🖥 Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
❤3👍1
Are you looking to become a machine learning engineer? 🤖
The algorithm brought you to the right place! 🚀
I created a free and comprehensive roadmap. Let’s go through this thread and explore what you need to know to become an expert machine learning engineer:
📚 Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Here’s what you need to focus on:
- Basic probability concepts 🎲
- Inferential statistics 📊
- Regression analysis 📈
- Experimental design & A/B testing 🔍
- Bayesian statistics 🔢
- Calculus 🧮
- Linear algebra 🔠
🐍 Python
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
- Variables, data types, and basic operations ✏️
- Control flow statements (e.g., if-else, loops) 🔄
- Functions and modules 🔧
- Error handling and exceptions ❌
- Basic data structures (e.g., lists, dictionaries, tuples) 🗂️
- Object-oriented programming concepts 🧱
- Basic work with APIs 🌐
- Detailed data structures and algorithmic thinking 🧠
🧪 Machine Learning Prerequisites
- Exploratory Data Analysis (EDA) with NumPy and Pandas 🔍
- Data visualization techniques to visualize variables 📉
- Feature extraction & engineering 🛠️
- Encoding data (different types) 🔐
⚙️ Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:
- Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees 📊
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering 🧠
- Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients 🕹️
Solve two types of problems:
- Regression 📈
- Classification 🧩
🧠 Neural Networks
Neural networks are like computer brains that learn from examples 🧠, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
- Feedforward Neural Networks: Simplest form, with straight connections and no loops 🔄
- Convolutional Neural Networks (CNNs): Great for images, learning visual patterns 🖼️
- Recurrent Neural Networks (RNNs): Good for sequences like text or time series 📚
In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.
🕸️ Deep Learning
Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.
- CNNs 🖼️
- RNNs 📝
- LSTMs ⏳
🚀 Machine Learning Project Deployment
Machine learning engineers should dive into MLOps and project deployment.
Here are the must-have skills:
- Version Control for Data and Models 🗃️
- Automated Testing and Continuous Integration (CI) 🔄
- Continuous Delivery and Deployment (CD) 🚚
- Monitoring and Logging 🖥️
- Experiment Tracking and Management 🧪
- Feature Stores 🗂️
- Data Pipeline and Workflow Orchestration 🛠️
- Infrastructure as Code (IaC) 🏗️
- Model Serving and APIs 🌐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
The algorithm brought you to the right place! 🚀
I created a free and comprehensive roadmap. Let’s go through this thread and explore what you need to know to become an expert machine learning engineer:
📚 Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Here’s what you need to focus on:
- Basic probability concepts 🎲
- Inferential statistics 📊
- Regression analysis 📈
- Experimental design & A/B testing 🔍
- Bayesian statistics 🔢
- Calculus 🧮
- Linear algebra 🔠
🐍 Python
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
- Variables, data types, and basic operations ✏️
- Control flow statements (e.g., if-else, loops) 🔄
- Functions and modules 🔧
- Error handling and exceptions ❌
- Basic data structures (e.g., lists, dictionaries, tuples) 🗂️
- Object-oriented programming concepts 🧱
- Basic work with APIs 🌐
- Detailed data structures and algorithmic thinking 🧠
🧪 Machine Learning Prerequisites
- Exploratory Data Analysis (EDA) with NumPy and Pandas 🔍
- Data visualization techniques to visualize variables 📉
- Feature extraction & engineering 🛠️
- Encoding data (different types) 🔐
⚙️ Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:
- Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees 📊
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering 🧠
- Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients 🕹️
Solve two types of problems:
- Regression 📈
- Classification 🧩
🧠 Neural Networks
Neural networks are like computer brains that learn from examples 🧠, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
- Feedforward Neural Networks: Simplest form, with straight connections and no loops 🔄
- Convolutional Neural Networks (CNNs): Great for images, learning visual patterns 🖼️
- Recurrent Neural Networks (RNNs): Good for sequences like text or time series 📚
In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.
🕸️ Deep Learning
Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.
- CNNs 🖼️
- RNNs 📝
- LSTMs ⏳
🚀 Machine Learning Project Deployment
Machine learning engineers should dive into MLOps and project deployment.
Here are the must-have skills:
- Version Control for Data and Models 🗃️
- Automated Testing and Continuous Integration (CI) 🔄
- Continuous Delivery and Deployment (CD) 🚚
- Monitoring and Logging 🖥️
- Experiment Tracking and Management 🧪
- Feature Stores 🗂️
- Data Pipeline and Workflow Orchestration 🛠️
- Infrastructure as Code (IaC) 🏗️
- Model Serving and APIs 🌐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
👍1🔥1
5 Easy Projects to Build as a Beginner
(No AI degree needed. Just curiosity & coffee.)
❯ 1. Calculator App
• Learn logic building
• Try it in Python, JavaScript or C++
• Bonus: Add GUI using Tkinter or HTML/CSS
❯ 2. Quiz App (with Score Tracker)
• Build a fun MCQ quiz
• Use basic conditions, loops, and arrays
• Add a timer for extra challenge!
❯ 3. Rock, Paper, Scissors Game
• Classic game using random choice
• Great to practice conditions and user input
• Optional: Add a scoreboard
❯ 4. Currency Converter
• Convert from USD to INR, EUR, etc.
• Use basic math or try fetching live rates via API
• Build a mini web app for it!
❯ 5. To-Do List App
• Create, read, update, delete tasks
• Perfect for learning arrays and functions
• Bonus: Add local storage (in JS) or file saving (in Python)
React with ❤️ for the source code
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
(No AI degree needed. Just curiosity & coffee.)
❯ 1. Calculator App
• Learn logic building
• Try it in Python, JavaScript or C++
• Bonus: Add GUI using Tkinter or HTML/CSS
❯ 2. Quiz App (with Score Tracker)
• Build a fun MCQ quiz
• Use basic conditions, loops, and arrays
• Add a timer for extra challenge!
❯ 3. Rock, Paper, Scissors Game
• Classic game using random choice
• Great to practice conditions and user input
• Optional: Add a scoreboard
❯ 4. Currency Converter
• Convert from USD to INR, EUR, etc.
• Use basic math or try fetching live rates via API
• Build a mini web app for it!
❯ 5. To-Do List App
• Create, read, update, delete tasks
• Perfect for learning arrays and functions
• Bonus: Add local storage (in JS) or file saving (in Python)
React with ❤️ for the source code
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
❤3👍2
Important questions to ace your machine learning interview with an approach to answer:
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
👇👇
https://topmate.io/coding/914624
Like for more 😄
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
👇👇
https://topmate.io/coding/914624
Like for more 😄
👍2
There are several techniques that can be used to handle imbalanced data in machine learning. Some common techniques include:
1. Resampling: This involves either oversampling the minority class, undersampling the majority class, or a combination of both to create a more balanced dataset.
2. Synthetic data generation: Techniques such as SMOTE (Synthetic Minority Over-sampling Technique) can be used to generate synthetic data points for the minority class to balance the dataset.
3. Cost-sensitive learning: Adjusting the misclassification costs during the training of the model to give more weight to the minority class can help address imbalanced data.
4. Ensemble methods: Using ensemble methods like bagging, boosting, or stacking can help improve the predictive performance on imbalanced datasets.
5. Anomaly detection: Identifying and treating the minority class as anomalies can help in addressing imbalanced data.
6. Using different evaluation metrics: Instead of using accuracy as the evaluation metric, other metrics such as precision, recall, F1-score, or area under the ROC curve (AUC-ROC) can be more informative when dealing with imbalanced datasets.
These techniques can be used individually or in combination to handle imbalanced data and improve the performance of machine learning models.
1. Resampling: This involves either oversampling the minority class, undersampling the majority class, or a combination of both to create a more balanced dataset.
2. Synthetic data generation: Techniques such as SMOTE (Synthetic Minority Over-sampling Technique) can be used to generate synthetic data points for the minority class to balance the dataset.
3. Cost-sensitive learning: Adjusting the misclassification costs during the training of the model to give more weight to the minority class can help address imbalanced data.
4. Ensemble methods: Using ensemble methods like bagging, boosting, or stacking can help improve the predictive performance on imbalanced datasets.
5. Anomaly detection: Identifying and treating the minority class as anomalies can help in addressing imbalanced data.
6. Using different evaluation metrics: Instead of using accuracy as the evaluation metric, other metrics such as precision, recall, F1-score, or area under the ROC curve (AUC-ROC) can be more informative when dealing with imbalanced datasets.
These techniques can be used individually or in combination to handle imbalanced data and improve the performance of machine learning models.
❤3
AI-agents-for-beginners
10 Lessons to Get Started Building AI Agents
Creator: Microsoft
Stars ⭐️: 16,050
Forked by: 3,926
Github Repo:
https://github.com/microsoft/ai-agents-for-beginners
#github
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
10 Lessons to Get Started Building AI Agents
Creator: Microsoft
Stars ⭐️: 16,050
Forked by: 3,926
Github Repo:
https://github.com/microsoft/ai-agents-for-beginners
#github
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
GitHub
GitHub - microsoft/ai-agents-for-beginners: 11 Lessons to Get Started Building AI Agents
11 Lessons to Get Started Building AI Agents. Contribute to microsoft/ai-agents-for-beginners development by creating an account on GitHub.
❤2👍1
How to master Python from scratch🚀
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
👍3❤1
Guys, Big Announcement!
We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!
I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Here’s what you’ll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic — Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs — Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer script)
- Final Project (Choose, build, and polish your app!)
React with ❤️ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!
I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Here’s what you’ll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic — Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs — Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer script)
- Final Project (Choose, build, and polish your app!)
React with ❤️ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
👍3❤2
Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.me/machinelearning_deeplearning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.me/machinelearning_deeplearning
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Artificial Intelligence
🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data
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10 Steps to Landing a High Paying Job in Data Analytics
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback 😄👍
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback 😄👍
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Important data science topics you should definitely be aware of
1. Statistics & Probability
Descriptive Statistics (mean, median, mode, variance, std deviation)
Probability Distributions (Normal, Binomial, Poisson)
Bayes' Theorem
Hypothesis Testing (t-test, chi-square test, ANOVA)
Confidence Intervals
2. Data Manipulation & Analysis
Data wrangling/cleaning
Handling missing values & outliers
Feature engineering & scaling
GroupBy operations
Pivot tables
Time series manipulation
3. Programming (Python/R)
Data structures (lists, dictionaries, sets)
Libraries:
Python: pandas, NumPy, matplotlib, seaborn, scikit-learn
R: dplyr, ggplot2, caret
Writing reusable functions
Working with APIs & files (CSV, JSON, Excel)
4. Data Visualization
Plot types: bar, line, scatter, histograms, heatmaps, boxplots
Dashboards (Power BI, Tableau, Plotly Dash, Streamlit)
Communicating insights clearly
5. Machine Learning
Supervised Learning
Linear & Logistic Regression
Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM)
SVM, KNN
Unsupervised Learning
K-means Clustering
PCA
Hierarchical Clustering
Model Evaluation
Accuracy, Precision, Recall, F1-Score
Confusion Matrix, ROC-AUC
Cross-validation, Grid Search
6. Deep Learning (Basics)
Neural Networks (perceptron, activation functions)
CNNs, RNNs (just an overview unless you're going deep into DL)
Frameworks: TensorFlow, PyTorch, Keras
7. SQL & Databases
SELECT, WHERE, GROUP BY, JOINS, CTEs, Subqueries
Window functions
Indexes and Query Optimization
8. Big Data & Cloud (Basics)
Hadoop, Spark
AWS, GCP, Azure (basic knowledge of data services)
9. Deployment & MLOps (Basic Awareness)
Model deployment (Flask, FastAPI)
Docker basics
CI/CD pipelines
Model monitoring
10. Business & Domain Knowledge
Framing a problem
Understanding business KPIs
Translating data insights into actionable strategies
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for the detailed explanation on each topic 😄👍
1. Statistics & Probability
Descriptive Statistics (mean, median, mode, variance, std deviation)
Probability Distributions (Normal, Binomial, Poisson)
Bayes' Theorem
Hypothesis Testing (t-test, chi-square test, ANOVA)
Confidence Intervals
2. Data Manipulation & Analysis
Data wrangling/cleaning
Handling missing values & outliers
Feature engineering & scaling
GroupBy operations
Pivot tables
Time series manipulation
3. Programming (Python/R)
Data structures (lists, dictionaries, sets)
Libraries:
Python: pandas, NumPy, matplotlib, seaborn, scikit-learn
R: dplyr, ggplot2, caret
Writing reusable functions
Working with APIs & files (CSV, JSON, Excel)
4. Data Visualization
Plot types: bar, line, scatter, histograms, heatmaps, boxplots
Dashboards (Power BI, Tableau, Plotly Dash, Streamlit)
Communicating insights clearly
5. Machine Learning
Supervised Learning
Linear & Logistic Regression
Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM)
SVM, KNN
Unsupervised Learning
K-means Clustering
PCA
Hierarchical Clustering
Model Evaluation
Accuracy, Precision, Recall, F1-Score
Confusion Matrix, ROC-AUC
Cross-validation, Grid Search
6. Deep Learning (Basics)
Neural Networks (perceptron, activation functions)
CNNs, RNNs (just an overview unless you're going deep into DL)
Frameworks: TensorFlow, PyTorch, Keras
7. SQL & Databases
SELECT, WHERE, GROUP BY, JOINS, CTEs, Subqueries
Window functions
Indexes and Query Optimization
8. Big Data & Cloud (Basics)
Hadoop, Spark
AWS, GCP, Azure (basic knowledge of data services)
9. Deployment & MLOps (Basic Awareness)
Model deployment (Flask, FastAPI)
Docker basics
CI/CD pipelines
Model monitoring
10. Business & Domain Knowledge
Framing a problem
Understanding business KPIs
Translating data insights into actionable strategies
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for the detailed explanation on each topic 😄👍
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🖥 Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
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Essential Data Science Concepts Everyone Should Know:
1. Data Types and Structures:
• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Descriptive Statistics:
• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
• Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
• Outlier Detection and Removal: Identifying and addressing extreme values
• Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
• Data Privacy and Security: Protecting sensitive information
• Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
• R: Statistical programming language with strong visualization capabilities
• SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
• Hadoop and Spark: Frameworks for processing massive datasets
• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
• Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
• Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
1. Data Types and Structures:
• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)
• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)
• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)
2. Descriptive Statistics:
• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)
• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)
• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)
3. Probability and Statistics:
• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)
• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)
• Confidence Intervals: Estimating the range of plausible values for a population parameter
4. Machine Learning:
• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)
• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)
• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)
5. Data Cleaning and Preprocessing:
• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)
• Outlier Detection and Removal: Identifying and addressing extreme values
• Feature Engineering: Creating new features from existing ones (e.g., combining variables)
6. Data Visualization:
• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)
• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)
7. Ethical Considerations in Data Science:
• Data Privacy and Security: Protecting sensitive information
• Bias and Fairness: Ensuring algorithms are unbiased and fair
8. Programming Languages and Tools:
• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn
• R: Statistical programming language with strong visualization capabilities
• SQL: For querying and manipulating data in databases
9. Big Data and Cloud Computing:
• Hadoop and Spark: Frameworks for processing massive datasets
• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)
10. Domain Expertise:
• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis
• Problem Framing: Defining the right questions and objectives for data-driven decision making
Bonus:
• Data Storytelling: Communicating insights and findings in a clear and engaging manner
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
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Essential Skills to Master for Using Generative AI
1️⃣ Prompt Engineering
✍️ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2️⃣ Data Literacy
📊 Understand data sources, biases, and how AI models process information.
3️⃣ AI Ethics & Responsible Usage
⚖️ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4️⃣ Creativity & Critical Thinking
💡 AI enhances creativity, but human intuition is key for quality content.
5️⃣ AI Tool Familiarity
🔍 Get hands-on experience with tools like ChatGPT, DALL·E, Midjourney, and Runway ML.
6️⃣ Coding Basics (Optional)
💻 Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7️⃣ Business & Marketing Awareness
📢 Leverage AI for automation, branding, and customer engagement.
8️⃣ Cybersecurity & Privacy Knowledge
🔐 Learn how AI-generated data can be misused and ways to protect sensitive information.
9️⃣ Adaptability & Continuous Learning
🚀 AI evolves fast—stay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! 🔥
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
1️⃣ Prompt Engineering
✍️ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2️⃣ Data Literacy
📊 Understand data sources, biases, and how AI models process information.
3️⃣ AI Ethics & Responsible Usage
⚖️ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4️⃣ Creativity & Critical Thinking
💡 AI enhances creativity, but human intuition is key for quality content.
5️⃣ AI Tool Familiarity
🔍 Get hands-on experience with tools like ChatGPT, DALL·E, Midjourney, and Runway ML.
6️⃣ Coding Basics (Optional)
💻 Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7️⃣ Business & Marketing Awareness
📢 Leverage AI for automation, branding, and customer engagement.
8️⃣ Cybersecurity & Privacy Knowledge
🔐 Learn how AI-generated data can be misused and ways to protect sensitive information.
9️⃣ Adaptability & Continuous Learning
🚀 AI evolves fast—stay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! 🔥
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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Machine Learning (17.4%)
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)
Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)
Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets
Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)
Statistics and Probability (11.7%)
Concepts: Descriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference
Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)
Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data
Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)
Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)
Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets
Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)
Statistics and Probability (11.7%)
Concepts: Descriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference
Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)
Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data
Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring
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