Are you looking to become a machine learning engineer? The algorithm brought you to the right place! ๐
I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, itโs the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, itโs the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
๐6โค1๐ฅฐ1
10 Machine Learning Concepts You Must Know
1. Supervised vs Unsupervised Learning
Supervised Learning involves training a model on labeled data (input-output pairs). Examples: Linear Regression, Classification.
Unsupervised Learning deals with unlabeled data. The model tries to find hidden patterns or groupings. Examples: Clustering (K-Means), Dimensionality Reduction (PCA).
2. Bias-Variance Tradeoff
Bias is the error due to overly simplistic assumptions in the learning algorithm.
Variance is the error due to excessive sensitivity to small fluctuations in the training data.
Goal: Minimize both for optimal model performance. High bias โ underfitting; High variance โ overfitting.
3. Feature Engineering
The process of selecting, transforming, and creating variables (features) to improve model performance.
Examples: Normalization, encoding categorical variables, creating interaction terms, handling missing data.
4. Train-Test Split & Cross-Validation
Train-Test Split divides the dataset into training and testing subsets to evaluate model generalization.
Cross-Validation (e.g., k-fold) provides a more reliable evaluation by splitting data into k subsets and training/testing on each.
5. Confusion Matrix
A performance evaluation tool for classification models showing TP, TN, FP, FN.
From it, we derive:
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
6. Gradient Descent
An optimization algorithm used to minimize the cost/loss function by iteratively updating model parameters in the direction of the negative gradient.
Variants: Batch GD, Stochastic GD (SGD), Mini-batch GD.
7. Regularization (L1/L2)
Techniques to prevent overfitting by adding a penalty term to the loss function.
L1 (Lasso): Adds absolute value of coefficients, can shrink some to zero (feature selection).
L2 (Ridge): Adds square of coefficients, tends to shrink but not eliminate coefficients.
8. Decision Trees & Random Forests
Decision Tree: A tree-structured model that splits data based on features. Easy to interpret.
Random Forest: An ensemble of decision trees; reduces overfitting and improves accuracy.
9. Support Vector Machines (SVM)
A supervised learning algorithm used for classification. It finds the optimal hyperplane that separates classes.
Uses kernels (linear, polynomial, RBF) to handle non-linearly separable data.
10. Neural Networks
Inspired by the human brain, these consist of layers of interconnected neurons.
Deep Neural Networks (DNNs) can model complex patterns.
The backbone of deep learning applications like image recognition, NLP, etc.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
1. Supervised vs Unsupervised Learning
Supervised Learning involves training a model on labeled data (input-output pairs). Examples: Linear Regression, Classification.
Unsupervised Learning deals with unlabeled data. The model tries to find hidden patterns or groupings. Examples: Clustering (K-Means), Dimensionality Reduction (PCA).
2. Bias-Variance Tradeoff
Bias is the error due to overly simplistic assumptions in the learning algorithm.
Variance is the error due to excessive sensitivity to small fluctuations in the training data.
Goal: Minimize both for optimal model performance. High bias โ underfitting; High variance โ overfitting.
3. Feature Engineering
The process of selecting, transforming, and creating variables (features) to improve model performance.
Examples: Normalization, encoding categorical variables, creating interaction terms, handling missing data.
4. Train-Test Split & Cross-Validation
Train-Test Split divides the dataset into training and testing subsets to evaluate model generalization.
Cross-Validation (e.g., k-fold) provides a more reliable evaluation by splitting data into k subsets and training/testing on each.
5. Confusion Matrix
A performance evaluation tool for classification models showing TP, TN, FP, FN.
From it, we derive:
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
6. Gradient Descent
An optimization algorithm used to minimize the cost/loss function by iteratively updating model parameters in the direction of the negative gradient.
Variants: Batch GD, Stochastic GD (SGD), Mini-batch GD.
7. Regularization (L1/L2)
Techniques to prevent overfitting by adding a penalty term to the loss function.
L1 (Lasso): Adds absolute value of coefficients, can shrink some to zero (feature selection).
L2 (Ridge): Adds square of coefficients, tends to shrink but not eliminate coefficients.
8. Decision Trees & Random Forests
Decision Tree: A tree-structured model that splits data based on features. Easy to interpret.
Random Forest: An ensemble of decision trees; reduces overfitting and improves accuracy.
9. Support Vector Machines (SVM)
A supervised learning algorithm used for classification. It finds the optimal hyperplane that separates classes.
Uses kernels (linear, polynomial, RBF) to handle non-linearly separable data.
10. Neural Networks
Inspired by the human brain, these consist of layers of interconnected neurons.
Deep Neural Networks (DNNs) can model complex patterns.
The backbone of deep learning applications like image recognition, NLP, etc.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
๐5โค2๐ฅฐ1
Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting , F1 Score
1. What do you understand by the term silhouette coefficient?
The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.
2. What is the difference between trend and seasonality in time series?
Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metricโs value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.
3. What is Bag of Words in NLP?
Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.
4. What is the difference between bagging and boosting?
Bagging is a homogeneous weak learnersโ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learnersโ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm
5. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting , F1 Score
1. What do you understand by the term silhouette coefficient?
The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.
2. What is the difference between trend and seasonality in time series?
Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metricโs value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.
3. What is Bag of Words in NLP?
Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.
4. What is the difference between bagging and boosting?
Bagging is a homogeneous weak learnersโ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learnersโ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm
5. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
๐2
Guys, Big Announcement!
Weโve officially hit 2 MILLION followers โ and itโs time to take our Python journey to the next level!
Iโm super excited to launch the 30-Day Python Coding Challenge โ perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python โ bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Hereโs what youโll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic โ Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs โ Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer script)
- Final Project (Choose, build, and polish your app!)
React with โค๏ธ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
Weโve officially hit 2 MILLION followers โ and itโs time to take our Python journey to the next level!
Iโm super excited to launch the 30-Day Python Coding Challenge โ perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python โ bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Hereโs what youโll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic โ Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs โ Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer script)
- Final Project (Choose, build, and polish your app!)
React with โค๏ธ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
โค7๐1
You know what DOESN'T matter?
How you got started in data.
Maybe you focused on a single tool.
Maybe you learned Python before SQL.
Maybe you thought you needed to know R.
Maybe you only know Excel and that's all you need.
Maybe you tried Power BI before deciding on Tableau.
It doesn't matter how you get started - it matters how you continue.
Do you...
- provide insights that drive business decisions?
- help stakeholders meet goals and objectives?
- analyze data to add value to your organization?
- ask questions and use them to guide analysis?
- effectively explain what your analysis means?
How you get started in data has much less importance than what you do once you're in.
How you got started in data.
Maybe you focused on a single tool.
Maybe you learned Python before SQL.
Maybe you thought you needed to know R.
Maybe you only know Excel and that's all you need.
Maybe you tried Power BI before deciding on Tableau.
It doesn't matter how you get started - it matters how you continue.
Do you...
- provide insights that drive business decisions?
- help stakeholders meet goals and objectives?
- analyze data to add value to your organization?
- ask questions and use them to guide analysis?
- effectively explain what your analysis means?
How you get started in data has much less importance than what you do once you're in.
๐4
๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ฟ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ ๐๐ผ ๐๐ต๐ฎ๐ฝ๐ฒ ๐๐ผ๐๐ฟ ๐ฐ๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ: ๐
-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean scripts, automate tasks, and manipulate data like a pro.
-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.
-> 3. Nail the Basics of Statistics & Probability
You canโt call yourself a data scientist if you donโt understand distributions, p-values, confidence intervals, and hypothesis testing.
-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.
-> 5. Learn Machine Learning the Right Way
Start simple:
Linear Regression
Logistic Regression
Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.
-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problemsโdonโt just learn, apply.
Make a portfolio that speaks louder than your resume.
-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
Turn models into real-world applications.
-> 8. Sharpen Soft Skills
Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.
๐ฌ๐ผ๐ ๐ฑ๐ผ๐ปโ๐ ๐ต๐ฎ๐๐ฒ ๐๐ผ ๐ฏ๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ฒ๐ฐ๐.
๐ฌ๐ผ๐ ๐ท๐๐๐ ๐ต๐ฎ๐๐ฒ ๐๐ผ ๐ฏ๐ฒ ๐ฐ๐ผ๐ป๐๐ถ๐๐๐ฒ๐ป๐.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
Hope this helps you ๐
-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean scripts, automate tasks, and manipulate data like a pro.
-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.
-> 3. Nail the Basics of Statistics & Probability
You canโt call yourself a data scientist if you donโt understand distributions, p-values, confidence intervals, and hypothesis testing.
-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.
-> 5. Learn Machine Learning the Right Way
Start simple:
Linear Regression
Logistic Regression
Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.
-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problemsโdonโt just learn, apply.
Make a portfolio that speaks louder than your resume.
-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
Turn models into real-world applications.
-> 8. Sharpen Soft Skills
Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.
๐ฌ๐ผ๐ ๐ฑ๐ผ๐ปโ๐ ๐ต๐ฎ๐๐ฒ ๐๐ผ ๐ฏ๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ฒ๐ฐ๐.
๐ฌ๐ผ๐ ๐ท๐๐๐ ๐ต๐ฎ๐๐ฒ ๐๐ผ ๐ฏ๐ฒ ๐ฐ๐ผ๐ป๐๐ถ๐๐๐ฒ๐ป๐.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
Hope this helps you ๐
๐2โค1
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.
๐3โค1
Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
๐2โค1
Artificial Intelligence on WhatsApp ๐
Top AI Channels on WhatsApp!
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4. Perplexity AI โ Your AI-powered research buddy with real-time answers. https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
5. Generative AI โ Your creative partner for text, images, code, and more. https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
6. Prompt Engineering โ Your secret weapon to get the best out of AI. https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
7. AI Tools โ Your toolkit for automating, analyzing, and accelerating everything. https://whatsapp.com/channel/0029VaojSv9LCoX0gBZUxX3B
8. AI Studio โ Everything about AI & Tech https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
9. Google Gemini โ Generate images & videos with AI. https://whatsapp.com/channel/0029Vb5Q4ly3mFY3Jz7qIu3i/103
10. Data Science & Machine Learning โ Your fuel for insights, predictions, and smarter decisions. https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. Data Science Projects โ Your engine for building smarter, self-learning systems. https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z/208
React โค๏ธ for more
Top AI Channels on WhatsApp!
1. ChatGPT โ Your go-to AI for anything and everything. https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
2. OpenAI โ Your gateway to cutting-edge artificial intelligence innovation. https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
3. Microsoft Copilot โ Your productivity powerhouse. https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
4. Perplexity AI โ Your AI-powered research buddy with real-time answers. https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
5. Generative AI โ Your creative partner for text, images, code, and more. https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
6. Prompt Engineering โ Your secret weapon to get the best out of AI. https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
7. AI Tools โ Your toolkit for automating, analyzing, and accelerating everything. https://whatsapp.com/channel/0029VaojSv9LCoX0gBZUxX3B
8. AI Studio โ Everything about AI & Tech https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
9. Google Gemini โ Generate images & videos with AI. https://whatsapp.com/channel/0029Vb5Q4ly3mFY3Jz7qIu3i/103
10. Data Science & Machine Learning โ Your fuel for insights, predictions, and smarter decisions. https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. Data Science Projects โ Your engine for building smarter, self-learning systems. https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z/208
React โค๏ธ for more
โค4๐1๐1
๐ช๐๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐
๐๐๐ ๐๐ ๐๐๐๐๐๐๐๐ ๐ ๐๐๐๐๐๐ ๐๐ ๐บ๐ธ๐ณ:
1. ๐ผ๐๐ ๐๐๐๐๐๐๐ ๐๐๐ ๐ฉ๐๐๐๐๐ ๐๐ ๐บ๐ธ๐ณ
๐. ๐๐ง๐ญ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐ญ๐จ ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ
๐๐ก๐๐ญ ๐ข๐ฌ ๐ ๐๐๐ญ๐๐๐๐ฌ๐?: Understanding the concept of databases and relational databases.
๐๐๐ญ๐๐๐๐ฌ๐ ๐๐๐ง๐๐ ๐๐ฆ๐๐ง๐ญ ๐๐ฒ๐ฌ๐ญ๐๐ฆ๐ฌ (๐๐๐๐): Learn about different DBMS like MySQL, PostgreSQL, SQL Server, Oracle.
๐. ๐๐๐ฌ๐ข๐ ๐๐๐ ๐๐จ๐ฆ๐ฆ๐๐ง๐๐ฌ
๐๐๐ญ๐ ๐๐๐ญ๐ซ๐ข๐๐ฏ๐๐ฅ:
๐๐๐๐๐๐: Basic retrieval of data.
๐๐๐๐๐: Filtering data based on conditions.
๐๐๐๐๐ ๐๐: Sorting results.
๐๐๐๐๐: Limiting the number of rows returned.
๐๐๐ญ๐ ๐๐๐ง๐ข๐ฉ๐ฎ๐ฅ๐๐ญ๐ข๐จ๐ง:
๐๐๐๐๐๐: Adding new data.
๐๐๐๐๐๐: Modifying existing data.
๐๐๐๐๐๐: Removing data.
2. ๐๐ง๐ญ๐๐ซ๐ฆ๐๐๐ข๐๐ญ๐ ๐๐๐ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
๐. ๐๐๐ฏ๐๐ง๐๐๐ ๐๐๐ญ๐ ๐๐๐ญ๐ซ๐ข๐๐ฏ๐๐ฅ
๐๐๐๐๐ฌ: Understanding different types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Using functions like COUNT, SUM, AVG, MIN, MAX.
๐๐๐๐๐ ๐๐: Grouping data to perform aggregate calculations.
๐๐๐๐๐๐: Filtering groups based on aggregate values.
๐. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ ๐๐ง๐ ๐๐๐ฌ๐ญ๐๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ
๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ: Using queries within queries.
๐๐จ๐ซ๐ซ๐๐ฅ๐๐ญ๐๐ ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ: Subqueries that reference columns from the outer query.
๐ช. ๐ซ๐๐๐ ๐ซ๐๐๐๐๐๐๐๐๐ ๐ณ๐๐๐๐๐๐๐ (๐ซ๐ซ๐ณ)
๐๐ซ๐๐๐ญ๐ข๐ง๐ ๐๐๐๐ฅ๐๐ฌ: CREATE TABLE.
๐๐จ๐๐ข๐๐ฒ๐ข๐ง๐ ๐๐๐๐ฅ๐๐ฌ: ALTER TABLE.
๐น๐๐๐๐๐๐๐ ๐ป๐๐๐๐๐: DROP TABLE.
3. ๐๐๐ฏ๐๐ง๐๐๐ ๐๐๐ ๐๐๐๐ก๐ง๐ข๐ช๐ฎ๐๐ฌ
๐. ๐๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
๐๐ง๐๐๐ฑ๐๐ฌ: Understanding and creating indexes to speed up queries.
๐๐ฎ๐๐ซ๐ฒ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Techniques to write efficient SQL queries.
๐. ๐๐๐ฏ๐๐ง๐๐๐ ๐๐๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ
๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Using functions like ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG.
๐๐๐ (๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ): Using WITH to create temporary result sets.
๐. ๐๐ซ๐๐ง๐ฌ๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐๐ง๐ ๐๐จ๐ง๐๐ฎ๐ซ๐ซ๐๐ง๐๐ฒ
๐๐ซ๐๐ง๐ฌ๐๐๐ญ๐ข๐จ๐ง๐ฌ: Using BEGIN, COMMIT, ROLLBACK.
๐๐จ๐ง๐๐ฎ๐ซ๐ซ๐๐ง๐๐ฒ ๐๐จ๐ง๐ญ๐ซ๐จ๐ฅ: Understanding isolation levels and locking mechanisms.
4. ๐๐ซ๐๐๐ญ๐ข๐๐๐ฅ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐๐ง๐ ๐๐๐๐ฅ-๐๐จ๐ซ๐ฅ๐ ๐๐๐๐ง๐๐ซ๐ข๐จ๐ฌ
๐. ๐๐๐ญ๐๐๐๐ฌ๐ ๐๐๐ฌ๐ข๐ ๐ง
๐๐จ๐ซ๐ฆ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Understanding normal forms and how to normalize databases.
๐๐ ๐๐ข๐๐ ๐ซ๐๐ฆ๐ฌ: Creating Entity-Relationship diagrams to model databases.
๐. ๐๐๐ญ๐ ๐๐ง๐ญ๐๐ ๐ซ๐๐ญ๐ข๐จ๐ง
๐๐๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐๐ฌ: Extract, Transform, Load processes for data integration.
๐๐ญ๐จ๐ซ๐๐ ๐๐ซ๐จ๐๐๐๐ฎ๐ซ๐๐ฌ ๐๐ง๐ ๐๐ซ๐ข๐ ๐ ๐๐ซ๐ฌ: Writing and using stored procedures and triggers for complex logic and automation.
๐. ๐๐๐ฌ๐ ๐๐ญ๐ฎ๐๐ข๐๐ฌ ๐๐ง๐ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ
๐๐๐๐ฅ-๐๐จ๐ซ๐ฅ๐ ๐๐๐๐ง๐๐ซ๐ข๐จ๐ฌ: Work on case studies involving complex database operations.
๐๐๐ฉ๐ฌ๐ญ๐จ๐ง๐ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Develop comprehensive projects that showcase your SQL expertise.
๐๐๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐๐ง๐ ๐๐จ๐จ๐ฅ๐ฌ
๐๐จ๐จ๐ค๐ฌ: "SQL in 10 Minutes, Sams Teach Yourself" by Ben Forta, "SQL for Data Scientists" by Renee M. P. Teate.
๐๐ง๐ฅ๐ข๐ง๐ ๐๐ฅ๐๐ญ๐๐จ๐ซ๐ฆ๐ฌ: Coursera, Udacity, edX, Khan Academy.
๐๐ซ๐๐๐ญ๐ข๐๐ ๐๐ฅ๐๐ญ๐๐จ๐ซ๐ฆ๐ฌ: LeetCode, HackerRank, Mode Analytics, SQLZoo.
1. ๐ผ๐๐ ๐๐๐๐๐๐๐ ๐๐๐ ๐ฉ๐๐๐๐๐ ๐๐ ๐บ๐ธ๐ณ
๐. ๐๐ง๐ญ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐ญ๐จ ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ
๐๐ก๐๐ญ ๐ข๐ฌ ๐ ๐๐๐ญ๐๐๐๐ฌ๐?: Understanding the concept of databases and relational databases.
๐๐๐ญ๐๐๐๐ฌ๐ ๐๐๐ง๐๐ ๐๐ฆ๐๐ง๐ญ ๐๐ฒ๐ฌ๐ญ๐๐ฆ๐ฌ (๐๐๐๐): Learn about different DBMS like MySQL, PostgreSQL, SQL Server, Oracle.
๐. ๐๐๐ฌ๐ข๐ ๐๐๐ ๐๐จ๐ฆ๐ฆ๐๐ง๐๐ฌ
๐๐๐ญ๐ ๐๐๐ญ๐ซ๐ข๐๐ฏ๐๐ฅ:
๐๐๐๐๐๐: Basic retrieval of data.
๐๐๐๐๐: Filtering data based on conditions.
๐๐๐๐๐ ๐๐: Sorting results.
๐๐๐๐๐: Limiting the number of rows returned.
๐๐๐ญ๐ ๐๐๐ง๐ข๐ฉ๐ฎ๐ฅ๐๐ญ๐ข๐จ๐ง:
๐๐๐๐๐๐: Adding new data.
๐๐๐๐๐๐: Modifying existing data.
๐๐๐๐๐๐: Removing data.
2. ๐๐ง๐ญ๐๐ซ๐ฆ๐๐๐ข๐๐ญ๐ ๐๐๐ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ
๐. ๐๐๐ฏ๐๐ง๐๐๐ ๐๐๐ญ๐ ๐๐๐ญ๐ซ๐ข๐๐ฏ๐๐ฅ
๐๐๐๐๐ฌ: Understanding different types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
๐๐ ๐ ๐ซ๐๐ ๐๐ญ๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Using functions like COUNT, SUM, AVG, MIN, MAX.
๐๐๐๐๐ ๐๐: Grouping data to perform aggregate calculations.
๐๐๐๐๐๐: Filtering groups based on aggregate values.
๐. ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ ๐๐ง๐ ๐๐๐ฌ๐ญ๐๐ ๐๐ฎ๐๐ซ๐ข๐๐ฌ
๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ: Using queries within queries.
๐๐จ๐ซ๐ซ๐๐ฅ๐๐ญ๐๐ ๐๐ฎ๐๐ช๐ฎ๐๐ซ๐ข๐๐ฌ: Subqueries that reference columns from the outer query.
๐ช. ๐ซ๐๐๐ ๐ซ๐๐๐๐๐๐๐๐๐ ๐ณ๐๐๐๐๐๐๐ (๐ซ๐ซ๐ณ)
๐๐ซ๐๐๐ญ๐ข๐ง๐ ๐๐๐๐ฅ๐๐ฌ: CREATE TABLE.
๐๐จ๐๐ข๐๐ฒ๐ข๐ง๐ ๐๐๐๐ฅ๐๐ฌ: ALTER TABLE.
๐น๐๐๐๐๐๐๐ ๐ป๐๐๐๐๐: DROP TABLE.
3. ๐๐๐ฏ๐๐ง๐๐๐ ๐๐๐ ๐๐๐๐ก๐ง๐ข๐ช๐ฎ๐๐ฌ
๐. ๐๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
๐๐ง๐๐๐ฑ๐๐ฌ: Understanding and creating indexes to speed up queries.
๐๐ฎ๐๐ซ๐ฒ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Techniques to write efficient SQL queries.
๐. ๐๐๐ฏ๐๐ง๐๐๐ ๐๐๐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ
๐๐ข๐ง๐๐จ๐ฐ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ: Using functions like ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG.
๐๐๐ (๐๐จ๐ฆ๐ฆ๐จ๐ง ๐๐๐๐ฅ๐ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง๐ฌ): Using WITH to create temporary result sets.
๐. ๐๐ซ๐๐ง๐ฌ๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐๐ง๐ ๐๐จ๐ง๐๐ฎ๐ซ๐ซ๐๐ง๐๐ฒ
๐๐ซ๐๐ง๐ฌ๐๐๐ญ๐ข๐จ๐ง๐ฌ: Using BEGIN, COMMIT, ROLLBACK.
๐๐จ๐ง๐๐ฎ๐ซ๐ซ๐๐ง๐๐ฒ ๐๐จ๐ง๐ญ๐ซ๐จ๐ฅ: Understanding isolation levels and locking mechanisms.
4. ๐๐ซ๐๐๐ญ๐ข๐๐๐ฅ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐๐ง๐ ๐๐๐๐ฅ-๐๐จ๐ซ๐ฅ๐ ๐๐๐๐ง๐๐ซ๐ข๐จ๐ฌ
๐. ๐๐๐ญ๐๐๐๐ฌ๐ ๐๐๐ฌ๐ข๐ ๐ง
๐๐จ๐ซ๐ฆ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Understanding normal forms and how to normalize databases.
๐๐ ๐๐ข๐๐ ๐ซ๐๐ฆ๐ฌ: Creating Entity-Relationship diagrams to model databases.
๐. ๐๐๐ญ๐ ๐๐ง๐ญ๐๐ ๐ซ๐๐ญ๐ข๐จ๐ง
๐๐๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐๐ฌ: Extract, Transform, Load processes for data integration.
๐๐ญ๐จ๐ซ๐๐ ๐๐ซ๐จ๐๐๐๐ฎ๐ซ๐๐ฌ ๐๐ง๐ ๐๐ซ๐ข๐ ๐ ๐๐ซ๐ฌ: Writing and using stored procedures and triggers for complex logic and automation.
๐. ๐๐๐ฌ๐ ๐๐ญ๐ฎ๐๐ข๐๐ฌ ๐๐ง๐ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ
๐๐๐๐ฅ-๐๐จ๐ซ๐ฅ๐ ๐๐๐๐ง๐๐ซ๐ข๐จ๐ฌ: Work on case studies involving complex database operations.
๐๐๐ฉ๐ฌ๐ญ๐จ๐ง๐ ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Develop comprehensive projects that showcase your SQL expertise.
๐๐๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐๐ง๐ ๐๐จ๐จ๐ฅ๐ฌ
๐๐จ๐จ๐ค๐ฌ: "SQL in 10 Minutes, Sams Teach Yourself" by Ben Forta, "SQL for Data Scientists" by Renee M. P. Teate.
๐๐ง๐ฅ๐ข๐ง๐ ๐๐ฅ๐๐ญ๐๐จ๐ซ๐ฆ๐ฌ: Coursera, Udacity, edX, Khan Academy.
๐๐ซ๐๐๐ญ๐ข๐๐ ๐๐ฅ๐๐ญ๐๐จ๐ซ๐ฆ๐ฌ: LeetCode, HackerRank, Mode Analytics, SQLZoo.
โค4๐3
Let's explore some of the best open source projects by language.
1โฃ Best Python Open Source Projects
๐ฃโโ TensorFlow
๐ฃโโ Matplotlib
๐ฃโโ Flask
๐ฃโโ Django
๐ฃโโ PyTorch
2โฃ Best JavaScript Open Source Projects
๐ฃโโ React
๐ฃโโ Node.JS
๐ฃโโ jQuery
3โฃ Best C++ Open Source Projects
๐ฃโโ Serenity
๐ฃโโ MongoDB
๐ฃโโ SonarSource
๐ฃโโ OBS Studio
๐ฃโโ Electron
4โฃ Best Java Open Source Projects
๐ฃโโ Mockito
๐ฃโโ Realm
๐ฃโโ Jenkins
๐ฃโโ Guava
๐ฃโโ Moshi
It's time to start developing your own open source projects. Explore the projects
1โฃ Best Python Open Source Projects
๐ฃโโ TensorFlow
๐ฃโโ Matplotlib
๐ฃโโ Flask
๐ฃโโ Django
๐ฃโโ PyTorch
2โฃ Best JavaScript Open Source Projects
๐ฃโโ React
๐ฃโโ Node.JS
๐ฃโโ jQuery
3โฃ Best C++ Open Source Projects
๐ฃโโ Serenity
๐ฃโโ MongoDB
๐ฃโโ SonarSource
๐ฃโโ OBS Studio
๐ฃโโ Electron
4โฃ Best Java Open Source Projects
๐ฃโโ Mockito
๐ฃโโ Realm
๐ฃโโ Jenkins
๐ฃโโ Guava
๐ฃโโ Moshi
It's time to start developing your own open source projects. Explore the projects
โค8
New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc.
Don't get bogged down trying to learn every new term & technology you come across.
Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.
The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!
Don't get bogged down trying to learn every new term & technology you come across.
Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.
The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!
โค7
10 commonly asked data science interview questions along with their answers
1๏ธโฃ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.
2๏ธโฃ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.
3๏ธโฃ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.
4๏ธโฃ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.
5๏ธโฃ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.
6๏ธโฃ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.
7๏ธโฃ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.
8๏ธโฃ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.
9๏ธโฃ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.
๐ What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
1๏ธโฃ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.
2๏ธโฃ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.
3๏ธโฃ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.
4๏ธโฃ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.
5๏ธโฃ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.
6๏ธโฃ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.
7๏ธโฃ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.
8๏ธโฃ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.
9๏ธโฃ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.
๐ What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
โค5๐2
Source codes for data science projects ๐๐
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
๐๐ก๐๐ข๐ฌ ๐๐๐๐ฅ๐ก๐๐ก๐๐๐
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
๐๐ก๐๐ข๐ฌ ๐๐๐๐ฅ๐ก๐๐ก๐๐๐
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๐ Roadmap to Master Machine Learning in 6 Steps
Whether you're just starting or looking to go pro in ML, this roadmap will keep you on track:
1๏ธโฃ Learn the Fundamentals
Build a math foundation (algebra, calculus, stats) + Python + libraries like NumPy & Pandas
2๏ธโฃ Learn Essential ML Concepts
Start with supervised learning (regression, classification), then unsupervised learning (K-Means, PCA)
3๏ธโฃ Understand Data Handling
Clean, transform, and visualize data effectively using summary stats & feature engineering
4๏ธโฃ Explore Advanced Techniques
Delve into ensemble methods, CNNs, deep learning, and NLP fundamentals
5๏ธโฃ Learn Model Deployment
Use Flask, FastAPI, and cloud platforms (AWS, GCP) for scalable deployment
6๏ธโฃ Build Projects & Network
Participate in Kaggle, create portfolio projects, and connect with the ML community
๐ Start your journey now with these top-rated ML & AI courses: https://imp.i384100.net/MAoag3
React โค๏ธ for more
Whether you're just starting or looking to go pro in ML, this roadmap will keep you on track:
1๏ธโฃ Learn the Fundamentals
Build a math foundation (algebra, calculus, stats) + Python + libraries like NumPy & Pandas
2๏ธโฃ Learn Essential ML Concepts
Start with supervised learning (regression, classification), then unsupervised learning (K-Means, PCA)
3๏ธโฃ Understand Data Handling
Clean, transform, and visualize data effectively using summary stats & feature engineering
4๏ธโฃ Explore Advanced Techniques
Delve into ensemble methods, CNNs, deep learning, and NLP fundamentals
5๏ธโฃ Learn Model Deployment
Use Flask, FastAPI, and cloud platforms (AWS, GCP) for scalable deployment
6๏ธโฃ Build Projects & Network
Participate in Kaggle, create portfolio projects, and connect with the ML community
๐ Start your journey now with these top-rated ML & AI courses: https://imp.i384100.net/MAoag3
React โค๏ธ for more
โค4
Data Science Interview Questions with Answers
Whatโs the difference between random forest and gradient boosting?
Random Forests builds each tree independently while Gradient Boosting builds one tree at a time.
Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.
What happens to our linear regression model if we have three columns in our data: x, y, z โโโ and z is a sum of x and y?
We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression would be a singular (not invertible) matrix.
Which regularization techniques do you know?
There are mainly two types of regularization,
L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function.
L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function
Here, Lambda determines the amount of regularization.
How does L2 regularization look like in a linear model?
L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter.
This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.
What are the main parameters in the gradient boosting model?
There are many parameters, but below are a few key defaults.
learning_rate=0.1 (shrinkage).
n_estimators=100 (number of trees).
max_depth=3.
min_samples_split=2.
min_samples_leaf=1.
subsample=1.0.
Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Whatโs the difference between random forest and gradient boosting?
Random Forests builds each tree independently while Gradient Boosting builds one tree at a time.
Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.
What happens to our linear regression model if we have three columns in our data: x, y, z โโโ and z is a sum of x and y?
We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression would be a singular (not invertible) matrix.
Which regularization techniques do you know?
There are mainly two types of regularization,
L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function.
L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function
Here, Lambda determines the amount of regularization.
How does L2 regularization look like in a linear model?
L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter.
This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.
What are the main parameters in the gradient boosting model?
There are many parameters, but below are a few key defaults.
learning_rate=0.1 (shrinkage).
n_estimators=100 (number of trees).
max_depth=3.
min_samples_split=2.
min_samples_leaf=1.
subsample=1.0.
Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
โค2
Free Programming and Data Analytics Resources ๐๐
โ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://t.iss.one/datasciencefun/1371
๐ Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
๐ค Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
๐ Useful Resources for the Programmers
Data Analyst Roadmap
https://t.iss.one/sqlspecialist/94
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://t.iss.one/datasciencefree/68
Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.iss.one/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single
๐ป Free Programming Courses by Microsoft
โฏ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/
โฏ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING ๐๐
โ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://t.iss.one/datasciencefun/1371
๐ Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
๐ค Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
๐ Useful Resources for the Programmers
Data Analyst Roadmap
https://t.iss.one/sqlspecialist/94
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://t.iss.one/datasciencefree/68
Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.iss.one/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single
๐ป Free Programming Courses by Microsoft
โฏ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/
โฏ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING ๐๐
โค4
If I Were to Start My Data Science Career from Scratch, Here's What I Would Do ๐
1๏ธโฃ Master Advanced SQL
Foundations: Learn database structures, tables, and relationships.
Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.
Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.
JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.
Advanced Concepts: CTEs, window functions, and query optimization.
Metric Development: Build and report metrics effectively.
2๏ธโฃ Study Statistics & A/B Testing
Descriptive Statistics: Know your mean, median, mode, and standard deviation.
Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.
Probability: Understand basic probability and Bayes' theorem.
Intro to ML: Start with linear regression, decision trees, and K-means clustering.
Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.
A/B Testing: Design experimentsโhypothesis formation, sample size calculation, and sample biases.
3๏ธโฃ Learn Python for Data
Data Manipulation: Use pandas for data cleaning and manipulation.
Data Visualization: Explore matplotlib and seaborn for creating visualizations.
Hypothesis Testing: Dive into scipy for statistical testing.
Basic Modeling: Practice building models with scikit-learn.
4๏ธโฃ Develop Product Sense
Product Management Basics: Manage projects and understand the product life cycle.
Data-Driven Strategy: Leverage data to inform decisions and measure success.
Metrics in Business: Define and evaluate metrics that matter to the business.
5๏ธโฃ Hone Soft Skills
Communication: Clearly explain data findings to technical and non-technical audiences.
Collaboration: Work effectively in teams.
Time Management: Prioritize and manage projects efficiently.
Self-Reflection: Regularly assess and improve your skills.
6๏ธโฃ Bonus: Basic Data Engineering
Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.
ETL: Set up extraction jobs, manage dependencies, clean and validate data.
Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
1๏ธโฃ Master Advanced SQL
Foundations: Learn database structures, tables, and relationships.
Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.
Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.
JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.
Advanced Concepts: CTEs, window functions, and query optimization.
Metric Development: Build and report metrics effectively.
2๏ธโฃ Study Statistics & A/B Testing
Descriptive Statistics: Know your mean, median, mode, and standard deviation.
Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.
Probability: Understand basic probability and Bayes' theorem.
Intro to ML: Start with linear regression, decision trees, and K-means clustering.
Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.
A/B Testing: Design experimentsโhypothesis formation, sample size calculation, and sample biases.
3๏ธโฃ Learn Python for Data
Data Manipulation: Use pandas for data cleaning and manipulation.
Data Visualization: Explore matplotlib and seaborn for creating visualizations.
Hypothesis Testing: Dive into scipy for statistical testing.
Basic Modeling: Practice building models with scikit-learn.
4๏ธโฃ Develop Product Sense
Product Management Basics: Manage projects and understand the product life cycle.
Data-Driven Strategy: Leverage data to inform decisions and measure success.
Metrics in Business: Define and evaluate metrics that matter to the business.
5๏ธโฃ Hone Soft Skills
Communication: Clearly explain data findings to technical and non-technical audiences.
Collaboration: Work effectively in teams.
Time Management: Prioritize and manage projects efficiently.
Self-Reflection: Regularly assess and improve your skills.
6๏ธโฃ Bonus: Basic Data Engineering
Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.
ETL: Set up extraction jobs, manage dependencies, clean and validate data.
Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
โค6