like finance, healthcare, e-commerce, marketing, and more as companies are increasingly relying on AI to automate and increate productivity.
No prior programming experience is required. Yes. Though our training approach is beginner friendly, and we work on several real world projects to help our student have deeper understanding of each module. However, you need to know how to have data analytics or data science background to attend this training as they will give you the solid foundation to understand the concepts better.
We have weekend class for those who cannot attend the weekday class. 10am to 1pm (Nigerian Time)
You can pay N500,000 at the beginning of each month to attend the training.
Yes, it is recommended you come with your own laptop as it will help you to practice whatever you are learning. However, we provide laptop that can be used only within our premises if need be.
Depending on your budget, Core i7, 16 GB ram with SSD is best.
However, core i3 or i5 with 8gb ram HDD will also work fine.
It can be HP, DELL, Mac or any other brand.
Yes. In fact, this is our strength. We are committed to supporting our students to ensure they succeed. You will be added to our WhatsApp group to connect with others and also ask questions.
We don’t guarantee job after the training. However, some of our students are connected to remote internships with companies like Accenture, PwC, KPMG etc. We share job alerts from time to time on available vacancies and tips that can help our students get job faster. We also offer CV review, LinkedIn profile optimization and letter of recommendation to diligent students.
All our training are physical at our Ikeja office. However, those who are outside Nigeria or living far can join online. People join our classes from Canada, The UK, USA, UAE, Finland etc.
Yes. Tech365 was established to bridge the practical knowledge that people are lacking to secure a job. Hence, we focus on making the class beginner friendly with lots of projects to help the participants master the skill.
We issue a certificate of completion after the training. If you are interested in certification, you can pay to take certification exam by international bodies online.
You can chat with our representation using the WhatsApp chat button on our website or call the phone number at the top or bottom of this page. You can also click the register button on this page to show your interest and one of our team will reach out to you.
We don’t compete based on price. Our fees is based on the value we offer. If you want a place that will give you in-depth knowledge and value for your money, you are in the right place. Our past students are glad to learn from us as our training is hands-on and designed to make you job-ready. You can check what over 80 of our students had to say about our training on our Google review page.
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