Top 10 machine Learning algorithms ๐๐
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
๐7โค1
Artificial Intelligence isn't easy!
Itโs the cutting-edge field that enables machines to think, learn, and act like humans.
To truly master Artificial Intelligence, focus on these key areas:
0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.
1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.
2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.
3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.
4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).
5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.
6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.
7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.
8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.
9. Staying Updated with AI Research: AI is an ever-evolving fieldโstay on top of cutting-edge advancements, papers, and new algorithms.
Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.
๐ก Embrace the journey of learning and building systems that can reason, understand, and adapt.
โณ With dedication, hands-on practice, and continuous learning, youโll contribute to shaping the future of intelligent systems!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
Itโs the cutting-edge field that enables machines to think, learn, and act like humans.
To truly master Artificial Intelligence, focus on these key areas:
0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.
1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.
2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.
3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.
4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).
5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.
6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.
7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.
8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.
9. Staying Updated with AI Research: AI is an ever-evolving fieldโstay on top of cutting-edge advancements, papers, and new algorithms.
Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.
๐ก Embrace the journey of learning and building systems that can reason, understand, and adapt.
โณ With dedication, hands-on practice, and continuous learning, youโll contribute to shaping the future of intelligent systems!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
๐11โค2
5 Handy Tips to Master Data Science โฌ๏ธ
1๏ธโฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2๏ธโฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3๏ธโฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4๏ธโฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5๏ธโฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
1๏ธโฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2๏ธโฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3๏ธโฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4๏ธโฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5๏ธโฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
๐2โค1
Prepare for GATE: The Right Time is NOW!
GeeksforGeeks brings you everything you need to crack GATE 2026 โ 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
Whatโs inside?
โ Live & recorded classes with Indiaโs top educators
โ 200+ mock tests to track your progress
โ Study materials - PYQs, workbooks, formula book & more
โ 1:1 mentorship & AI doubt resolution for instant support
โ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav โ Trained 20K+ students
Dr. Khaleel โ Ph.D. in CS, 29+ years of experience
Chandan Jha โ Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal โ M.Tech (NIT), 13+ years of experience
Sakshi Singhal โ IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh โ GATE 99.24 percentile
Devasane Mallesham โ IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
๐ Enroll for a free counseling session now: https://gfgcdn.com/tu/UI2/
GeeksforGeeks brings you everything you need to crack GATE 2026 โ 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
Whatโs inside?
โ Live & recorded classes with Indiaโs top educators
โ 200+ mock tests to track your progress
โ Study materials - PYQs, workbooks, formula book & more
โ 1:1 mentorship & AI doubt resolution for instant support
โ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav โ Trained 20K+ students
Dr. Khaleel โ Ph.D. in CS, 29+ years of experience
Chandan Jha โ Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal โ M.Tech (NIT), 13+ years of experience
Sakshi Singhal โ IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh โ GATE 99.24 percentile
Devasane Mallesham โ IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
๐ Enroll for a free counseling session now: https://gfgcdn.com/tu/UI2/
๐1
Al Terms Everyone SHOULD KNOW!
1. AGI: Al that can think like humans.
2. CoT (Chain of Thought): Al thinking step-by-step.
3. Al Agents: Autonomous programs that make decisions.
4. Al Wrapper: Simplifies interaction with Al models.
5. Al Alignment: Ensuring Al follows human values.
6. Fine-tuning: Improving Al with specific training data.
7. Hallucination: When Al generates false information.
8. Al Model: A trained system for a task.
9. Chatbot: Al that simulates human conversation.
10. Compute: Processing power for Al models.
11. Computer Vision: Al that understands images and videos.
12. Context: Information Al retains for better responses.
13. Deep Learning: Al learning through layered neural networks.
14. Embedding: Numeric representation of words for Al.
15. Explainability: How Al decisions are understood.
16. Foundation Model: Large Al model adaptable to tasks.
17. Generative Al: Al that creates text, images, etc.
18. GPU: Hardware for fast Al processing.
19. Ground Truth: Verified data Al learns from.
20. Inference: Al making predictions on new data.
21. LLM (Large Language Model): Al trained on vast text data.
22. Machine Learning: Al improving from data experience.
23. MCP (Model Context Protocol): Standard for Al external data access.
24. NLP (Natural Language Processing): Al understanding human language.
25. Neural Network: Al model inspired by the brain.
26. Parameters: Al's internal variables for learning.
27. Prompt Engineering: Crafting inputs to guide Al output.
28. Reasoning Model: Al that follows logical thinking.
29. Reinforcement Learning: Al learning from rewards and penalties.
30. RAG (Retrieval-Augmented Generation): Al combining search with responses.
31. Supervised Learning: Al trained on labeled data.
32. TPU: Google's Al-specialized processor.
33. Tokenization: Breaking text into smaller parts.
34. Training: Teaching Al by adjusting its parameters.
35. Transformer: Al architecture for language processing.
36. Unsupervised Learning: Al finding patterns in unlabeled data.
37. Vibe Coding: Al-assisted coding via natural language prompts.
1. AGI: Al that can think like humans.
2. CoT (Chain of Thought): Al thinking step-by-step.
3. Al Agents: Autonomous programs that make decisions.
4. Al Wrapper: Simplifies interaction with Al models.
5. Al Alignment: Ensuring Al follows human values.
6. Fine-tuning: Improving Al with specific training data.
7. Hallucination: When Al generates false information.
8. Al Model: A trained system for a task.
9. Chatbot: Al that simulates human conversation.
10. Compute: Processing power for Al models.
11. Computer Vision: Al that understands images and videos.
12. Context: Information Al retains for better responses.
13. Deep Learning: Al learning through layered neural networks.
14. Embedding: Numeric representation of words for Al.
15. Explainability: How Al decisions are understood.
16. Foundation Model: Large Al model adaptable to tasks.
17. Generative Al: Al that creates text, images, etc.
18. GPU: Hardware for fast Al processing.
19. Ground Truth: Verified data Al learns from.
20. Inference: Al making predictions on new data.
21. LLM (Large Language Model): Al trained on vast text data.
22. Machine Learning: Al improving from data experience.
23. MCP (Model Context Protocol): Standard for Al external data access.
24. NLP (Natural Language Processing): Al understanding human language.
25. Neural Network: Al model inspired by the brain.
26. Parameters: Al's internal variables for learning.
27. Prompt Engineering: Crafting inputs to guide Al output.
28. Reasoning Model: Al that follows logical thinking.
29. Reinforcement Learning: Al learning from rewards and penalties.
30. RAG (Retrieval-Augmented Generation): Al combining search with responses.
31. Supervised Learning: Al trained on labeled data.
32. TPU: Google's Al-specialized processor.
33. Tokenization: Breaking text into smaller parts.
34. Training: Teaching Al by adjusting its parameters.
35. Transformer: Al architecture for language processing.
36. Unsupervised Learning: Al finding patterns in unlabeled data.
37. Vibe Coding: Al-assisted coding via natural language prompts.
๐7โค5
Master AI (Artificial Intelligence) in 10 days ๐๐
#AI
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.
Free Resources: https://t.me/machinelearning_deeplearning
Share for more: https://t.me/datasciencefun
ENJOY LEARNING ๐๐
#AI
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.
Free Resources: https://t.me/machinelearning_deeplearning
Share for more: https://t.me/datasciencefun
ENJOY LEARNING ๐๐
๐2
Are you looking to become a machine learning engineer? ๐ค
The algorithm brought you to the right place! ๐
I created a free and comprehensive roadmap. Letโs go through this thread and explore what you need to know to become an expert machine learning engineer:
๐ Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Hereโs what you need to focus on:
- Basic probability concepts ๐ฒ
- Inferential statistics ๐
- Regression analysis ๐
- Experimental design & A/B testing ๐
- Bayesian statistics ๐ข
- Calculus ๐งฎ
- Linear algebra ๐
๐ 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 โ๏ธ
- Control flow statements (e.g., if-else, loops) ๐
- Functions and modules ๐ง
- Error handling and exceptions โ
- Basic data structures (e.g., lists, dictionaries, tuples) ๐๏ธ
- Object-oriented programming concepts ๐งฑ
- Basic work with APIs ๐
- Detailed data structures and algorithmic thinking ๐ง
๐งช Machine Learning Prerequisites
- Exploratory Data Analysis (EDA) with NumPy and Pandas ๐
- Data visualization techniques to visualize variables ๐
- Feature extraction & engineering ๐ ๏ธ
- Encoding data (different types) ๐
โ๏ธ Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:
- Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees ๐
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering ๐ง
- Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients ๐น๏ธ
Solve two types of problems:
- Regression ๐
- Classification ๐งฉ
๐ง Neural Networks
Neural networks are like computer brains that learn from examples ๐ง , 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 ๐
- Convolutional Neural Networks (CNNs): Great for images, learning visual patterns ๐ผ๏ธ
- Recurrent Neural Networks (RNNs): Good for sequences like text or time series ๐
In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.
๐ธ๏ธ Deep Learning
Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.
- CNNs ๐ผ๏ธ
- RNNs ๐
- LSTMs โณ
๐ 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 ๐๏ธ
- Automated Testing and Continuous Integration (CI) ๐
- Continuous Delivery and Deployment (CD) ๐
- Monitoring and Logging ๐ฅ๏ธ
- Experiment Tracking and Management ๐งช
- Feature Stores ๐๏ธ
- Data Pipeline and Workflow Orchestration ๐ ๏ธ
- Infrastructure as Code (IaC) ๐๏ธ
- Model Serving and APIs ๐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
The algorithm brought you to the right place! ๐
I created a free and comprehensive roadmap. Letโs go through this thread and explore what you need to know to become an expert machine learning engineer:
๐ Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Hereโs what you need to focus on:
- Basic probability concepts ๐ฒ
- Inferential statistics ๐
- Regression analysis ๐
- Experimental design & A/B testing ๐
- Bayesian statistics ๐ข
- Calculus ๐งฎ
- Linear algebra ๐
๐ 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 โ๏ธ
- Control flow statements (e.g., if-else, loops) ๐
- Functions and modules ๐ง
- Error handling and exceptions โ
- Basic data structures (e.g., lists, dictionaries, tuples) ๐๏ธ
- Object-oriented programming concepts ๐งฑ
- Basic work with APIs ๐
- Detailed data structures and algorithmic thinking ๐ง
๐งช Machine Learning Prerequisites
- Exploratory Data Analysis (EDA) with NumPy and Pandas ๐
- Data visualization techniques to visualize variables ๐
- Feature extraction & engineering ๐ ๏ธ
- Encoding data (different types) ๐
โ๏ธ Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:
- Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees ๐
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering ๐ง
- Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients ๐น๏ธ
Solve two types of problems:
- Regression ๐
- Classification ๐งฉ
๐ง Neural Networks
Neural networks are like computer brains that learn from examples ๐ง , 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 ๐
- Convolutional Neural Networks (CNNs): Great for images, learning visual patterns ๐ผ๏ธ
- Recurrent Neural Networks (RNNs): Good for sequences like text or time series ๐
In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.
๐ธ๏ธ Deep Learning
Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.
- CNNs ๐ผ๏ธ
- RNNs ๐
- LSTMs โณ
๐ 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 ๐๏ธ
- Automated Testing and Continuous Integration (CI) ๐
- Continuous Delivery and Deployment (CD) ๐
- Monitoring and Logging ๐ฅ๏ธ
- Experiment Tracking and Management ๐งช
- Feature Stores ๐๏ธ
- Data Pipeline and Workflow Orchestration ๐ ๏ธ
- Infrastructure as Code (IaC) ๐๏ธ
- Model Serving and APIs ๐
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐11โค2๐ฅฐ1
2025 % of all code written by AI = 50%
2026 % of all code written by AI = 100%
This was Anthropic CEO's prediction
I say
Accurate Prediction
2025 % of all code written by AI = 50%
2026 % of all code written by AI = 100%
2027 % of all code written by AI = 75%
2028 a lot of very very expensive senior developers make bank undoing all the garbage written in 2026
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
2026 % of all code written by AI = 100%
This was Anthropic CEO's prediction
I say
Accurate Prediction
2025 % of all code written by AI = 50%
2026 % of all code written by AI = 100%
2027 % of all code written by AI = 75%
2028 a lot of very very expensive senior developers make bank undoing all the garbage written in 2026
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
๐6
AI Agents Course
by Hugging Face ๐ค
This free course will take you on a journey, from beginner to expert, in understanding, using and building AI agents.
https://huggingface.co/learn/agents-course/unit0/introduction
by Hugging Face ๐ค
This free course will take you on a journey, from beginner to expert, in understanding, using and building AI agents.
https://huggingface.co/learn/agents-course/unit0/introduction
๐1
Here are 8 concise tips to help you ace a technical AI engineering interview:
๐ญ. ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐๐ ๐ณ๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐ - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.
๐ฎ. ๐๐ถ๐๐ฐ๐๐๐ ๐ฝ๐ฟ๐ผ๐บ๐ฝ๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.
๐ฏ. ๐ฆ๐ต๐ฎ๐ฟ๐ฒ ๐๐๐ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ฒ๐ ๐ฎ๐บ๐ฝ๐น๐ฒ๐ - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.
๐ฐ. ๐ฆ๐๐ฎ๐ ๐๐ฝ๐ฑ๐ฎ๐๐ฒ๐ฑ ๐ผ๐ป ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.
๐ฑ. ๐๐ถ๐๐ฒ ๐ถ๐ป๐๐ผ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ๐ - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.
๐ฒ. ๐๐ถ๐๐ฐ๐๐๐ ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐พ๐๐ฒ๐ - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.
๐ณ. ๐๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฒ๐ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.
๐ด. ๐๐๐ธ ๐๐ต๐ผ๐๐ด๐ต๐๐ณ๐๐น ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
๐ญ. ๐๐ ๐ฝ๐น๐ฎ๐ถ๐ป ๐๐๐ ๐ณ๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐ - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.
๐ฎ. ๐๐ถ๐๐ฐ๐๐๐ ๐ฝ๐ฟ๐ผ๐บ๐ฝ๐ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.
๐ฏ. ๐ฆ๐ต๐ฎ๐ฟ๐ฒ ๐๐๐ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ฒ๐ ๐ฎ๐บ๐ฝ๐น๐ฒ๐ - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.
๐ฐ. ๐ฆ๐๐ฎ๐ ๐๐ฝ๐ฑ๐ฎ๐๐ฒ๐ฑ ๐ผ๐ป ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.
๐ฑ. ๐๐ถ๐๐ฒ ๐ถ๐ป๐๐ผ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ๐ - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.
๐ฒ. ๐๐ถ๐๐ฐ๐๐๐ ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐พ๐๐ฒ๐ - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.
๐ณ. ๐๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฒ๐ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐ฒ - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.
๐ด. ๐๐๐ธ ๐๐ต๐ผ๐๐ด๐ต๐๐ณ๐๐น ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐ - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
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99% AI startups are just API resellers. ๐
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