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/
❤3
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.
❤4
How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://news.1rj.ru/str/machinelearning_deeplearning
Like for more ❤️
All the best 👍👍
Where do you go to learn these skills? What courses are the best?
There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.
What’s more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.
Join for more: https://news.1rj.ru/str/machinelearning_deeplearning
Like for more ❤️
All the best 👍👍
❤7
Machine Learning isn't easy!
It’s the field that powers intelligent systems and predictive models.
To truly master Machine Learning, focus on these key areas:
0. Understanding the Basics of Algorithms: Learn about linear regression, decision trees, and k-nearest neighbors to build a solid foundation.
1. Mastering Data Preprocessing: Clean, normalize, and handle missing data to prepare your datasets for training.
2. Learning Supervised Learning Techniques: Dive deep into classification and regression models, such as SVMs, random forests, and logistic regression.
3. Exploring Unsupervised Learning: Understand clustering techniques (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE).
4. Mastering Model Evaluation: Use techniques like cross-validation, confusion matrices, ROC curves, and F1 scores to assess model performance.
5. Understanding Overfitting and Underfitting: Learn how to balance bias and variance to build robust models.
6. Optimizing Hyperparameters: Use grid search, random search, and Bayesian optimization to fine-tune your models for better performance.
7. Diving into Neural Networks and Deep Learning: Explore deep learning with frameworks like TensorFlow and PyTorch to create advanced models like CNNs and RNNs.
8. Working with Natural Language Processing (NLP): Master text data, sentiment analysis, and techniques like word embeddings and transformers.
9. Staying Updated with New Techniques: Machine learning evolves rapidly—keep up with emerging models, techniques, and research.
Machine learning is about learning from data and improving models over time.
💡 Embrace the challenges of building algorithms, experimenting with data, and solving complex problems.
⏳ With time, practice, and persistence, you’ll develop the expertise to create systems that learn, predict, and adapt.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
#datascience
It’s the field that powers intelligent systems and predictive models.
To truly master Machine Learning, focus on these key areas:
0. Understanding the Basics of Algorithms: Learn about linear regression, decision trees, and k-nearest neighbors to build a solid foundation.
1. Mastering Data Preprocessing: Clean, normalize, and handle missing data to prepare your datasets for training.
2. Learning Supervised Learning Techniques: Dive deep into classification and regression models, such as SVMs, random forests, and logistic regression.
3. Exploring Unsupervised Learning: Understand clustering techniques (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE).
4. Mastering Model Evaluation: Use techniques like cross-validation, confusion matrices, ROC curves, and F1 scores to assess model performance.
5. Understanding Overfitting and Underfitting: Learn how to balance bias and variance to build robust models.
6. Optimizing Hyperparameters: Use grid search, random search, and Bayesian optimization to fine-tune your models for better performance.
7. Diving into Neural Networks and Deep Learning: Explore deep learning with frameworks like TensorFlow and PyTorch to create advanced models like CNNs and RNNs.
8. Working with Natural Language Processing (NLP): Master text data, sentiment analysis, and techniques like word embeddings and transformers.
9. Staying Updated with New Techniques: Machine learning evolves rapidly—keep up with emerging models, techniques, and research.
Machine learning is about learning from data and improving models over time.
💡 Embrace the challenges of building algorithms, experimenting with data, and solving complex problems.
⏳ With time, practice, and persistence, you’ll develop the expertise to create systems that learn, predict, and adapt.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
#datascience
❤3