🏆 – AI/ML Engineer
Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects
Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects
👍7
Don't overwhelm to learn Git,🙌
Git is only this much👇😇
1.Core:
• git init
• git clone
• git add
• git commit
• git status
• git diff
• git checkout
• git reset
• git log
• git show
• git tag
• git push
• git pull
2.Branching:
• git branch
• git checkout -b
• git merge
• git rebase
• git branch --set-upstream-to
• git branch --unset-upstream
• git cherry-pick
3.Merging:
• git merge
• git rebase
4.Stashing:
• git stash
• git stash pop
• git stash list
• git stash apply
• git stash drop
5.Remotes:
• git remote
• git remote add
• git remote remove
• git fetch
• git pull
• git push
• git clone --mirror
6.Configuration:
• git config
• git global config
• git reset config
7. Plumbing:
• git cat-file
• git checkout-index
• git commit-tree
• git diff-tree
• git for-each-ref
• git hash-object
• git ls-files
• git ls-remote
• git merge-tree
• git read-tree
• git rev-parse
• git show-branch
• git show-ref
• git symbolic-ref
• git tag --list
• git update-ref
8.Porcelain:
• git blame
• git bisect
• git checkout
• git commit
• git diff
• git fetch
• git grep
• git log
• git merge
• git push
• git rebase
• git reset
• git show
• git tag
9.Alias:
• git config --global alias.<alias> <command>
10.Hook:
• git config --local core.hooksPath <path>
✅ Best Telegram channels to get free coding & data science resources
https://t.me/addlist/4q2PYC0pH_VjZDk5
✅ Free Courses with Certificate:
https://t.me/free4unow_backup
Git is only this much👇😇
1.Core:
• git init
• git clone
• git add
• git commit
• git status
• git diff
• git checkout
• git reset
• git log
• git show
• git tag
• git push
• git pull
2.Branching:
• git branch
• git checkout -b
• git merge
• git rebase
• git branch --set-upstream-to
• git branch --unset-upstream
• git cherry-pick
3.Merging:
• git merge
• git rebase
4.Stashing:
• git stash
• git stash pop
• git stash list
• git stash apply
• git stash drop
5.Remotes:
• git remote
• git remote add
• git remote remove
• git fetch
• git pull
• git push
• git clone --mirror
6.Configuration:
• git config
• git global config
• git reset config
7. Plumbing:
• git cat-file
• git checkout-index
• git commit-tree
• git diff-tree
• git for-each-ref
• git hash-object
• git ls-files
• git ls-remote
• git merge-tree
• git read-tree
• git rev-parse
• git show-branch
• git show-ref
• git symbolic-ref
• git tag --list
• git update-ref
8.Porcelain:
• git blame
• git bisect
• git checkout
• git commit
• git diff
• git fetch
• git grep
• git log
• git merge
• git push
• git rebase
• git reset
• git show
• git tag
9.Alias:
• git config --global alias.<alias> <command>
10.Hook:
• git config --local core.hooksPath <path>
✅ Best Telegram channels to get free coding & data science resources
https://t.me/addlist/4q2PYC0pH_VjZDk5
✅ Free Courses with Certificate:
https://t.me/free4unow_backup
👍9❤2😁1🤯1
Keep yourself updated with Artificial Intelligence & latest technology
👇👇
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
👇👇
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Top 10 machine Learning algorithms 👇👇
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 😄👍
Hope this helps you 😊
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 😄👍
Hope this helps you 😊
👍7❤1
Artificial Intelligence isn't easy!
It’s 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 field—stay on top of cutting-edge advancements, papers, and new algorithms.
Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.
💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.
⏳ With dedication, hands-on practice, and continuous learning, you’ll 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 😄👍
Hope this helps you 😊
#ai #datascience
It’s 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 field—stay on top of cutting-edge advancements, papers, and new algorithms.
Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.
💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.
⏳ With dedication, hands-on practice, and continuous learning, you’ll 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 😄👍
Hope this helps you 😊
#ai #datascience
👍11❤2
5 Handy Tips to Master Data Science ⬇️
1️⃣ 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
2️⃣ 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.
3️⃣ 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.
4️⃣ 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.
5️⃣ 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.
1️⃣ 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
2️⃣ 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.
3️⃣ 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.
4️⃣ 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.
5️⃣ 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.
👍2❤1
Prepare for GATE: The Right Time is NOW!
GeeksforGeeks brings you everything you need to crack GATE 2026 – 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
What’s inside?
✔ Live & recorded classes with India’s top educators
✔ 200+ mock tests to track your progress
✔ Study materials - PYQs, workbooks, formula book & more
✔ 1:1 mentorship & AI doubt resolution for instant support
✔ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav – Trained 20K+ students
Dr. Khaleel – Ph.D. in CS, 29+ years of experience
Chandan Jha – Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal – M.Tech (NIT), 13+ years of experience
Sakshi Singhal – IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh – GATE 99.24 percentile
Devasane Mallesham – IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
📌 Enroll for a free counseling session now: https://gfgcdn.com/tu/UI2/
GeeksforGeeks brings you everything you need to crack GATE 2026 – 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
What’s inside?
✔ Live & recorded classes with India’s top educators
✔ 200+ mock tests to track your progress
✔ Study materials - PYQs, workbooks, formula book & more
✔ 1:1 mentorship & AI doubt resolution for instant support
✔ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav – Trained 20K+ students
Dr. Khaleel – Ph.D. in CS, 29+ years of experience
Chandan Jha – Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal – M.Tech (NIT), 13+ years of experience
Sakshi Singhal – IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh – GATE 99.24 percentile
Devasane Mallesham – IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
📌 Enroll for a free counseling session now: https://gfgcdn.com/tu/UI2/
👍1