✍️ Prompt Engineering Fundamentals: Speak AI’s Language with Precision

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🧾 Course Description

Prompt engineering is the skill of designing effective inputs to get accurate, creative, and actionable outputs from Generative AI models like GPT-4, Claude, LLaMA, Gemini, and Stable Diffusion. In “Prompt Engineering Fundamentals”, you’ll learn to craft, refine, and automate prompts for use in text, code, images, chatbots, and more.

Designed for developers, content creators, AI product designers, educators, and automation experts, this course turns you into a prompt expert with a mix of strategy, structure, and experimentation.

Whether you’re building AI copilots, chat agents, creative pipelines, or marketing tools, mastering prompts is your key to unlocking GenAI’s full potential.

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✅ Key Benefits

• 🧠 Master the Craft of Prompting — Understand how large language models think and respond
• 🎯 Structured & Strategic — Learn techniques like role prompting, few-shot, chain-of-thought
• 💬 Cross-Modal Mastery — Text, code, image, and tool-augmented prompting
• 🤖 Build Prompt-Driven Apps — Ready for ChatGPT, LangChain, and OpenAI API integration
• 🚀 Boost Productivity & Creativity — Write, code, ideate, and build faster with AI

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🎯 Pre-requisites

• Basic understanding of Generative AI tools (like ChatGPT, Claude, DALL·E)
• Comfortable with using text editors, APIs, or no-code tools
• Optional: experience in Python, copywriting, or LLMs helpful but not required
• No prior AI/ML background needed

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📚 Curriculum Breakdown

📘 Module 1: Introduction to Prompt Engineering

• What is a prompt? What makes it effective?
• Language models and prompt-response architecture
• Prompt vs fine-tuning vs embeddings vs agents

✍️ Module 2: Core Prompting Techniques

• Instructional prompting
• Role prompting (You are a...)
• Zero-shot vs one-shot vs few-shot
• Prompt patterns for summarization, rewriting, ideation, coding

🔗 Module 3: Advanced Prompt Patterns

• Chain-of-Thought (CoT) and self-consistency
• ReAct (Reasoning + Action)
• Tree of Thought and step-wise solving
• Injecting context, memory, and constraints

🛠️ Module 4: Prompt Engineering in Tools & APIs

• Prompting in OpenAI Playground, ChatGPT, Claude
• API-based prompting via OpenAI or HuggingFace
• Prompt templates and injection in LangChain / LlamaIndex
• Temperature, top_p, max_tokens explained

🎨 Module 5: Cross-Modal Prompting

• Prompting DALL·E, Midjourney, Stable Diffusion
• Crafting image prompts: style, composition, structure
• Prompting for audio, video, and multi-turn conversations

📊 Module 6: Evaluation & Iteration

• Measuring output quality: relevance, bias, hallucination
• Refining prompts with prompt chaining and feedback loops
• Designing reusable and modular prompts

🧪 Module 7: Final Projects

Choose one:

• Build a multi-turn conversational AI prompt suite
• Create a prompt-powered writing assistant
• Design text-to-image pipelines using structured prompts
• Build prompt workflows using Zapier + OpenAI API

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⏱️ Estimated Duration

Daily Study Time	Estimated Duration	Ideal For
2 hours/day	10–12 days (~2 weeks)	Best for thoughtful learning pace
4 hours/day	5–6 days (1 week)	Balanced practice + experimentation
6 hours/day	3–4 days (bootcamp)	Intense, project-driven immersion

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🎓 Outcome

By the end of Prompt Engineering Fundamentals, you will:

• Write clear, powerful prompts for text, code, image, and chat
• Use prompting as a superpower in creative, analytical, and technical work
• Automate and integrate prompt workflows using APIs or GenAI tools
• Be ready for AI product design, LLM app development, and AI agent engineering

🎨 Generative AI Tools & Workflows: Build, Create, and Automate with Intelligence

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🧾 Course Description

Generative AI is revolutionizing the way we create content, design experiences, and build intelligent systems. In “Generative AI Tools & Workflows”, you’ll explore how to use modern GenAI platforms — like OpenAI, HuggingFace, Google Vertex AI, and Stability AI — to generate text, images, code, and audio, and integrate them into real-world applications.

This course is designed for developers, creatives, product designers, marketers, and AI enthusiasts who want to master tools like ChatGPT, DALL·E, Midjourney, GitHub Copilot, and build end-to-end GenAI workflows that go beyond experimentation and into usable products.

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✅ Key Benefits

• 🧠 Understand GenAI Foundations — Learn how LLMs, diffusion models, and transformers work
• 🔌 Tool-Focused & Practical — Hands-on with OpenAI, HuggingFace, DALL·E, and more
• 🤖 Automate & Accelerate — Build GenAI agents, pipelines, and integrations
• 🧰 No-Code to Code — Combine GUI tools and APIs for flexible workflows
• 🚀 Real Use Cases — Product copywriting, code generation, image synthesis, voice AI, and chatbots

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🎯 Pre-requisites

• Basic knowledge of Python, APIs, and working with JSON
• Experience with web apps, automation, or creative tools is helpful
• No prior machine learning or deep AI background required

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📚 Curriculum Breakdown

📘 Module 1: Foundations of Generative AI

• What is Generative AI? Types: Text, Image, Code, Audio, Video
• Core models: LLMs (GPT), Diffusion (Stable Diffusion), GANs
• Understanding prompts, embeddings, transformers

💬 Module 2: Text Generation Workflows

• Prompt engineering with OpenAI GPT-4 / ChatGPT API
• Use cases: blog writing, summarization, email generation, chatbot flows
• Prompt chaining, few-shot examples, system messages

🎨 Module 3: Image Generation Tools

• Tools: DALL·E 3, Midjourney, Stable Diffusion, Canva AI
• Text-to-image prompts, styles, inpainting, variations
• Use cases: social media graphics, storyboarding, thumbnails

🧠 Module 4: Code Generation & Developer Tools

• Using GitHub Copilot, OpenAI Codex
• Generating Python/JavaScript snippets
• Automating documentation and test generation
• Safety and hallucination issues in generated code

🧩 Module 5: APIs, Agents, and Workflows

• Calling GenAI models via API (OpenAI, HuggingFace, Replicate)
• Building intelligent agents using LangChain or custom Python wrappers
• Connecting multiple tools into one pipeline (e.g., input → process → output → deploy)

🎛️ Module 6: Automation, Ethics & Limitations

• Using Zapier, Make, and no-code tools to trigger GenAI flows
• Handling latency, token limits, hallucination, cost
• Ethical considerations: bias, misuse, data privacy

🧪 Module 7: Final Projects (Choose One)

• Build a content generator for blogs/social media
• Create a prompt-based image+text portfolio builder
• Develop a simple AI chatbot using OpenAI API + LangChain
• Deploy a GitHub Copilot-style code assistant

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⏱️ Estimated Duration

Daily Study Time	Estimated Duration	Ideal For
2 hours/day	14–16 days (~2 weeks)	Explorers and builders
4 hours/day	6–8 days (1 week)	Balanced project-driven learners
6 hours/day	3–4 days (bootcamp)	Intensive fast-track learning

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🎓 Outcome

By the end of Generative AI Tools & Workflows, you will:

• Use text, image, and code generation tools effectively
• Integrate GenAI APIs into products and creative workflows
• Build simple agents and automation using OpenAI and LangChain
• Be ready to pursue LLM app development, GenAI product design, or AI agent engineering

🧠 Introduction to Machine Learning & Deep Learning: From Data to Intelligence

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🧾 Course Description

Machine Learning (ML) and Deep Learning (DL) are the engines powering today’s intelligent systems — from recommendation engines and fraud detection to self-driving cars and chatbots. In “Introduction to Machine Learning & Deep Learning”, you’ll learn the mathematical intuition, practical tools, and coding skills to build and deploy your first models.

This beginner-friendly course covers supervised and unsupervised learning, neural networks, model evaluation, and hands-on implementation using Python, Scikit-learn, TensorFlow, and Keras. Whether you’re a developer, data enthusiast, or aspiring AI engineer, this course builds the right foundation for your ML journey.

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✅ Key Benefits

• 🧠 Build Strong Foundations — Learn core concepts like regression, classification, loss functions, and overfitting
• 🧰 Hands-On Implementation — Code real ML/DL models using Scikit-learn and TensorFlow/Keras
• 🔄 Understand Model Lifecycle — Train, evaluate, optimize, and deploy ML models
• 🎯 Clarity over Complexity — Visual explanations of neural networks and backpropagation
• 🚀 Career Gateway — Prepares you for ML specialization, GenAI, or MLOps tracks

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🎯 Pre-requisites

• Proficiency with Python (data types, functions, loops, libraries like NumPy/Pandas)
• Basic understanding of linear algebra, statistics, and probability (conceptual level)
• No prior experience in ML or DL required

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📚 Curriculum Breakdown

📘 Module 1: Introduction to Machine Learning

• What is ML? Types of ML: supervised, unsupervised, reinforcement
• Use cases: NLP, vision, recommendation, healthcare
• ML workflow: data → model → prediction → evaluation

📊 Module 2: Data Preprocessing & Exploration

• Cleaning and transforming data
• Feature engineering and normalization
• Data splitting: train, test, validation

🔢 Module 3: Supervised Learning Algorithms

• Linear regression, logistic regression
• Decision trees, random forest, SVM
• Model metrics: accuracy, precision, recall, F1-score

🧩 Module 4: Unsupervised Learning

• Clustering: K-Means, Hierarchical
• Dimensionality reduction: PCA
• Real-life use cases: customer segmentation, anomaly detection

🧠 Module 5: Introduction to Deep Learning

• What is a neural network? Perceptron to MLP
• Layers, weights, activation functions
• Forward pass and backpropagation (intuitive explanation)

⚙️ Module 6: Building Neural Networks with TensorFlow/Keras

• Model architecture and compiling
• Training and evaluating a model
• Avoiding overfitting: dropout, early stopping, batch size

🧪 Module 7: Real-World Projects

Choose one:

• Predict house prices (regression)
• Classify images (MNIST / CIFAR-10)
• Detect spam messages (NLP)

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⏱️ Estimated Duration

Daily Study Time	Estimated Duration	Ideal For
2 hours/day	16–18 days (~3 weeks)	Balanced pace with practice
4 hours/day	8–9 days (~1.5 weeks)	For committed learners
6 hours/day	5–6 days (bootcamp)	Immersive and project-intensive

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🎓 Outcome

By the end of Introduction to Machine Learning & Deep Learning, you will:

• Understand the mathematics and workflow behind ML/DL models
• Build, train, and evaluate your own models using Scikit-learn and Keras
• Apply ML/DL to real-world problems in vision, text, and prediction
• Be ready to advance into deep learning, MLOps, LLMs, or AI product development

🤖 AI Concepts for Developers: Build Smarter Software with Intelligent Design

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🧾 Course Description

Artificial Intelligence is no longer limited to research labs — it’s now a core part of modern software development. “AI Concepts for Developers” is designed to help developers, software engineers, and architects understand how AI works, where it fits into their tech stack, and how to start building intelligent features using practical tools and APIs.

This course covers foundational concepts like machine learning, neural networks, natural language processing (NLP), computer vision, and introduces frameworks like TensorFlow, PyTorch, and OpenAI APIs — all explained with real-world developer use cases.

Whether you’re building a chatbot, a recommendation engine, or a smart search feature, this course gives you the knowledge to get started confidently.

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✅ Key Benefits

• 🧠 Understand AI Without a PhD — Learn ML and AI principles as a developer
• 🔌 Practical & Tool-Centric — Use Python, APIs, and SDKs to implement AI features
• 🌍 Real-World Use Cases — Image recognition, sentiment analysis, intelligent automation
• 🧰 Developer-Centric Approach — Write and deploy simple AI models, not just theory
• 🚀 Foundation for Advanced Learning — Ideal gateway to deeper ML, LLMs, and GenAI tracks

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🎯 Pre-requisites

• Familiarity with Python or JavaScript (control flow, functions, data structures)
• Comfort with APIs, JSON, and basic HTTP concepts
• No prior experience in AI or machine learning required

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📚 Curriculum Breakdown

📘 Module 1: Introduction to AI & ML

• What is AI, ML, DL? Differences and use cases
• Supervised vs unsupervised learning
• Real-world applications and trends

🧠 Module 2: Key AI Concepts for Developers

• Classification, regression, clustering
• Data preprocessing basics
• Accuracy, precision, recall explained

🧰 Module 3: AI Tools & Frameworks

• Python + Scikit-learn basics
• TensorFlow & PyTorch (intro only)
• Using HuggingFace & OpenAI APIs

💬 Module 4: Natural Language Processing (NLP)

• Tokenization, sentiment analysis
• Text classification using pretrained models
• Building simple chatbots using OpenAI or Cohere

🖼️ Module 5: Computer Vision Basics

• Image classification with MobileNet or ResNet
• Object detection using pretrained models
• Using OpenCV for image processing tasks

📦 Module 6: Integrating AI into Applications

• AI as a microservice
• Using AI in frontend/backend via REST APIs
• Security, latency, cost considerations

🧪 Module 7: Final Project

• Choose one:
  • Smart chatbot using OpenAI API
  • AI image recognizer (upload + predict)
  • AI-powered feedback summarizer or recommender

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⏱️ Estimated Duration

Daily Study Time	Estimated Duration	Ideal For
2 hours/day	14–16 days (~2 weeks)	Developers with full-time jobs
4 hours/day	7–8 days (~1 week)	Intermediate learners
6 hours/day	4–5 days (bootcamp)	Fast-track with mini-project

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🎓 Outcome

By the end of AI Concepts for Developers, you will:

• Understand the building blocks of AI and ML
• Be able to use popular APIs and Python libraries to solve real-world problems
• Build your first AI-powered features into apps
• Be ready to pursue deeper learning in ML, LLMs, or GenAI