Guys, Big Announcement!
We’ve officially crossed 4 Lakh followers on this journey together — and it’s time to step up now! ❤️
I’m launching a Coding Interview Prep Series — designed for everyone from beginners to those polishing their skills for FAANG-level interviews.
This will be a structured, step-by-step journey — with short explanations, real coding examples, and mini-challenges after every topic to build real muscle memory.
Here’s what’s coming in the next few weeks:
Week 1: The Very Basics
- What is an Algorithm?
- What is Data Structure?
- Understanding Time Complexity (Big O Notation - made simple!)
- Basic Math for Coding Interviews
- Problem Solving Approach (How to break down a question)
Week 2: Arrays & Strings — Your Building Blocks
- Introduction to Arrays and Strings
- Common Operations (Insert, Delete, Search)
- Two Pointer Techniques (Easy to Medium problems)
- Sliding Window Problems (Optimization techniques)
- String Manipulation Tricks for Interviews
Week 3: Hashing & Recursion
- HashMaps and HashSets (Power tools for coders!)
- Solving Problems using Hashing
- Introduction to Recursion
- Base Case and Recursive Case (Explained like a 5-year-old)
- Classic Recursion Problems
Week 4: Linked Lists, Stacks & Queues
- Singly vs Doubly Linked List
- Stack Operations and Problems (Valid Parentheses, Min Stack)
- Queue and Deque Concepts (with real examples)
- When to Use Stack vs Queue in Interviews
Week 5: Trees & Graphs Essentials
- Binary Trees and BST Basics
- Tree Traversals (Inorder, Preorder, Postorder)
- Graph Representations (Adjacency List, Matrix)
- Breadth-First Search (BFS) and Depth-First Search (DFS) explained simply
Week 6: Sorting, Searching & Interview Patterns
- Core Sorting Algorithms (Selection, Bubble, Insertion)
- Advanced Sorting (Merge Sort, Quick Sort)
- Binary Search Patterns (Find First, Last Occurrence, etc.)
- Mastering Interview Patterns (Two Sum, Three Sum, Subarray Sum, etc.)
Week 7: Dynamic Programming & Advanced Problem Solving
- What is Dynamic Programming (DP)?
- Top-Down vs Bottom-Up Approach
- Memoization and Tabulation Explained
- Classic DP Problems (Fibonacci, 0/1 Knapsack, Longest Subsequence)
Week 8: Real-World Mock Interviews
- Solving Medium to Hard Problems
- Tackling FAANG-level Interview Questions
- Tips to Handle Pressure in Coding Rounds
- Building the Right Mindset for Success
React with ❤️ if you're ready for this new coding series
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
We’ve officially crossed 4 Lakh followers on this journey together — and it’s time to step up now! ❤️
I’m launching a Coding Interview Prep Series — designed for everyone from beginners to those polishing their skills for FAANG-level interviews.
This will be a structured, step-by-step journey — with short explanations, real coding examples, and mini-challenges after every topic to build real muscle memory.
Here’s what’s coming in the next few weeks:
Week 1: The Very Basics
- What is an Algorithm?
- What is Data Structure?
- Understanding Time Complexity (Big O Notation - made simple!)
- Basic Math for Coding Interviews
- Problem Solving Approach (How to break down a question)
Week 2: Arrays & Strings — Your Building Blocks
- Introduction to Arrays and Strings
- Common Operations (Insert, Delete, Search)
- Two Pointer Techniques (Easy to Medium problems)
- Sliding Window Problems (Optimization techniques)
- String Manipulation Tricks for Interviews
Week 3: Hashing & Recursion
- HashMaps and HashSets (Power tools for coders!)
- Solving Problems using Hashing
- Introduction to Recursion
- Base Case and Recursive Case (Explained like a 5-year-old)
- Classic Recursion Problems
Week 4: Linked Lists, Stacks & Queues
- Singly vs Doubly Linked List
- Stack Operations and Problems (Valid Parentheses, Min Stack)
- Queue and Deque Concepts (with real examples)
- When to Use Stack vs Queue in Interviews
Week 5: Trees & Graphs Essentials
- Binary Trees and BST Basics
- Tree Traversals (Inorder, Preorder, Postorder)
- Graph Representations (Adjacency List, Matrix)
- Breadth-First Search (BFS) and Depth-First Search (DFS) explained simply
Week 6: Sorting, Searching & Interview Patterns
- Core Sorting Algorithms (Selection, Bubble, Insertion)
- Advanced Sorting (Merge Sort, Quick Sort)
- Binary Search Patterns (Find First, Last Occurrence, etc.)
- Mastering Interview Patterns (Two Sum, Three Sum, Subarray Sum, etc.)
Week 7: Dynamic Programming & Advanced Problem Solving
- What is Dynamic Programming (DP)?
- Top-Down vs Bottom-Up Approach
- Memoization and Tabulation Explained
- Classic DP Problems (Fibonacci, 0/1 Knapsack, Longest Subsequence)
Week 8: Real-World Mock Interviews
- Solving Medium to Hard Problems
- Tackling FAANG-level Interview Questions
- Tips to Handle Pressure in Coding Rounds
- Building the Right Mindset for Success
React with ❤️ if you're ready for this new coding series
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
❤8👍1
You can use ChatGPT to make money online.
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"
Read more......
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"
Read more......
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List of AI Project Ideas 👨🏻💻🤖 -
Beginner Projects
🔹 Sentiment Analyzer
🔹 Image Classifier
🔹 Spam Detection System
🔹 Face Detection
🔹 Chatbot (Rule-based)
🔹 Movie Recommendation System
🔹 Handwritten Digit Recognition
🔹 Speech-to-Text Converter
🔹 AI-Powered Calculator
🔹 AI Hangman Game
Intermediate Projects
🔸 AI Virtual Assistant
🔸 Fake News Detector
🔸 Music Genre Classification
🔸 AI Resume Screener
🔸 Style Transfer App
🔸 Real-Time Object Detection
🔸 Chatbot with Memory
🔸 Autocorrect Tool
🔸 Face Recognition Attendance System
🔸 AI Sudoku Solver
Advanced Projects
🔺 AI Stock Predictor
🔺 AI Writer (GPT-based)
🔺 AI-powered Resume Builder
🔺 Deepfake Generator
🔺 AI Lawyer Assistant
🔺 AI-Powered Medical Diagnosis
🔺 AI-based Game Bot
🔺 Custom Voice Cloning
🔺 Multi-modal AI App
🔺 AI Research Paper Summarizer
Join for more: https://t.me/machinelearning_deeplearning
Beginner Projects
🔹 Sentiment Analyzer
🔹 Image Classifier
🔹 Spam Detection System
🔹 Face Detection
🔹 Chatbot (Rule-based)
🔹 Movie Recommendation System
🔹 Handwritten Digit Recognition
🔹 Speech-to-Text Converter
🔹 AI-Powered Calculator
🔹 AI Hangman Game
Intermediate Projects
🔸 AI Virtual Assistant
🔸 Fake News Detector
🔸 Music Genre Classification
🔸 AI Resume Screener
🔸 Style Transfer App
🔸 Real-Time Object Detection
🔸 Chatbot with Memory
🔸 Autocorrect Tool
🔸 Face Recognition Attendance System
🔸 AI Sudoku Solver
Advanced Projects
🔺 AI Stock Predictor
🔺 AI Writer (GPT-based)
🔺 AI-powered Resume Builder
🔺 Deepfake Generator
🔺 AI Lawyer Assistant
🔺 AI-Powered Medical Diagnosis
🔺 AI-based Game Bot
🔺 Custom Voice Cloning
🔺 Multi-modal AI App
🔺 AI Research Paper Summarizer
Join for more: https://t.me/machinelearning_deeplearning
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How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
Like for more ❤️
All the best 👍👍
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.me/machinelearning_deeplearning
Like for more ❤️
All the best 👍👍
❤3👍1
LangChain Crash Course -Greg Lim, 2023.pdf
7.5 MB
LangChain Crash Course
Greg Lim, 2023
Greg Lim, 2023
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⭐️ What is Generative AI?
Generative AI typically uses machine learning models, especially deep learning models, to learn from input data and then generate new data based on the patterns and trends it has learned. This can be applied for many different purposes, from creating images, videos, sounds, text or 3D models. Generative AI is also being widely adopted in many business and industrial sectors to optimize processes, create new products and services, and improve overall organizational performance.
The latest breakthroughs like ChatGPT, a chatbot developed by OpenAI (USA) is a typical example of Generative AI. GPT Chat has the ability to create content in a variety of genres such as text responses, blogging, poetry, song lyrics… without limiting language or any topic. In addition to ChatGPT, many Generative AI products are available on the market and can fully handle programming, painting, video making, data analysis…
Hekate has successfully applied Generative AI in many fields: Retail and E-commerce (Coca-Cola; Pla18); Real Estate (Masterise); Public area; Governmental and non-governmental organizations.
Generative AI typically uses machine learning models, especially deep learning models, to learn from input data and then generate new data based on the patterns and trends it has learned. This can be applied for many different purposes, from creating images, videos, sounds, text or 3D models. Generative AI is also being widely adopted in many business and industrial sectors to optimize processes, create new products and services, and improve overall organizational performance.
The latest breakthroughs like ChatGPT, a chatbot developed by OpenAI (USA) is a typical example of Generative AI. GPT Chat has the ability to create content in a variety of genres such as text responses, blogging, poetry, song lyrics… without limiting language or any topic. In addition to ChatGPT, many Generative AI products are available on the market and can fully handle programming, painting, video making, data analysis…
Hekate has successfully applied Generative AI in many fields: Retail and E-commerce (Coca-Cola; Pla18); Real Estate (Masterise); Public area; Governmental and non-governmental organizations.
⭐️ How to evaluate Generative AI models?
Three important things for a successful generative AI model are:
Quality: For applications that interact directly with users, it is most important to have high quality output. For example, in speech production, if the quality is poor, it will be difficult for the listener to understand. Similarly, when creating images, the desired results should resemble natural images.
Diversity: A good generative model is one that is capable of capturing rare cases in the data without sacrificing output quality. This helps reduce unwanted biases in learning models.
Speed: Many interactive applications require rapid creation, such as instant photo editing for use in the content creation workflow.
Three important things for a successful generative AI model are:
Quality: For applications that interact directly with users, it is most important to have high quality output. For example, in speech production, if the quality is poor, it will be difficult for the listener to understand. Similarly, when creating images, the desired results should resemble natural images.
Diversity: A good generative model is one that is capable of capturing rare cases in the data without sacrificing output quality. This helps reduce unwanted biases in learning models.
Speed: Many interactive applications require rapid creation, such as instant photo editing for use in the content creation workflow.
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⭐️ What are the applications of Generative AI?
Generative AI is a powerful tool to standardize the workflow of innovators, engineers, researchers, scientists, and more. Use cases and capabilities span all sectors and individuals.
Generative AI models can take inputs like text, images, audio, video, and code and generate new content in any of the methods mentioned. For example, it can turn input text into images, turn images into songs, or turn videos into text.
Generative AI is a powerful tool to standardize the workflow of innovators, engineers, researchers, scientists, and more. Use cases and capabilities span all sectors and individuals.
Generative AI models can take inputs like text, images, audio, video, and code and generate new content in any of the methods mentioned. For example, it can turn input text into images, turn images into songs, or turn videos into text.
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⭐️ Generative AI Use Cases
Below are popular Generative AI applications
Language:
Text is the foundation of many AI models, and large language models (LLMs) are a popular example. LLM can be used for a variety of tasks such as essay creation, code development, translation, and even understanding genetic sequences.
Sound:
AI is also applied in music, audio and speech. Models can develop songs, generate audio from text, recognize objects in videos, and even generate audio for different scenes.
Image:
In the visual field, AI is widely used to create 3D images, avatars, videos, graphs, and illustrations. Models have the flexibility to create images with a variety of aesthetic styles and editing techniques.
Synthetic data:
Synthetic data is extremely important for training AI models when data is insufficient, limited, or simply cannot solve difficult cases with the highest accuracy. Synthetic data spans all methods and use cases and is made possible through a process called label efficient learning. Generative AI models can reduce labeling costs by generating training data automatically or by learning how to use less labeled data.
Innovative AI models are highly influential in many fields. In cars, they can help develop 3D worlds and simulations, as well as train autonomous vehicles. In medicine, they can aid in medical research and weather prediction. In entertainment, from games to movies and virtual worlds, AI models help create content and enhance creativity.
Below are popular Generative AI applications
Language:
Text is the foundation of many AI models, and large language models (LLMs) are a popular example. LLM can be used for a variety of tasks such as essay creation, code development, translation, and even understanding genetic sequences.
Sound:
AI is also applied in music, audio and speech. Models can develop songs, generate audio from text, recognize objects in videos, and even generate audio for different scenes.
Image:
In the visual field, AI is widely used to create 3D images, avatars, videos, graphs, and illustrations. Models have the flexibility to create images with a variety of aesthetic styles and editing techniques.
Synthetic data:
Synthetic data is extremely important for training AI models when data is insufficient, limited, or simply cannot solve difficult cases with the highest accuracy. Synthetic data spans all methods and use cases and is made possible through a process called label efficient learning. Generative AI models can reduce labeling costs by generating training data automatically or by learning how to use less labeled data.
Innovative AI models are highly influential in many fields. In cars, they can help develop 3D worlds and simulations, as well as train autonomous vehicles. In medicine, they can aid in medical research and weather prediction. In entertainment, from games to movies and virtual worlds, AI models help create content and enhance creativity.
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