🏆 – AI/ML Engineer

Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects

Master Artificial Intelligence in 10 days with free resources 😄👇

Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.

Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.

Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.

Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.

Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.

Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.

Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.

Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.

Here are 5 amazing AI projects with free datasets: https://bit.ly/3ZVDjR1

Throughout the 10 days, it's important to practice what you learn through coding and practical exercises. Additionally, consider reading AI-related books and articles, watching online courses, and participating in AI communities and forums to enhance your learning experience.

Free Books and Courses to Learn Artificial Intelligence
👇👇

Introduction to AI Free Udacity Course

Introduction to Prolog programming for artificial intelligence Free Book

Introduction to AI for Business Free Course

Artificial Intelligence: Foundations of Computational Agents Free Book

Learn Basics about AI Free Udemy Course
(4.4 Star ratings out of 5)

Amazing AI Reverse Image Search
(4.7 Star ratings out of 5)

13 AI Tools to improve your productivity: https://t.iss.one/crackingthecodinginterview/619

4 AI Certifications for Developers: https://t.iss.one/datasciencefun/1375

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

For those who feel like they're not learning much and feeling demotivated. You should definitely read these lines from one of the book by Andrew Ng 👇

No one can cram everything they need to know over a weekend or even a month. Everyone I
know who’s great at machine learning is a lifelong learner. Given how quickly our field is changing,
there’s little choice but to keep learning if you want to keep up.
How can you maintain a steady pace of learning for years? If you can cultivate the habit of
learning a little bit every week, you can make significant progress with what feels like less effort.

Everyday it gets easier but you need to do it everyday ❤️

OnSpace Mobile App builder: Idea → AppStore → Profit.
👉https://onspace.ai/?via=tg_ggpt
With OnSpace, you can turn your idea into a real iOS or Android app in AppStore/PlayStore.

What will you get:
- Create app by chatting with AI
- Real-time app demo.
- Add payments and monetize like in-app-purchase and Stripe.
- Functional login & signup.
- Database + dashboard in minutes.
- Preview, download, and publish to AppStore.
- Full tutorial on YouTube and within 1 day customer service

🫵It’s your shortcut from concept to cash flow.

✅ 50 Must-Know Generative AI Concepts for Interviews 🎨🤖

📍 Generative AI Basics
1. What is Generative AI?
2. Generative AI vs Traditional AI
3. Applications of Generative AI
4. Diffusion Models vs GANs
5. Text, Image, Audio, Code Generation

📍 Large Language Models (LLMs)
6. What is a Language Model?
7. , BERT, T5 – key differences
8. Prompt Engineering
9. Zero-shot, Few-shot, Fine-tuning
10. Tokenization & Attention Mechanism

📍 Foundational Concepts
11. Transformers
12. Self-Attention
13. Positional Encoding
14. Pre-training & Fine-tuning
15. Loss Functions in Language Models (e.g., Cross-Entropy)

📍 Image Generation
16. GANs (Generative Adversarial Networks)
17. StyleGAN / CycleGAN
18. Diffusion Models (e.g., DALL·E, Stable Diffusion)
19. CLIP (Contrastive Language-Image Pretraining)
20. Text-to-Image Models

📍 Audio & Video Generation
21. Text-to-Speech (TTS)
22. Voice Cloning
23. AI Music Generation
24. Video Generation with AI
25. Deepfakes & Synthetic Media

📍 Evaluation & Safety
26. Evaluating LLMs (BLEU, ROUGE, perplexity)
27. Hallucinations in LLMs
28. Content Filtering & Safety Layers
29. Jailbreaks & Model Misuse
30. Red Teaming in AI

📍 Popular Tools & Platforms
31. OpenAI (Chat, DALL·E)
32. Google ChatGPT
33. Anthropic Claude
34. Meta Llama
35. Hugging Face Transformers

📍 Use Cases in Industries
36. Marketing & Content Generation
37. Customer Support (AI Chatbots)
38. Education (Tutors, Summarizers)
39. Healthcare (Medical Report Generation)
40. Coding (Code Assistants like Copilot)

📍 Fine-Tuning & Customization
41. LoRA (Low-Rank Adaptation)
42. RLHF (Reinforcement Learning from Human Feedback)
43. Retrieval-Augmented Generation (RAG)
44. Embeddings & Vector DBs (e.g., FAISS, Pinecone)
45. System vs User Prompts in LLMs

📍 Ethics & Future
46. AI Copyright & Ownership
47. Bias & Fairness in Generative Models
48. AI Watermarking & Detection
49. Responsible Deployment
50. Future of Human-AI Collaboration

7 Best Chrome Extensions for Agentic AI

#1. Magical
Automate entire workflows with AI triggers & actions — no manual clicks.
Best for: End-to-end automation across multiple web apps.
💡 Use Cases: Data entry → CRM sync → report export → all on autopilot.

#2. Merlin AI
Your universal browser copilot — summarize, write, and automate anywhere.
Best for: In-browser tasks, summaries & AI drafting.
💡 Use Cases: Summarize YouTube, draft replies, or research inline.

#3. Zapier Agents
AI agents that connect 8,000+ apps to automate complex workflows.
Best for: Multi-agent, cross-app business automation.
💡 Use Cases: CRM updates, lead enrichment, marketing approvals.

#4. Recall
Your second brain — search everything you’ve read, watched, or saved.
Best for: Knowledge recall & research continuity.
💡 Use Cases: Find past insights, retrieve web pages, build context graphs.

#5. BrowserAgent
Local, private automation — run AI agents fully offline.
Best for: Developers & privacy-focused automation.
💡 Use Cases: Web scraping, testing, and JS/TS agent workflows.

#6. Taskade AI
Collaborative AI agents for projects, research & creative ops.
Best for: Team workflows & AI-powered content pipelines.
💡 Use Cases: Research bots, task automation, editorial review.

#7. Perplexity AI
Autonomous research with verified sources & fast AI browsing.
Best for: Deep research and fact-checked answers.
💡 Use Cases: Academic research, market analysis, content synthesis.

Tools Every AI Engineer Should Know

1. Data Science Tools
Python: Preferred language with libraries like NumPy, Pandas, Scikit-learn.
R: Ideal for statistical analysis and data visualization.
Jupyter Notebook: Interactive coding environment for Python and R.
MATLAB: Used for mathematical modeling and algorithm development.
RapidMiner: Drag-and-drop platform for machine learning workflows.
KNIME: Open-source analytics platform for data integration and analysis.

2. Machine Learning Tools
Scikit-learn: Comprehensive library for traditional ML algorithms.
XGBoost & LightGBM: Specialized tools for gradient boosting.
TensorFlow: Open-source framework for ML and DL.
PyTorch: Popular DL framework with a dynamic computation graph.
H2O.ai: Scalable platform for ML and AutoML.
Auto-sklearn: AutoML for automating the ML pipeline.

3. Deep Learning Tools
Keras: User-friendly high-level API for building neural networks.
PyTorch: Excellent for research and production in DL.
TensorFlow: Versatile for both research and deployment.
ONNX: Open format for model interoperability.
OpenCV: For image processing and computer vision.
Hugging Face: Focused on natural language processing.

4. Data Engineering Tools
Apache Hadoop: Framework for distributed storage and processing.
Apache Spark: Fast cluster-computing framework.
Kafka: Distributed streaming platform.
Airflow: Workflow automation tool.
Fivetran: ETL tool for data integration.
dbt: Data transformation tool using SQL.

5. Data Visualization Tools
Tableau: Drag-and-drop BI tool for interactive dashboards.
Power BI: Microsoft’s BI platform for data analysis and visualization.
Matplotlib & Seaborn: Python libraries for static and interactive plots.
Plotly: Interactive plotting library with Dash for web apps.
D3.js: JavaScript library for creating dynamic web visualizations.

6. Cloud Platforms
AWS: Services like SageMaker for ML model building.
Google Cloud Platform (GCP): Tools like BigQuery and AutoML.
Microsoft Azure: Azure ML Studio for ML workflows.
IBM Watson: AI platform for custom model development.

7. Version Control and Collaboration Tools
Git: Version control system.
GitHub/GitLab: Platforms for code sharing and collaboration.
Bitbucket: Version control for teams.

8. Other Essential Tools
Docker: For containerizing applications.
Kubernetes: Orchestration of containerized applications.
MLflow: Experiment tracking and deployment.
Weights & Biases (W&B): Experiment tracking and collaboration.
Pandas Profiling: Automated data profiling.
BigQuery/Athena: Serverless data warehousing tools.
Mastering these tools will ensure you are well-equipped to handle various challenges across the AI lifecycle.

#artificialintelligence

Today, let's understand Generative AI in detail: 🤖

Generative AI is a branch of artificial intelligence focused on creating new content—whether it's text, images, music, or even code—by learning patterns from existing data.

Think of it like an artist who has studied thousands of paintings and then creates a brand new masterpiece inspired by what they've learned.

How Does Generative AI Work? 🤔

⦁ It trains on large datasets (e.g., text from books, images from the internet).
⦁ Learns the underlying patterns, structures, and features.
⦁ Generates fresh content that looks or sounds like the original data, but is unique. 
  (Powered by foundation models like LLMs, which multitask with minimal fine-tuning—e.g., Vertex AI for scalable gen content.)

📝 Examples of Generative AI:

1. Text Generation
⦁ ChatGPT writes essays, answers questions, or even creates stories based on your prompts. 
  Example: 
  You type: "Write a poem about autumn." 
  AI responds with a brand new poem you’ve never seen before.

2. Image Creation
⦁ DALL·E can generate images from text descriptions. 
  Example: 
  You type: "A futuristic city at sunset." 
  AI creates a unique, never-before-seen image matching your description.

3. Music Composition 
   AI models can compose original music tracks based on genre or mood you specify.

4. Code Generation 
   Tools like GitHub Copilot help programmers by suggesting code snippets.

Difference Between AI, ML, and Deep Learning ✍️

⦁ AI (Artificial Intelligence): The broad field where machines mimic human intelligence. 
  Example: A chatbot answering questions.

⦁ ML (Machine Learning): A way AI learns by analyzing data and improving without explicit programming. 
  Example: Spam filters learning which emails are junk.

⦁ Deep Learning: A specialized ML method using layered neural networks to understand complex data. 
  Example: Recognizing faces in photos or understanding language context in chatbots. 
  (GenAI sits in deep learning, using techniques like transformers for creative outputs—key for 2025's interactive experiences.)

Generative AI Roadmap: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U/303

💬 Tap ❤️ for more!

✅ Generative AI Basics 🤖✨

📌 Basics of Neural Networks
⦁ Neural networks are computing systems inspired by the human brain.
⦁ They consist of layers of nodes (“neurons”) that process input data, learn patterns, and produce outputs.
⦁ Each connection has a weight adjusted during training to improve accuracy.
⦁ Common types: Feedforward, Convolutional (for images), Recurrent (for sequences).

📌 Introduction to NLP (Natural Language Processing)
⦁ NLP enables machines to understand, interpret, and generate human language.
⦁ Tasks include text classification, translation, sentiment analysis, and summarization.
⦁ Models process text by converting words into numbers and learning context.

📌 Introduction to Computer Vision
⦁ Computer Vision allows AI to “see” and interpret images or videos.
⦁ Tasks include image classification, object detection, segmentation, and image generation.
⦁ Uses convolutional neural networks (CNNs) to detect patterns like edges, shapes, and textures.

📌 Key Concepts: Embeddings, Tokens, Transformers
⦁ Tokens: Pieces of text (words, subwords) that models read one by one.
⦁ Embeddings: Numeric representations of tokens that capture meaning and relationships.
⦁ Transformers: A powerful AI architecture that uses “attention” to weigh the importance of tokens in context, enabling better understanding and generation of language.

📝 In short: 
Neural Networks build the brain → NLP teaches language understanding → Computer Vision teaches visual understanding → Transformers connect everything with context.

💬 Tap ❤️ for more!