If you want to get a job as a machine learning engineer, donโt start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
โค5
Roadmap to become a Data Scientist:
๐ Learn Python & R
โ๐ Learn Statistics & Probability
โ๐ Learn SQL & Data Handling
โ๐ Learn Data Cleaning & Preprocessing
โ๐ Learn Data Visualization (Matplotlib, Seaborn, Power BI/Tableau)
โ๐ Learn Machine Learning (Supervised, Unsupervised)
โ๐ Learn Deep Learning (Neural Nets, CNNs, RNNs)
โ๐ Learn Model Deployment (Flask, Streamlit, FastAPI)
โ๐ Build Real-world Projects & Case Studies
โโ Apply for Jobs & Internships
React โค๏ธ for more
๐ Learn Python & R
โ๐ Learn Statistics & Probability
โ๐ Learn SQL & Data Handling
โ๐ Learn Data Cleaning & Preprocessing
โ๐ Learn Data Visualization (Matplotlib, Seaborn, Power BI/Tableau)
โ๐ Learn Machine Learning (Supervised, Unsupervised)
โ๐ Learn Deep Learning (Neural Nets, CNNs, RNNs)
โ๐ Learn Model Deployment (Flask, Streamlit, FastAPI)
โ๐ Build Real-world Projects & Case Studies
โโ Apply for Jobs & Internships
React โค๏ธ for more
โค12
๐จโ๐The Best Courses for AI from Universities with YouTube Playlists
Stanford University Courses
โขCS221 - Artificial Intelligence: Principles and Techniques
โขCS224U: Natural Language Understanding
โขCS224n - Natural Language Processing with Deep Learning
โขCS229 - Machine Learning
โขCS230 - Deep Learning
โขCS231n - Convolutional Neural Networks for Visual Recognition
โขCS234 - Reinforcement Learning
โขCS330 - Deep Multi-task and Meta-Learning
โขCS25 - Transformers United
Carnegie Mellon University Courses
โขCS 10-708: Probabilistic Graphical Models
โขCS/LTI 11-711: Advanced NLP
โขCS/LTI 11-737: Multilingual NLP
โขCS/LTI 11-747: Neural Networks for NLP
โขCS/LTI 11-785: Introduction to Deep Learning
โขCS/LTI 11-785: Neural Networks
Massachusetts Institute of Technology Courses
โขIntroduction to Algorithms
โขIntroduction to Deep Learning
โข6.S094 - Deep Learning
DeepMind x UCL
โขCOMP M050 - Introduction to Reinforcement Learning
โขDeep Learning Series
Stanford University Courses
โขCS221 - Artificial Intelligence: Principles and Techniques
โขCS224U: Natural Language Understanding
โขCS224n - Natural Language Processing with Deep Learning
โขCS229 - Machine Learning
โขCS230 - Deep Learning
โขCS231n - Convolutional Neural Networks for Visual Recognition
โขCS234 - Reinforcement Learning
โขCS330 - Deep Multi-task and Meta-Learning
โขCS25 - Transformers United
Carnegie Mellon University Courses
โขCS 10-708: Probabilistic Graphical Models
โขCS/LTI 11-711: Advanced NLP
โขCS/LTI 11-737: Multilingual NLP
โขCS/LTI 11-747: Neural Networks for NLP
โขCS/LTI 11-785: Introduction to Deep Learning
โขCS/LTI 11-785: Neural Networks
Massachusetts Institute of Technology Courses
โขIntroduction to Algorithms
โขIntroduction to Deep Learning
โข6.S094 - Deep Learning
DeepMind x UCL
โขCOMP M050 - Introduction to Reinforcement Learning
โขDeep Learning Series
โค4๐1
10 Free Machine Learning Books For 2025
๐ 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
๐ Click Here
๐ 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
๐ Open Book
๐ 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
๐ Click Here
๐ 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
๐ Open Book
๐ 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
๐ Click Here
๐ 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
๐ Open Book
๐ 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
๐ Click Here
๐ 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
๐ Open Book
๐ 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
๐ Click Here
๐ 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
๐ Open Book
Like for more โค๏ธ
๐ 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
๐ Click Here
๐ 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
๐ Open Book
๐ 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
๐ Click Here
๐ 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
๐ Open Book
๐ 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
๐ Click Here
๐ 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
๐ Open Book
๐ 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
๐ Click Here
๐ 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
๐ Open Book
๐ 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
๐ Click Here
๐ 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
๐ Open Book
Like for more โค๏ธ
โค5๐1
Artificial intelligence doesn't make us dumber, it makes us smarter. It presents us with the challenge of asking the right questions. Artificial intelligence doesn't know what we want and that's why it's so incredibly important to develop a specific question for a specific request and that's often harder than you think.
You have to think carefully about what you need to ask the right question that is specific and then use the answer provided by artificial intelligence to solve your problem. This requires a lot of thought, and artificial intelligence helps us to formulate our concerns more precisely and apply the outputs specifically. Using artificial intelligence well and correctly is not a trivial task, but requires some effort.
You have to think carefully about what you need to ask the right question that is specific and then use the answer provided by artificial intelligence to solve your problem. This requires a lot of thought, and artificial intelligence helps us to formulate our concerns more precisely and apply the outputs specifically. Using artificial intelligence well and correctly is not a trivial task, but requires some effort.
โค9๐1
Four best-advanced university courses on NLP & LLM to advance your skills:
1. Advanced NLP -- Carnegie Mellon University
Link: https://lnkd.in/ddEtMghr
2. Recent Advances on Foundation Models -- University of Waterloo
Link: https://lnkd.in/dbdpUV9v
3. Large Language Model Agents -- University of California, Berkeley
Link: https://lnkd.in/d-MdSM8Y
4. Advanced LLM Agent -- University Berkeley
Link: https://lnkd.in/dvCD4HR4
1. Advanced NLP -- Carnegie Mellon University
Link: https://lnkd.in/ddEtMghr
2. Recent Advances on Foundation Models -- University of Waterloo
Link: https://lnkd.in/dbdpUV9v
3. Large Language Model Agents -- University of California, Berkeley
Link: https://lnkd.in/d-MdSM8Y
4. Advanced LLM Agent -- University Berkeley
Link: https://lnkd.in/dvCD4HR4
โค7
Three different learning styles in machine learning algorithms:
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
โค3
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasnโt been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasnโt been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
โค5
Three different learning styles in machine learning algorithms:
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
โค4
๐ง Technologies for Data Science, Machine Learning & AI!
๐ Data Science
โช๏ธ Python โ The go-to language for Data Science
โช๏ธ R โ Statistical Computing and Graphics
โช๏ธ Pandas โ Data Manipulation & Analysis
โช๏ธ NumPy โ Numerical Computing
โช๏ธ Matplotlib / Seaborn โ Data Visualization
โช๏ธ Jupyter Notebooks โ Interactive Development Environment
๐ค Machine Learning
โช๏ธ Scikit-learn โ Classical ML Algorithms
โช๏ธ TensorFlow โ Deep Learning Framework
โช๏ธ Keras โ High-Level Neural Networks API
โช๏ธ PyTorch โ Deep Learning with Dynamic Computation
โช๏ธ XGBoost โ High-Performance Gradient Boosting
โช๏ธ LightGBM โ Fast, Distributed Gradient Boosting
๐ง Artificial Intelligence
โช๏ธ OpenAI GPT โ Natural Language Processing
โช๏ธ Transformers (Hugging Face) โ Pretrained Models for NLP
โช๏ธ spaCy โ Industrial-Strength NLP
โช๏ธ NLTK โ Natural Language Toolkit
โช๏ธ Computer Vision (OpenCV) โ Image Processing & Object Detection
โช๏ธ YOLO (You Only Look Once) โ Real-Time Object Detection
๐พ Data Storage & Databases
โช๏ธ SQL โ Structured Query Language for Databases
โช๏ธ MongoDB โ NoSQL, Flexible Data Storage
โช๏ธ BigQuery โ Googleโs Data Warehouse for Large Scale Data
โช๏ธ Apache Hadoop โ Distributed Storage and Processing
โช๏ธ Apache Spark โ Big Data Processing & ML
๐ Data Engineering & Deployment
โช๏ธ Apache Airflow โ Workflow Automation & Scheduling
โช๏ธ Docker โ Containerization for ML Models
โช๏ธ Kubernetes โ Container Orchestration
โช๏ธ AWS Sagemaker / Google AI Platform โ Cloud ML Model Deployment
โช๏ธ Flask / FastAPI โ APIs for ML Models
๐ง Tools & Libraries for Automation & Experimentation
โช๏ธ MLflow โ Tracking ML Experiments
โช๏ธ TensorBoard โ Visualization for TensorFlow Models
โช๏ธ DVC (Data Version Control) โ Versioning for Data & Models
React โค๏ธ for more
๐ Data Science
โช๏ธ Python โ The go-to language for Data Science
โช๏ธ R โ Statistical Computing and Graphics
โช๏ธ Pandas โ Data Manipulation & Analysis
โช๏ธ NumPy โ Numerical Computing
โช๏ธ Matplotlib / Seaborn โ Data Visualization
โช๏ธ Jupyter Notebooks โ Interactive Development Environment
๐ค Machine Learning
โช๏ธ Scikit-learn โ Classical ML Algorithms
โช๏ธ TensorFlow โ Deep Learning Framework
โช๏ธ Keras โ High-Level Neural Networks API
โช๏ธ PyTorch โ Deep Learning with Dynamic Computation
โช๏ธ XGBoost โ High-Performance Gradient Boosting
โช๏ธ LightGBM โ Fast, Distributed Gradient Boosting
๐ง Artificial Intelligence
โช๏ธ OpenAI GPT โ Natural Language Processing
โช๏ธ Transformers (Hugging Face) โ Pretrained Models for NLP
โช๏ธ spaCy โ Industrial-Strength NLP
โช๏ธ NLTK โ Natural Language Toolkit
โช๏ธ Computer Vision (OpenCV) โ Image Processing & Object Detection
โช๏ธ YOLO (You Only Look Once) โ Real-Time Object Detection
๐พ Data Storage & Databases
โช๏ธ SQL โ Structured Query Language for Databases
โช๏ธ MongoDB โ NoSQL, Flexible Data Storage
โช๏ธ BigQuery โ Googleโs Data Warehouse for Large Scale Data
โช๏ธ Apache Hadoop โ Distributed Storage and Processing
โช๏ธ Apache Spark โ Big Data Processing & ML
๐ Data Engineering & Deployment
โช๏ธ Apache Airflow โ Workflow Automation & Scheduling
โช๏ธ Docker โ Containerization for ML Models
โช๏ธ Kubernetes โ Container Orchestration
โช๏ธ AWS Sagemaker / Google AI Platform โ Cloud ML Model Deployment
โช๏ธ Flask / FastAPI โ APIs for ML Models
๐ง Tools & Libraries for Automation & Experimentation
โช๏ธ MLflow โ Tracking ML Experiments
โช๏ธ TensorBoard โ Visualization for TensorFlow Models
โช๏ธ DVC (Data Version Control) โ Versioning for Data & Models
React โค๏ธ for more
โค8
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ (๐ก๐ผ ๐ฆ๐๐ฟ๐ถ๐ป๐ด๐ ๐๐๐๐ฎ๐ฐ๐ต๐ฒ๐ฑ)
๐ก๐ผ ๐ณ๐ฎ๐ป๐ฐ๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐, ๐ป๐ผ ๐ฐ๐ผ๐ป๐ฑ๐ถ๐๐ถ๐ผ๐ป๐, ๐ท๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.
๐๐ฒ๐ฟ๐ฒโ๐ ๐ต๐ผ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐:
1๏ธโฃ Python Programming for Data Science โ Harvardโs CS50P
The best intro to Python for absolute beginners:
โฌ Covers loops, data structures, and practical exercises.
โฌ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2๏ธโฃ Statistics & Probability โ Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
โฌ Clear, beginner-friendly videos.
โฌ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3๏ธโฃ Linear Algebra for Data Science โ 3Blue1Brown
โฌ Learn about matrices, vectors, and transformations.
โฌ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4๏ธโฃ SQL Basics โ Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
โฌ Writing queries, joins, and filtering data.
โฌ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5๏ธโฃ Data Visualization โ freeCodeCamp
Learn to create stunning visualizations using Python libraries:
โฌ Covers Matplotlib, Seaborn, and Plotly.
โฌ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6๏ธโฃ Machine Learning Basics โ Googleโs Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
โฌ Learn supervised and unsupervised learning.
โฌ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7๏ธโฃ Deep Learning โ Fast.aiโs Free Course
Fast.ai makes deep learning easy and accessible:
โฌ Build neural networks with PyTorch.
โฌ Learn by coding real projects.
Link: https://course.fast.ai/
8๏ธโฃ Data Science Projects โ Kaggle
โฌ Compete in challenges to practice your skills.
โฌ Great way to build your portfolio.
Link: https://www.kaggle.com/
๐ก๐ผ ๐ณ๐ฎ๐ป๐ฐ๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐, ๐ป๐ผ ๐ฐ๐ผ๐ป๐ฑ๐ถ๐๐ถ๐ผ๐ป๐, ๐ท๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.
๐๐ฒ๐ฟ๐ฒโ๐ ๐ต๐ผ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐:
1๏ธโฃ Python Programming for Data Science โ Harvardโs CS50P
The best intro to Python for absolute beginners:
โฌ Covers loops, data structures, and practical exercises.
โฌ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2๏ธโฃ Statistics & Probability โ Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
โฌ Clear, beginner-friendly videos.
โฌ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3๏ธโฃ Linear Algebra for Data Science โ 3Blue1Brown
โฌ Learn about matrices, vectors, and transformations.
โฌ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4๏ธโฃ SQL Basics โ Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
โฌ Writing queries, joins, and filtering data.
โฌ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5๏ธโฃ Data Visualization โ freeCodeCamp
Learn to create stunning visualizations using Python libraries:
โฌ Covers Matplotlib, Seaborn, and Plotly.
โฌ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6๏ธโฃ Machine Learning Basics โ Googleโs Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
โฌ Learn supervised and unsupervised learning.
โฌ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7๏ธโฃ Deep Learning โ Fast.aiโs Free Course
Fast.ai makes deep learning easy and accessible:
โฌ Build neural networks with PyTorch.
โฌ Learn by coding real projects.
Link: https://course.fast.ai/
8๏ธโฃ Data Science Projects โ Kaggle
โฌ Compete in challenges to practice your skills.
โฌ Great way to build your portfolio.
Link: https://www.kaggle.com/
โค3
The Only roadmap you need to become an ML Engineer ๐ฅณ
Phase 1: Foundations (1-2 Months)
๐น Math & Stats Basics โ Linear Algebra, Probability, Statistics
๐น Python Programming โ NumPy, Pandas, Matplotlib, Scikit-Learn
๐น Data Handling โ Cleaning, Feature Engineering, Exploratory Data Analysis
Phase 2: Core Machine Learning (2-3 Months)
๐น Supervised & Unsupervised Learning โ Regression, Classification, Clustering
๐น Model Evaluation โ Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
๐น Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization
๐น Basic ML Projects โ Predict house prices, customer segmentation
Phase 3: Deep Learning & Advanced ML (2-3 Months)
๐น Neural Networks โ TensorFlow & PyTorch Basics
๐น CNNs & Image Processing โ Object Detection, Image Classification
๐น NLP & Transformers โ Sentiment Analysis, BERT, LLMs (GPT, Gemini)
๐น Reinforcement Learning Basics โ Q-learning, Policy Gradient
Phase 4: ML System Design & MLOps (2-3 Months)
๐น ML in Production โ Model Deployment (Flask, FastAPI, Docker)
๐น MLOps โ CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
๐น Cloud & Big Data โ AWS/GCP/Azure, Spark, Kafka
๐น End-to-End ML Projects โ Fraud detection, Recommendation systems
Phase 5: Specialization & Job Readiness (Ongoing)
๐น Specialize โ Computer Vision, NLP, Generative AI, Edge AI
๐น Interview Prep โ Leetcode for ML, System Design, ML Case Studies
๐น Portfolio Building โ GitHub, Kaggle Competitions, Writing Blogs
๐น Networking โ Contribute to open-source, Attend ML meetups, LinkedIn presence
The data field is vast, offering endless opportunities so start preparing now.
Phase 1: Foundations (1-2 Months)
๐น Math & Stats Basics โ Linear Algebra, Probability, Statistics
๐น Python Programming โ NumPy, Pandas, Matplotlib, Scikit-Learn
๐น Data Handling โ Cleaning, Feature Engineering, Exploratory Data Analysis
Phase 2: Core Machine Learning (2-3 Months)
๐น Supervised & Unsupervised Learning โ Regression, Classification, Clustering
๐น Model Evaluation โ Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
๐น Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization
๐น Basic ML Projects โ Predict house prices, customer segmentation
Phase 3: Deep Learning & Advanced ML (2-3 Months)
๐น Neural Networks โ TensorFlow & PyTorch Basics
๐น CNNs & Image Processing โ Object Detection, Image Classification
๐น NLP & Transformers โ Sentiment Analysis, BERT, LLMs (GPT, Gemini)
๐น Reinforcement Learning Basics โ Q-learning, Policy Gradient
Phase 4: ML System Design & MLOps (2-3 Months)
๐น ML in Production โ Model Deployment (Flask, FastAPI, Docker)
๐น MLOps โ CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
๐น Cloud & Big Data โ AWS/GCP/Azure, Spark, Kafka
๐น End-to-End ML Projects โ Fraud detection, Recommendation systems
Phase 5: Specialization & Job Readiness (Ongoing)
๐น Specialize โ Computer Vision, NLP, Generative AI, Edge AI
๐น Interview Prep โ Leetcode for ML, System Design, ML Case Studies
๐น Portfolio Building โ GitHub, Kaggle Competitions, Writing Blogs
๐น Networking โ Contribute to open-source, Attend ML meetups, LinkedIn presence
The data field is vast, offering endless opportunities so start preparing now.
โค4