Machine Learning Algorithms Overview
▌1. Supervised Learning
Supervised learning algorithms learn from labeled data — input features with corresponding output labels.
- Linear Regression
- Used for predicting continuous numerical values.
- Example: Predicting house prices based on features like size, location.
- Learns the linear relationship between input variables and output.
- Logistic Regression
- Used for binary classification problems.
- Example: Spam detection (spam or not spam).
- Outputs probabilities using a logistic (sigmoid) function.
- Decision Trees
- Used for classification and regression.
- Splits data based on feature values to make predictions.
- Easy to interpret but can overfit if not pruned.
- Random Forest
- An ensemble of decision trees.
- Reduces overfitting by averaging multiple trees.
- Good accuracy and robustness.
- Support Vector Machines (SVM)
- Used for classification tasks.
- Finds the hyperplane that best separates classes with maximum margin.
- Can handle non-linear boundaries with kernel tricks.
- K-Nearest Neighbors (KNN)
- Classification and regression based on proximity to neighbors.
- Simple but computationally expensive on large datasets.
- Gradient Boosting Machines (GBM), XGBoost, LightGBM
- Ensemble methods that build models sequentially to correct previous errors.
- Powerful, widely used for structured/tabular data.
- Neural Networks (Basic)
- Can be used for both regression and classification.
- Consists of layers of interconnected nodes (neurons).
- Basis for deep learning but also useful in simpler forms.
▌2. Unsupervised Learning
Unsupervised algorithms learn patterns from unlabeled data.
- K-Means Clustering
- Groups data into K clusters based on feature similarity.
- Used for customer segmentation, anomaly detection.
- Hierarchical Clustering
- Builds a tree of clusters (dendrogram).
- Useful for understanding data structure.
- Principal Component Analysis (PCA)
- Dimensionality reduction technique.
- Projects data into fewer dimensions while preserving variance.
- Helps in visualization and noise reduction.
- Autoencoders (Neural Networks)
- Learn efficient data encodings.
- Used for anomaly detection and data compression.
▌3. Reinforcement Learning (Brief)
- Learns by interacting with an environment to maximize cumulative reward.
- Used in robotics, game playing (e.g., AlphaGo), recommendation systems.
▌4. Other Important Algorithms and Concepts
- Naive Bayes
- Probabilistic classifier based on Bayes theorem.
- Assumes feature independence.
- Fast and effective for text classification.
- Dimensionality Reduction
- Techniques like t-SNE, UMAP for visualization and noise reduction.
- Deep Learning (Advanced Neural Networks)
- Convolutional Neural Networks (CNN) for images.
- Recurrent Neural Networks (RNN), LSTM for sequence data.
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▌1. Supervised Learning
Supervised learning algorithms learn from labeled data — input features with corresponding output labels.
- Linear Regression
- Used for predicting continuous numerical values.
- Example: Predicting house prices based on features like size, location.
- Learns the linear relationship between input variables and output.
- Logistic Regression
- Used for binary classification problems.
- Example: Spam detection (spam or not spam).
- Outputs probabilities using a logistic (sigmoid) function.
- Decision Trees
- Used for classification and regression.
- Splits data based on feature values to make predictions.
- Easy to interpret but can overfit if not pruned.
- Random Forest
- An ensemble of decision trees.
- Reduces overfitting by averaging multiple trees.
- Good accuracy and robustness.
- Support Vector Machines (SVM)
- Used for classification tasks.
- Finds the hyperplane that best separates classes with maximum margin.
- Can handle non-linear boundaries with kernel tricks.
- K-Nearest Neighbors (KNN)
- Classification and regression based on proximity to neighbors.
- Simple but computationally expensive on large datasets.
- Gradient Boosting Machines (GBM), XGBoost, LightGBM
- Ensemble methods that build models sequentially to correct previous errors.
- Powerful, widely used for structured/tabular data.
- Neural Networks (Basic)
- Can be used for both regression and classification.
- Consists of layers of interconnected nodes (neurons).
- Basis for deep learning but also useful in simpler forms.
▌2. Unsupervised Learning
Unsupervised algorithms learn patterns from unlabeled data.
- K-Means Clustering
- Groups data into K clusters based on feature similarity.
- Used for customer segmentation, anomaly detection.
- Hierarchical Clustering
- Builds a tree of clusters (dendrogram).
- Useful for understanding data structure.
- Principal Component Analysis (PCA)
- Dimensionality reduction technique.
- Projects data into fewer dimensions while preserving variance.
- Helps in visualization and noise reduction.
- Autoencoders (Neural Networks)
- Learn efficient data encodings.
- Used for anomaly detection and data compression.
▌3. Reinforcement Learning (Brief)
- Learns by interacting with an environment to maximize cumulative reward.
- Used in robotics, game playing (e.g., AlphaGo), recommendation systems.
▌4. Other Important Algorithms and Concepts
- Naive Bayes
- Probabilistic classifier based on Bayes theorem.
- Assumes feature independence.
- Fast and effective for text classification.
- Dimensionality Reduction
- Techniques like t-SNE, UMAP for visualization and noise reduction.
- Deep Learning (Advanced Neural Networks)
- Convolutional Neural Networks (CNN) for images.
- Recurrent Neural Networks (RNN), LSTM for sequence data.
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Data Science Fundamentals You Should Know ☑️
I. Core Mathematics and Statistics:
• Linear Algebra:
• Why: Understanding how algorithms manipulate data as vectors and matrices. Crucial for machine learning.
• Key Concepts: Vectors, matrices, matrix operations (addition, multiplication, transpose, inverse), eigenvalues, eigenvectors, singular value decomposition (SVD).
• Calculus:
• Why: Optimization algorithms (like gradient descent) rely on calculus concepts.
• Key Concepts: Derivatives, integrals, limits, optimization, chain rule.
• Probability and Statistics:
• Why: Data is inherently uncertain. Statistics provides the tools to understand and quantify that uncertainty.
• Key Concepts:
* Descriptive Statistics: Mean, median, mode, variance, standard deviation, percentiles.
* Probability Distributions: Normal, binomial, Poisson, exponential.
* Hypothesis Testing: Null hypothesis, alternative hypothesis, p-values, t-tests, chi-squared tests, ANOVA.
* Confidence Intervals: Estimating population parameters.
* Bayesian Statistics: Bayes' theorem, prior probabilities, posterior probabilities.
• Discrete Mathematics (Optional, but helpful):
* Why: Especially relevant if you're working with graph data or network analysis.
* Key Concepts: Sets, logic, combinatorics, graph theory.
II. Programming Fundamentals:
• Python or R (Choose one to start, Python is often preferred):
• Why: These are the workhorses of data science.
• Key Concepts:
* Data Structures: Lists, dictionaries (Python), vectors, lists (R).
* Control Flow: Loops, conditional statements.
* Functions: Defining and using functions.
* Object-Oriented Programming (OOP) Basics: Classes, objects (helpful, but not essential to start).
• Key Python Libraries:
• NumPy: Numerical computing (arrays, linear algebra).
• Pandas: Data manipulation and analysis (DataFrames).
• Matplotlib & Seaborn: Data visualization.
• Scikit-learn: Machine learning algorithms.
• Key R Libraries:
• dplyr: Data manipulation.
• ggplot2: Data visualization.
• caret: Machine learning.
• SQL:
• Why: Essential for retrieving and manipulating data from databases.
• Key Concepts: SELECT, FROM, WHERE, JOIN, GROUP BY, ORDER BY, aggregate functions.
III. Data Wrangling and Exploration:
• Data Collection:
• Understanding Data Sources: APIs, databases, web scraping (ethical considerations).
• Data Cleaning:
• Handling Missing Values: Imputation strategies.
• Removing Duplicates: Identifying and removing redundant data.
• Correcting Inconsistencies: Standardizing formats, fixing errors.
• Data Transformation:
• Scaling and Normalization: Standardizing numerical features.
• Encoding Categorical Features: One-hot encoding, label encoding.
• Exploratory Data Analysis (EDA):
• Univariate Analysis: Examining individual variables.
• Bivariate Analysis: Examining relationships between two variables.
• Multivariate Analysis: Examining relationships among multiple variables.
• Visualization: Using charts and graphs to uncover patterns.
IV. Machine Learning Fundamentals:
• Supervised Learning:
• Regression: Predicting continuous values (linear regression, polynomial regression).
• Classification: Predicting categories (logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors).
• Model Evaluation Metrics: R-squared, RMSE (regression), accuracy, precision, recall, F1-score, AUC (classification).
• Unsupervised Learning:
• Clustering: Grouping similar data points (k-means, hierarchical clustering).
• Dimensionality Reduction: Reducing the number of features (principal component analysis).
• Model Selection and Evaluation:
I. Core Mathematics and Statistics:
• Linear Algebra:
• Why: Understanding how algorithms manipulate data as vectors and matrices. Crucial for machine learning.
• Key Concepts: Vectors, matrices, matrix operations (addition, multiplication, transpose, inverse), eigenvalues, eigenvectors, singular value decomposition (SVD).
• Calculus:
• Why: Optimization algorithms (like gradient descent) rely on calculus concepts.
• Key Concepts: Derivatives, integrals, limits, optimization, chain rule.
• Probability and Statistics:
• Why: Data is inherently uncertain. Statistics provides the tools to understand and quantify that uncertainty.
• Key Concepts:
* Descriptive Statistics: Mean, median, mode, variance, standard deviation, percentiles.
* Probability Distributions: Normal, binomial, Poisson, exponential.
* Hypothesis Testing: Null hypothesis, alternative hypothesis, p-values, t-tests, chi-squared tests, ANOVA.
* Confidence Intervals: Estimating population parameters.
* Bayesian Statistics: Bayes' theorem, prior probabilities, posterior probabilities.
• Discrete Mathematics (Optional, but helpful):
* Why: Especially relevant if you're working with graph data or network analysis.
* Key Concepts: Sets, logic, combinatorics, graph theory.
II. Programming Fundamentals:
• Python or R (Choose one to start, Python is often preferred):
• Why: These are the workhorses of data science.
• Key Concepts:
* Data Structures: Lists, dictionaries (Python), vectors, lists (R).
* Control Flow: Loops, conditional statements.
* Functions: Defining and using functions.
* Object-Oriented Programming (OOP) Basics: Classes, objects (helpful, but not essential to start).
• Key Python Libraries:
• NumPy: Numerical computing (arrays, linear algebra).
• Pandas: Data manipulation and analysis (DataFrames).
• Matplotlib & Seaborn: Data visualization.
• Scikit-learn: Machine learning algorithms.
• Key R Libraries:
• dplyr: Data manipulation.
• ggplot2: Data visualization.
• caret: Machine learning.
• SQL:
• Why: Essential for retrieving and manipulating data from databases.
• Key Concepts: SELECT, FROM, WHERE, JOIN, GROUP BY, ORDER BY, aggregate functions.
III. Data Wrangling and Exploration:
• Data Collection:
• Understanding Data Sources: APIs, databases, web scraping (ethical considerations).
• Data Cleaning:
• Handling Missing Values: Imputation strategies.
• Removing Duplicates: Identifying and removing redundant data.
• Correcting Inconsistencies: Standardizing formats, fixing errors.
• Data Transformation:
• Scaling and Normalization: Standardizing numerical features.
• Encoding Categorical Features: One-hot encoding, label encoding.
• Exploratory Data Analysis (EDA):
• Univariate Analysis: Examining individual variables.
• Bivariate Analysis: Examining relationships between two variables.
• Multivariate Analysis: Examining relationships among multiple variables.
• Visualization: Using charts and graphs to uncover patterns.
IV. Machine Learning Fundamentals:
• Supervised Learning:
• Regression: Predicting continuous values (linear regression, polynomial regression).
• Classification: Predicting categories (logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors).
• Model Evaluation Metrics: R-squared, RMSE (regression), accuracy, precision, recall, F1-score, AUC (classification).
• Unsupervised Learning:
• Clustering: Grouping similar data points (k-means, hierarchical clustering).
• Dimensionality Reduction: Reducing the number of features (principal component analysis).
• Model Selection and Evaluation:
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• Train/Test Split: Dividing data into training and testing sets.
• Cross-Validation: Evaluating model performance robustly.
• Overfitting and Underfitting: Understanding and mitigating these issues.
• Bias-Variance Tradeoff: Understanding the balance between model complexity and generalization ability.
V. Communication and Presentation:
• Data Storytelling: Crafting a narrative around your data findings.
• Visualization Best Practices: Choosing the right chart types, designing clear and effective visuals.
• Presentation Skills: Presenting your findings clearly and concisely to both technical and non-technical audiences.
• Report Writing: Documenting your analysis and findings in a clear and organized manner.
VI. Essential Soft Skills:
• Critical Thinking: Analyzing problems and formulating solutions.
• Communication: Explaining complex concepts clearly.
• Problem-Solving: Identifying and addressing data-related challenges.
• Teamwork: Collaborating effectively with others.
• Curiosity: A desire to learn and explore new data and techniques.
VII. Ethical Considerations:
• Data Privacy Understanding regulations like GDPR and CCPA.
• Bias Detection and Mitigation Ensuring your models are fair and unbiased.
• Transparency and Explainability Being able to explain how your models make decisions.
How to Learn:
• Online Courses: Coursera, edX, Udacity, DataCamp.
• Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron, "Python Data Science Handbook" by Jake VanderPlas.
• Kaggle: Practice on real-world datasets.
• Personal Projects: Apply your knowledge to projects that interest you.
• Community: Engage with other data scientists online and in person.
This is a comprehensive list, and you don't need to master everything immediately.
Focus on building a strong foundation in the core areas, and you can gradually expand your knowledge and skills over time.
Join our WhatsApp channel for more useful resources: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O
ENJOY LEARNING⭐
• Cross-Validation: Evaluating model performance robustly.
• Overfitting and Underfitting: Understanding and mitigating these issues.
• Bias-Variance Tradeoff: Understanding the balance between model complexity and generalization ability.
V. Communication and Presentation:
• Data Storytelling: Crafting a narrative around your data findings.
• Visualization Best Practices: Choosing the right chart types, designing clear and effective visuals.
• Presentation Skills: Presenting your findings clearly and concisely to both technical and non-technical audiences.
• Report Writing: Documenting your analysis and findings in a clear and organized manner.
VI. Essential Soft Skills:
• Critical Thinking: Analyzing problems and formulating solutions.
• Communication: Explaining complex concepts clearly.
• Problem-Solving: Identifying and addressing data-related challenges.
• Teamwork: Collaborating effectively with others.
• Curiosity: A desire to learn and explore new data and techniques.
VII. Ethical Considerations:
• Data Privacy Understanding regulations like GDPR and CCPA.
• Bias Detection and Mitigation Ensuring your models are fair and unbiased.
• Transparency and Explainability Being able to explain how your models make decisions.
How to Learn:
• Online Courses: Coursera, edX, Udacity, DataCamp.
• Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron, "Python Data Science Handbook" by Jake VanderPlas.
• Kaggle: Practice on real-world datasets.
• Personal Projects: Apply your knowledge to projects that interest you.
• Community: Engage with other data scientists online and in person.
This is a comprehensive list, and you don't need to master everything immediately.
Focus on building a strong foundation in the core areas, and you can gradually expand your knowledge and skills over time.
Join our WhatsApp channel for more useful resources: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O
ENJOY LEARNING
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Top 10 Data Science Concepts You Should Know 🧠
1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.
2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.
3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.
4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.
5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.
6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.
7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.
8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!
9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.
10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.
2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.
3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.
4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.
5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.
6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.
7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.
8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!
9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.
10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
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📊 Data Science Project Ideas to Practice & Master Your Skills ✅
🟢 Beginner Level
• Titanic Survival Prediction (Logistic Regression)
• House Price Prediction (Linear Regression)
• Exploratory Data Analysis on IPL or Netflix Dataset
• Customer Segmentation (K-Means Clustering)
• Weather Data Visualization
🟡 Intermediate Level
• Sentiment Analysis on Tweets
• Credit Card Fraud Detection
• Time Series Forecasting (Stock or Sales Data)
• Image Classification using CNN (Fashion MNIST)
• Recommendation System for Movies/Products
🔴 Advanced Level
• End-to-End Machine Learning Pipeline with Deployment
• NLP Chatbot using Transformers
• Real-Time Dashboard with Streamlit + ML
• Anomaly Detection in Network Traffic
• A/B Testing & Business Decision Modeling
💬 Double Tap ❤️ for more! 🤖📈
🟢 Beginner Level
• Titanic Survival Prediction (Logistic Regression)
• House Price Prediction (Linear Regression)
• Exploratory Data Analysis on IPL or Netflix Dataset
• Customer Segmentation (K-Means Clustering)
• Weather Data Visualization
🟡 Intermediate Level
• Sentiment Analysis on Tweets
• Credit Card Fraud Detection
• Time Series Forecasting (Stock or Sales Data)
• Image Classification using CNN (Fashion MNIST)
• Recommendation System for Movies/Products
🔴 Advanced Level
• End-to-End Machine Learning Pipeline with Deployment
• NLP Chatbot using Transformers
• Real-Time Dashboard with Streamlit + ML
• Anomaly Detection in Network Traffic
• A/B Testing & Business Decision Modeling
💬 Double Tap ❤️ for more! 🤖📈
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COMMON TERMINOLOGIES IN PYTHON - PART 1
Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?
In this series, we would be looking at the common Terminologies in python.
It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:
IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.
Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately
System Python - This is the version of python that comes with your operating system
Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions
REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)
Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.
Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function
Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.
Note: A return value can be any of these variable types: handle, integer, object, or string
Script - This is a file where you store your python code in a text file and execute all of the code with a single command
Script files - this is a file containing a group of python scripts
Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?
In this series, we would be looking at the common Terminologies in python.
It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:
IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.
Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately
System Python - This is the version of python that comes with your operating system
Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions
REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)
Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.
Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function
Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.
Note: A return value can be any of these variable types: handle, integer, object, or string
Script - This is a file where you store your python code in a text file and execute all of the code with a single command
Script files - this is a file containing a group of python scripts
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Being a Generalist Data Scientist won't get you hired.
Here is how you can specialize 👇
Companies have specific problems that require certain skills to solve. If you do not know which path you want to follow. Start broad first, explore your options, then specialize.
To discover what you enjoy the most, try answering different questions for each DS role:
- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How should we monitor model performance in production?”
- 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 / 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we visualize customer segmentation to highlight key demographics?”
- 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we use clustering to identify new customer segments for targeted marketing?”
- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫
Qs:
“What novel architectures can we explore to improve model robustness?”
- 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How can we automate the deployment of machine learning models to ensure continuous integration and delivery?”
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
Here is how you can specialize 👇
Companies have specific problems that require certain skills to solve. If you do not know which path you want to follow. Start broad first, explore your options, then specialize.
To discover what you enjoy the most, try answering different questions for each DS role:
- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How should we monitor model performance in production?”
- 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 / 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we visualize customer segmentation to highlight key demographics?”
- 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we use clustering to identify new customer segments for targeted marketing?”
- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫
Qs:
“What novel architectures can we explore to improve model robustness?”
- 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How can we automate the deployment of machine learning models to ensure continuous integration and delivery?”
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
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