One-year Artificial Intelligence and Generative AI training course for graduates and engineers and for working professionals for upskilling.
📘 Course Title:
Artificial Intelligence & Generative AI: From Foundations to Future Technologies
🎯 Course Objectives:
- Build a strong foundation in AI, Machine Learning, and Deep Learning.
- Gain hands-on experience with Generative AI models like GPT, GANs, and diffusion models.
- Develop end-to-end AI applications using industry-standard tools and frameworks.
- Prepare for industry certifications and enhance employability in AI and tech-driven roles.
📅 Duration:
One Year (52 Weeks)
Includes lectures, practical labs, assignments, quizzes, capstone projects, and certification readiness.
📆 Weekly Breakdown
Semester 1: Foundations of AI and ML (Weeks 1 to 26)
Week 1: Introduction to Artificial Intelligence – history, applications, ethical concerns, and AI in daily life.
Week 2: Python programming essentials for AI – focusing on NumPy, Pandas, and data visualization.
Week 3: Understanding Machine Learning – types, lifecycle, and tools.
Week 4: Supervised Learning – Linear and Logistic Regression.
Week 5: Supervised Learning – classification techniques like SVM, KNN, and Naive Bayes.
Week 6: Model evaluation using accuracy, precision, recall, F1 score, and confusion matrix.
Week 7: Unsupervised Learning – clustering methods like K-Means and hierarchical clustering.
Week 8: Dimensionality Reduction – techniques like PCA and t-SNE.
Week 9: Data preprocessing and feature engineering – handling missing values, scaling, encoding.
Week 10: Decision Trees, Random Forests, and ensemble techniques like XGBoost.
Week 11: Introduction to Neural Networks – perceptron, activation functions, and backpropagation.
Week 12: Deep Learning with TensorFlow and Keras – building your first ANN.
Week 13: Convolutional Neural Networks (CNNs) – architecture and image classification projects.
Week 14: Recurrent Neural Networks (RNNs) and LSTMs – for time series and sequential data.
Week 15: Introduction to Natural Language Processing (NLP) – tokenization, stemming, and lemmatization.
Week 16: Text feature extraction – Bag of Words, TF-IDF, and vectorization.
Week 17: Word embeddings – Word2Vec, GloVe, and contextual embeddings.
Week 18: Sentiment analysis, named entity recognition (NER), and basic NLP applications.
Week 19: Fundamentals of generative models and introduction to autoencoders.
Week 20: Variational Autoencoders (VAE) and their use in image generation.
Week 21: Introduction to GANs (Generative Adversarial Networks) – generator vs discriminator.
Week 22: Applications of GANs – deepfakes, image-to-image translation, and facial recognition.
Week 23: Case Study – Using StyleGAN for realistic image generation.
Week 24: Mid-term project – Build a text-to-image generation application using VAEs and GANs.
Week 25: Explore AI use cases in healthcare, finance, retail, and manufacturing.
Week 26: Review session and internal evaluation.
Semester 2: Generative AI, LLMs, and Industry Projects (Weeks 27 to 52)
Week 27: Introduction to Large Language Models (LLMs) – GPT, BERT, and Transformers.
Week 28: Prompt engineering and few-shot learning concepts.
Week 29: Fine-tuning open-source LLMs like LLaMA, Falcon, and Mistral.
Week 30: Building chatbots and text generation tools using GPT and LangChain.
Week 31: Introduction to Diffusion Models for image generation and tools like Stable Diffusion.
Week 32: Working with multimodal models – combining text, image, and audio inputs.
Week 33: Basics of Reinforcement Learning and RLHF (Reinforcement Learning from Human Feedback).
Week 34: Deploying AI applications using Flask, FastAPI, and Streamlit.
Week 35: Introduction to cloud AI platforms – AWS SageMaker, GCP Vertex AI, and Azure ML.
Week 36: Fundamentals of MLOps – version control, CI/CD, model monitoring.
Week 37: AI Ethics, bias in models, fairness, and Explainable AI.
Week 38: Edge AI – using AI on embedded devices, IoT, and mobile apps.
Week 39: Domain-specific AI – cybersecurity, HR analytics, marketing, and supply chain.
Week 40: Building your own GPT-style language model – tokenization, training, and inference.
Week 41: How startups are using Generative AI – case studies and product building tips.
Week 42: Create a resume and career plan using AI-powered tools.
Week 43: Planning final project – topic selection, tech stack, and proposal submission.
Weeks 44 to 47: Final Capstone Project – development and documentation.
Week 48: Final presentations and feedback sessions.
Week 49: Industry certification preparation – AWS, Azure, Google AI.
Week 50: Career readiness – portfolio building, GitHub setup, and networking tips.
Weeks 51 to 52: Internship, hackathon participation, or demo day for placement.
📚 Sample Assignments
- Implement a sentiment analysis tool using NLP.
- Develop a GAN to generate synthetic facial images.
- Create a chatbot that answers FAQs using GPT APIs.
- Build a CNN to classify medical X-ray images.
- Fine-tune a language model for resume evaluation.
🧪 Project Ideas
- AI Personal Assistant using GPT or open-source LLM.
- Fake News Detection System using NLP and ML.
- AI-based Resume Scanner for HR analytics.
- Text-to-Image generation app using diffusion models.
- Reinforcement learning-based stock trading simulator.
🎓 Certifications Covered (Optional Prep)
- AWS Certified Machine Learning – Specialty
- Microsoft Azure AI Engineer Associate
- Google Cloud Professional ML Engineer
- TensorFlow Developer Certificate
- Hugging Face Generative AI Certification
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A practical one-year Artificial Intelligence and Generative AI course for graduates and engineers, covering Python, Machine Learning, Deep Learning, GPT, GANs, NLP, Computer Vision, and real-world AI applications. Includes certification preparation and career support.
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CONTACT FOR ADMISSION
mail@global-skills-academy.com
