🔍 Machine Learning Cheat Sheet 🔍
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
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1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
🚀 Dive into Machine Learning and transform data into insights! 🚀
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
❤3
Important data science topics you should definitely be aware of
1. Statistics & Probability
Descriptive Statistics (mean, median, mode, variance, std deviation)
Probability Distributions (Normal, Binomial, Poisson)
Bayes' Theorem
Hypothesis Testing (t-test, chi-square test, ANOVA)
Confidence Intervals
2. Data Manipulation & Analysis
Data wrangling/cleaning
Handling missing values & outliers
Feature engineering & scaling
GroupBy operations
Pivot tables
Time series manipulation
3. Programming (Python/R)
Data structures (lists, dictionaries, sets)
Libraries:
Python: pandas, NumPy, matplotlib, seaborn, scikit-learn
R: dplyr, ggplot2, caret
Writing reusable functions
Working with APIs & files (CSV, JSON, Excel)
4. Data Visualization
Plot types: bar, line, scatter, histograms, heatmaps, boxplots
Dashboards (Power BI, Tableau, Plotly Dash, Streamlit)
Communicating insights clearly
5. Machine Learning
Supervised Learning
Linear & Logistic Regression
Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM)
SVM, KNN
Unsupervised Learning
K-means Clustering
PCA
Hierarchical Clustering
Model Evaluation
Accuracy, Precision, Recall, F1-Score
Confusion Matrix, ROC-AUC
Cross-validation, Grid Search
6. Deep Learning (Basics)
Neural Networks (perceptron, activation functions)
CNNs, RNNs (just an overview unless you're going deep into DL)
Frameworks: TensorFlow, PyTorch, Keras
7. SQL & Databases
SELECT, WHERE, GROUP BY, JOINS, CTEs, Subqueries
Window functions
Indexes and Query Optimization
8. Big Data & Cloud (Basics)
Hadoop, Spark
AWS, GCP, Azure (basic knowledge of data services)
9. Deployment & MLOps (Basic Awareness)
Model deployment (Flask, FastAPI)
Docker basics
CI/CD pipelines
Model monitoring
10. Business & Domain Knowledge
Framing a problem
Understanding business KPIs
Translating data insights into actionable strategies
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1. Statistics & Probability
Descriptive Statistics (mean, median, mode, variance, std deviation)
Probability Distributions (Normal, Binomial, Poisson)
Bayes' Theorem
Hypothesis Testing (t-test, chi-square test, ANOVA)
Confidence Intervals
2. Data Manipulation & Analysis
Data wrangling/cleaning
Handling missing values & outliers
Feature engineering & scaling
GroupBy operations
Pivot tables
Time series manipulation
3. Programming (Python/R)
Data structures (lists, dictionaries, sets)
Libraries:
Python: pandas, NumPy, matplotlib, seaborn, scikit-learn
R: dplyr, ggplot2, caret
Writing reusable functions
Working with APIs & files (CSV, JSON, Excel)
4. Data Visualization
Plot types: bar, line, scatter, histograms, heatmaps, boxplots
Dashboards (Power BI, Tableau, Plotly Dash, Streamlit)
Communicating insights clearly
5. Machine Learning
Supervised Learning
Linear & Logistic Regression
Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM)
SVM, KNN
Unsupervised Learning
K-means Clustering
PCA
Hierarchical Clustering
Model Evaluation
Accuracy, Precision, Recall, F1-Score
Confusion Matrix, ROC-AUC
Cross-validation, Grid Search
6. Deep Learning (Basics)
Neural Networks (perceptron, activation functions)
CNNs, RNNs (just an overview unless you're going deep into DL)
Frameworks: TensorFlow, PyTorch, Keras
7. SQL & Databases
SELECT, WHERE, GROUP BY, JOINS, CTEs, Subqueries
Window functions
Indexes and Query Optimization
8. Big Data & Cloud (Basics)
Hadoop, Spark
AWS, GCP, Azure (basic knowledge of data services)
9. Deployment & MLOps (Basic Awareness)
Model deployment (Flask, FastAPI)
Docker basics
CI/CD pipelines
Model monitoring
10. Business & Domain Knowledge
Framing a problem
Understanding business KPIs
Translating data insights into actionable strategies
I have curated the best interview resources to crack Data Science Interviews
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Step-by-Step Roadmap to Learn Data Science in 2025:
Step 1: Understand the Role
A data scientist in 2025 is expected to:
Analyze data to extract insights
Build predictive models using ML
Communicate findings to stakeholders
Work with large datasets in cloud environments
Step 2: Master the Prerequisite Skills
A. Programming
Learn Python (must-have): Focus on pandas, numpy, matplotlib, seaborn, scikit-learn
R (optional but helpful for statistical analysis)
SQL: Strong command over data extraction and transformation
B. Math & Stats
Probability, Descriptive & Inferential Statistics
Linear Algebra & Calculus (only what's necessary for ML)
Hypothesis testing
Step 3: Learn Data Handling
Data Cleaning, Preprocessing
Exploratory Data Analysis (EDA)
Feature Engineering
Tools: Python (pandas), Excel, SQL
Step 4: Master Machine Learning
Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost
Unsupervised Learning: K-Means, Hierarchical Clustering, PCA
Deep Learning (optional): Use TensorFlow or PyTorch
Evaluation Metrics: Accuracy, AUC, Confusion Matrix, RMSE
Step 5: Learn Data Visualization & Storytelling
Python (matplotlib, seaborn, plotly)
Power BI / Tableau
Communicating insights clearly is as important as modeling
Step 6: Use Real Datasets & Projects
Work on projects using Kaggle, UCI, or public APIs
Examples:
Customer churn prediction
Sales forecasting
Sentiment analysis
Fraud detection
Step 7: Understand Cloud & MLOps (2025+ Skills)
Cloud: AWS (S3, EC2, SageMaker), GCP, or Azure
MLOps: Model deployment (Flask, FastAPI), CI/CD for ML, Docker basics
Step 8: Build Portfolio & Resume
Create GitHub repos with well-documented code
Post projects and blogs on Medium or LinkedIn
Prepare a data science-specific resume
Step 9: Apply Smartly
Focus on job roles like: Data Scientist, ML Engineer, Data Analyst → DS
Use platforms like LinkedIn, Glassdoor, Hirect, AngelList, etc.
Practice data science interviews: case studies, ML concepts, SQL + Python coding
Step 10: Keep Learning & Updating
Follow top newsletters: Data Elixir, Towards Data Science
Read papers (arXiv, Google Scholar) on trending topics: LLMs, AutoML, Explainable AI
Upskill with certifications (Google Data Cert, Coursera, DataCamp, Udemy)
Free Resources to learn Data Science
Kaggle Courses: https://www.kaggle.com/learn
CS50 AI by Harvard: https://cs50.harvard.edu/ai/
Fast.ai: https://course.fast.ai/
Google ML Crash Course: https://developers.google.com/machine-learning/crash-course
Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998
Data Science Books: https://t.iss.one/datalemur
React ❤️ for more
Step 1: Understand the Role
A data scientist in 2025 is expected to:
Analyze data to extract insights
Build predictive models using ML
Communicate findings to stakeholders
Work with large datasets in cloud environments
Step 2: Master the Prerequisite Skills
A. Programming
Learn Python (must-have): Focus on pandas, numpy, matplotlib, seaborn, scikit-learn
R (optional but helpful for statistical analysis)
SQL: Strong command over data extraction and transformation
B. Math & Stats
Probability, Descriptive & Inferential Statistics
Linear Algebra & Calculus (only what's necessary for ML)
Hypothesis testing
Step 3: Learn Data Handling
Data Cleaning, Preprocessing
Exploratory Data Analysis (EDA)
Feature Engineering
Tools: Python (pandas), Excel, SQL
Step 4: Master Machine Learning
Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost
Unsupervised Learning: K-Means, Hierarchical Clustering, PCA
Deep Learning (optional): Use TensorFlow or PyTorch
Evaluation Metrics: Accuracy, AUC, Confusion Matrix, RMSE
Step 5: Learn Data Visualization & Storytelling
Python (matplotlib, seaborn, plotly)
Power BI / Tableau
Communicating insights clearly is as important as modeling
Step 6: Use Real Datasets & Projects
Work on projects using Kaggle, UCI, or public APIs
Examples:
Customer churn prediction
Sales forecasting
Sentiment analysis
Fraud detection
Step 7: Understand Cloud & MLOps (2025+ Skills)
Cloud: AWS (S3, EC2, SageMaker), GCP, or Azure
MLOps: Model deployment (Flask, FastAPI), CI/CD for ML, Docker basics
Step 8: Build Portfolio & Resume
Create GitHub repos with well-documented code
Post projects and blogs on Medium or LinkedIn
Prepare a data science-specific resume
Step 9: Apply Smartly
Focus on job roles like: Data Scientist, ML Engineer, Data Analyst → DS
Use platforms like LinkedIn, Glassdoor, Hirect, AngelList, etc.
Practice data science interviews: case studies, ML concepts, SQL + Python coding
Step 10: Keep Learning & Updating
Follow top newsletters: Data Elixir, Towards Data Science
Read papers (arXiv, Google Scholar) on trending topics: LLMs, AutoML, Explainable AI
Upskill with certifications (Google Data Cert, Coursera, DataCamp, Udemy)
Free Resources to learn Data Science
Kaggle Courses: https://www.kaggle.com/learn
CS50 AI by Harvard: https://cs50.harvard.edu/ai/
Fast.ai: https://course.fast.ai/
Google ML Crash Course: https://developers.google.com/machine-learning/crash-course
Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998
Data Science Books: https://t.iss.one/datalemur
React ❤️ for more
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🔅SQL Revision Notes for Interview💡
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7 High-Impact Portfolio Project Ideas for Aspiring Data Analysts
✅ Sales Dashboard – Use Power BI or Tableau to visualize KPIs like revenue, profit, and region-wise performance
✅ Customer Churn Analysis – Predict which customers are likely to leave using Python (Logistic Regression, EDA)
✅ Netflix Dataset Exploration – Analyze trends in content types, genres, and release years with Pandas & Matplotlib
✅ HR Analytics Dashboard – Visualize attrition, department strength, and performance reviews
✅ Survey Data Analysis – Clean, visualize, and derive insights from user feedback or product surveys
✅ E-commerce Product Analysis – Analyze top-selling products, revenue by category, and return rates
✅ Airbnb Price Predictor – Use machine learning to predict listing prices based on location, amenities, and ratings
These projects showcase real-world skills and storytelling with data.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
✅ Sales Dashboard – Use Power BI or Tableau to visualize KPIs like revenue, profit, and region-wise performance
✅ Customer Churn Analysis – Predict which customers are likely to leave using Python (Logistic Regression, EDA)
✅ Netflix Dataset Exploration – Analyze trends in content types, genres, and release years with Pandas & Matplotlib
✅ HR Analytics Dashboard – Visualize attrition, department strength, and performance reviews
✅ Survey Data Analysis – Clean, visualize, and derive insights from user feedback or product surveys
✅ E-commerce Product Analysis – Analyze top-selling products, revenue by category, and return rates
✅ Airbnb Price Predictor – Use machine learning to predict listing prices based on location, amenities, and ratings
These projects showcase real-world skills and storytelling with data.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤3
Python Cheatsheet
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