If you want to get a job as a machine learning engineer, don’t 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:
𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - 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
𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - 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.
𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - 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/
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
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.
𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
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 👍👍
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:
𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - 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
𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - 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.
𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - 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/
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
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.
𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
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 👍👍
❤5
Roadmap to become a Data Scientist:
📂 Learn Python & R
∟📂 Learn Statistics & Probability
∟📂 Learn SQL & Data Handling
∟📂 Learn Data Cleaning & Preprocessing
∟📂 Learn Data Visualization (Matplotlib, Seaborn, Power BI/Tableau)
∟📂 Learn Machine Learning (Supervised, Unsupervised)
∟📂 Learn Deep Learning (Neural Nets, CNNs, RNNs)
∟📂 Learn Model Deployment (Flask, Streamlit, FastAPI)
∟📂 Build Real-world Projects & Case Studies
∟✅ Apply for Jobs & Internships
React ❤️ for more
📂 Learn Python & R
∟📂 Learn Statistics & Probability
∟📂 Learn SQL & Data Handling
∟📂 Learn Data Cleaning & Preprocessing
∟📂 Learn Data Visualization (Matplotlib, Seaborn, Power BI/Tableau)
∟📂 Learn Machine Learning (Supervised, Unsupervised)
∟📂 Learn Deep Learning (Neural Nets, CNNs, RNNs)
∟📂 Learn Model Deployment (Flask, Streamlit, FastAPI)
∟📂 Build Real-world Projects & Case Studies
∟✅ Apply for Jobs & Internships
React ❤️ for more
❤12
👨🎓The Best Courses for AI from Universities with YouTube Playlists
Stanford University Courses
•CS221 - Artificial Intelligence: Principles and Techniques
•CS224U: Natural Language Understanding
•CS224n - Natural Language Processing with Deep Learning
•CS229 - Machine Learning
•CS230 - Deep Learning
•CS231n - Convolutional Neural Networks for Visual Recognition
•CS234 - Reinforcement Learning
•CS330 - Deep Multi-task and Meta-Learning
•CS25 - Transformers United
Carnegie Mellon University Courses
•CS 10-708: Probabilistic Graphical Models
•CS/LTI 11-711: Advanced NLP
•CS/LTI 11-737: Multilingual NLP
•CS/LTI 11-747: Neural Networks for NLP
•CS/LTI 11-785: Introduction to Deep Learning
•CS/LTI 11-785: Neural Networks
Massachusetts Institute of Technology Courses
•Introduction to Algorithms
•Introduction to Deep Learning
•6.S094 - Deep Learning
DeepMind x UCL
•COMP M050 - Introduction to Reinforcement Learning
•Deep Learning Series
Stanford University Courses
•CS221 - Artificial Intelligence: Principles and Techniques
•CS224U: Natural Language Understanding
•CS224n - Natural Language Processing with Deep Learning
•CS229 - Machine Learning
•CS230 - Deep Learning
•CS231n - Convolutional Neural Networks for Visual Recognition
•CS234 - Reinforcement Learning
•CS330 - Deep Multi-task and Meta-Learning
•CS25 - Transformers United
Carnegie Mellon University Courses
•CS 10-708: Probabilistic Graphical Models
•CS/LTI 11-711: Advanced NLP
•CS/LTI 11-737: Multilingual NLP
•CS/LTI 11-747: Neural Networks for NLP
•CS/LTI 11-785: Introduction to Deep Learning
•CS/LTI 11-785: Neural Networks
Massachusetts Institute of Technology Courses
•Introduction to Algorithms
•Introduction to Deep Learning
•6.S094 - Deep Learning
DeepMind x UCL
•COMP M050 - Introduction to Reinforcement Learning
•Deep Learning Series
❤4👍1
10 Free Machine Learning Books For 2025
📘 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
🔘 Click Here
📙 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
🔘 Open Book
📗 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
🔘 Click Here
📕 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
🔘 Open Book
📘 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
🔘 Click Here
📙 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
🔘 Open Book
📗 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
🔘 Click Here
📕 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
🔘 Open Book
📘 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
🔘 Click Here
📙 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
🔘 Open Book
Like for more ❤️
📘 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
🔘 Click Here
📙 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
🔘 Open Book
📗 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
🔘 Click Here
📕 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
🔘 Open Book
📘 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
🔘 Click Here
📙 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
🔘 Open Book
📗 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
🔘 Click Here
📕 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
🔘 Open Book
📘 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
🔘 Click Here
📙 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
🔘 Open Book
Like for more ❤️
❤5👍1
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.
❤9👍1
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
❤7
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.
❤3
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 hasn’t 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 hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
❤5
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.
❤4
🧠 Technologies for Data Science, Machine Learning & AI!
📊 Data Science
▪️ Python – The go-to language for Data Science
▪️ R – Statistical Computing and Graphics
▪️ Pandas – Data Manipulation & Analysis
▪️ NumPy – Numerical Computing
▪️ Matplotlib / Seaborn – Data Visualization
▪️ Jupyter Notebooks – Interactive Development Environment
🤖 Machine Learning
▪️ Scikit-learn – Classical ML Algorithms
▪️ TensorFlow – Deep Learning Framework
▪️ Keras – High-Level Neural Networks API
▪️ PyTorch – Deep Learning with Dynamic Computation
▪️ XGBoost – High-Performance Gradient Boosting
▪️ LightGBM – Fast, Distributed Gradient Boosting
🧠 Artificial Intelligence
▪️ OpenAI GPT – Natural Language Processing
▪️ Transformers (Hugging Face) – Pretrained Models for NLP
▪️ spaCy – Industrial-Strength NLP
▪️ NLTK – Natural Language Toolkit
▪️ Computer Vision (OpenCV) – Image Processing & Object Detection
▪️ YOLO (You Only Look Once) – Real-Time Object Detection
💾 Data Storage & Databases
▪️ SQL – Structured Query Language for Databases
▪️ MongoDB – NoSQL, Flexible Data Storage
▪️ BigQuery – Google’s Data Warehouse for Large Scale Data
▪️ Apache Hadoop – Distributed Storage and Processing
▪️ Apache Spark – Big Data Processing & ML
🌐 Data Engineering & Deployment
▪️ Apache Airflow – Workflow Automation & Scheduling
▪️ Docker – Containerization for ML Models
▪️ Kubernetes – Container Orchestration
▪️ AWS Sagemaker / Google AI Platform – Cloud ML Model Deployment
▪️ Flask / FastAPI – APIs for ML Models
🔧 Tools & Libraries for Automation & Experimentation
▪️ MLflow – Tracking ML Experiments
▪️ TensorBoard – Visualization for TensorFlow Models
▪️ DVC (Data Version Control) – Versioning for Data & Models
React ❤️ for more
📊 Data Science
▪️ Python – The go-to language for Data Science
▪️ R – Statistical Computing and Graphics
▪️ Pandas – Data Manipulation & Analysis
▪️ NumPy – Numerical Computing
▪️ Matplotlib / Seaborn – Data Visualization
▪️ Jupyter Notebooks – Interactive Development Environment
🤖 Machine Learning
▪️ Scikit-learn – Classical ML Algorithms
▪️ TensorFlow – Deep Learning Framework
▪️ Keras – High-Level Neural Networks API
▪️ PyTorch – Deep Learning with Dynamic Computation
▪️ XGBoost – High-Performance Gradient Boosting
▪️ LightGBM – Fast, Distributed Gradient Boosting
🧠 Artificial Intelligence
▪️ OpenAI GPT – Natural Language Processing
▪️ Transformers (Hugging Face) – Pretrained Models for NLP
▪️ spaCy – Industrial-Strength NLP
▪️ NLTK – Natural Language Toolkit
▪️ Computer Vision (OpenCV) – Image Processing & Object Detection
▪️ YOLO (You Only Look Once) – Real-Time Object Detection
💾 Data Storage & Databases
▪️ SQL – Structured Query Language for Databases
▪️ MongoDB – NoSQL, Flexible Data Storage
▪️ BigQuery – Google’s Data Warehouse for Large Scale Data
▪️ Apache Hadoop – Distributed Storage and Processing
▪️ Apache Spark – Big Data Processing & ML
🌐 Data Engineering & Deployment
▪️ Apache Airflow – Workflow Automation & Scheduling
▪️ Docker – Containerization for ML Models
▪️ Kubernetes – Container Orchestration
▪️ AWS Sagemaker / Google AI Platform – Cloud ML Model Deployment
▪️ Flask / FastAPI – APIs for ML Models
🔧 Tools & Libraries for Automation & Experimentation
▪️ MLflow – Tracking ML Experiments
▪️ TensorBoard – Visualization for TensorFlow Models
▪️ DVC (Data Version Control) – Versioning for Data & Models
React ❤️ for more
❤8
𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 (𝗡𝗼 𝗦𝘁𝗿𝗶𝗻𝗴𝘀 𝗔𝘁𝘁𝗮𝗰𝗵𝗲𝗱)
𝗡𝗼 𝗳𝗮𝗻𝗰𝘆 𝗰𝗼𝘂𝗿𝘀𝗲𝘀, 𝗻𝗼 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀, 𝗷𝘂𝘀𝘁 𝗽𝘂𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.
𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘:
1️⃣ Python Programming for Data Science → Harvard’s CS50P
The best intro to Python for absolute beginners:
↬ Covers loops, data structures, and practical exercises.
↬ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://news.1rj.ru/str/datasciencefun
2️⃣ Statistics & Probability → Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
↬ Clear, beginner-friendly videos.
↬ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3️⃣ Linear Algebra for Data Science → 3Blue1Brown
↬ Learn about matrices, vectors, and transformations.
↬ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4️⃣ SQL Basics → Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
↬ Writing queries, joins, and filtering data.
↬ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5️⃣ Data Visualization → freeCodeCamp
Learn to create stunning visualizations using Python libraries:
↬ Covers Matplotlib, Seaborn, and Plotly.
↬ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6️⃣ Machine Learning Basics → Google’s Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
↬ Learn supervised and unsupervised learning.
↬ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7️⃣ Deep Learning → Fast.ai’s Free Course
Fast.ai makes deep learning easy and accessible:
↬ Build neural networks with PyTorch.
↬ Learn by coding real projects.
Link: https://course.fast.ai/
8️⃣ Data Science Projects → Kaggle
↬ Compete in challenges to practice your skills.
↬ Great way to build your portfolio.
Link: https://www.kaggle.com/
𝗡𝗼 𝗳𝗮𝗻𝗰𝘆 𝗰𝗼𝘂𝗿𝘀𝗲𝘀, 𝗻𝗼 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀, 𝗷𝘂𝘀𝘁 𝗽𝘂𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.
𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘:
1️⃣ Python Programming for Data Science → Harvard’s CS50P
The best intro to Python for absolute beginners:
↬ Covers loops, data structures, and practical exercises.
↬ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://news.1rj.ru/str/datasciencefun
2️⃣ Statistics & Probability → Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
↬ Clear, beginner-friendly videos.
↬ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3️⃣ Linear Algebra for Data Science → 3Blue1Brown
↬ Learn about matrices, vectors, and transformations.
↬ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4️⃣ SQL Basics → Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
↬ Writing queries, joins, and filtering data.
↬ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5️⃣ Data Visualization → freeCodeCamp
Learn to create stunning visualizations using Python libraries:
↬ Covers Matplotlib, Seaborn, and Plotly.
↬ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6️⃣ Machine Learning Basics → Google’s Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
↬ Learn supervised and unsupervised learning.
↬ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7️⃣ Deep Learning → Fast.ai’s Free Course
Fast.ai makes deep learning easy and accessible:
↬ Build neural networks with PyTorch.
↬ Learn by coding real projects.
Link: https://course.fast.ai/
8️⃣ Data Science Projects → Kaggle
↬ Compete in challenges to practice your skills.
↬ Great way to build your portfolio.
Link: https://www.kaggle.com/
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The Only roadmap you need to become an ML Engineer 🥳
Phase 1: Foundations (1-2 Months)
🔹 Math & Stats Basics – Linear Algebra, Probability, Statistics
🔹 Python Programming – NumPy, Pandas, Matplotlib, Scikit-Learn
🔹 Data Handling – Cleaning, Feature Engineering, Exploratory Data Analysis
Phase 2: Core Machine Learning (2-3 Months)
🔹 Supervised & Unsupervised Learning – Regression, Classification, Clustering
🔹 Model Evaluation – Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
🔹 Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization
🔹 Basic ML Projects – Predict house prices, customer segmentation
Phase 3: Deep Learning & Advanced ML (2-3 Months)
🔹 Neural Networks – TensorFlow & PyTorch Basics
🔹 CNNs & Image Processing – Object Detection, Image Classification
🔹 NLP & Transformers – Sentiment Analysis, BERT, LLMs (GPT, Gemini)
🔹 Reinforcement Learning Basics – Q-learning, Policy Gradient
Phase 4: ML System Design & MLOps (2-3 Months)
🔹 ML in Production – Model Deployment (Flask, FastAPI, Docker)
🔹 MLOps – CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
🔹 Cloud & Big Data – AWS/GCP/Azure, Spark, Kafka
🔹 End-to-End ML Projects – Fraud detection, Recommendation systems
Phase 5: Specialization & Job Readiness (Ongoing)
🔹 Specialize – Computer Vision, NLP, Generative AI, Edge AI
🔹 Interview Prep – Leetcode for ML, System Design, ML Case Studies
🔹 Portfolio Building – GitHub, Kaggle Competitions, Writing Blogs
🔹 Networking – 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)
🔹 Math & Stats Basics – Linear Algebra, Probability, Statistics
🔹 Python Programming – NumPy, Pandas, Matplotlib, Scikit-Learn
🔹 Data Handling – Cleaning, Feature Engineering, Exploratory Data Analysis
Phase 2: Core Machine Learning (2-3 Months)
🔹 Supervised & Unsupervised Learning – Regression, Classification, Clustering
🔹 Model Evaluation – Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
🔹 Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization
🔹 Basic ML Projects – Predict house prices, customer segmentation
Phase 3: Deep Learning & Advanced ML (2-3 Months)
🔹 Neural Networks – TensorFlow & PyTorch Basics
🔹 CNNs & Image Processing – Object Detection, Image Classification
🔹 NLP & Transformers – Sentiment Analysis, BERT, LLMs (GPT, Gemini)
🔹 Reinforcement Learning Basics – Q-learning, Policy Gradient
Phase 4: ML System Design & MLOps (2-3 Months)
🔹 ML in Production – Model Deployment (Flask, FastAPI, Docker)
🔹 MLOps – CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
🔹 Cloud & Big Data – AWS/GCP/Azure, Spark, Kafka
🔹 End-to-End ML Projects – Fraud detection, Recommendation systems
Phase 5: Specialization & Job Readiness (Ongoing)
🔹 Specialize – Computer Vision, NLP, Generative AI, Edge AI
🔹 Interview Prep – Leetcode for ML, System Design, ML Case Studies
🔹 Portfolio Building – GitHub, Kaggle Competitions, Writing Blogs
🔹 Networking – Contribute to open-source, Attend ML meetups, LinkedIn presence
The data field is vast, offering endless opportunities so start preparing now.
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