Logistic regression fits a logistic model to data and makes predictions about the probability of an event (between 0 and 1).

Naive Bayes uses Bayes Theorem to model the conditional relationship of each attribute to the class variable.

The k-Nearest Neighbor (kNN) method makes predictions by locating similar cases to a given data instance (using a similarity function) and returning the average or majority of the most similar data instances. The kNN algorithm can be used for classification or regression.

Classification and Regression Trees (CART) are constructed from a dataset by making splits that best separate the data for the classes or predictions being made. The CART algorithm can be used for classification or regression.

Support Vector Machines (SVM) are a method that uses points in a transformed problem space that best separate classes into two groups. Classification for multiple classes is supported by a one-vs-all method. SVM also supports regression by modeling the function with a minimum amount of allowable error.

Many data scientists don't know how to push ML models to production. Here's the recipe 👇

𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀

🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data
🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time
🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model
🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github
🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API
🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API
🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker
🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs
🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring

𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲

𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.

𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁

Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation noscripts to Github for reproducibility.

𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻

Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment

𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁

Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.

𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴

Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.

Important questions to ace your machine learning interview with an approach to answer:

1. Machine Learning Project Lifecycle:
   - Define the problem
   - Gather and preprocess data
   - Choose a model and train it
   - Evaluate model performance
   - Tune and optimize the model
   - Deploy and maintain the model

2. Supervised vs Unsupervised Learning:
   - Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
   - Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).

3. Evaluation Metrics for Regression:
   - Mean Absolute Error (MAE)
   - Mean Squared Error (MSE)
   - Root Mean Squared Error (RMSE)
   - R-squared (coefficient of determination)

4. Overfitting and Prevention:
   - Overfitting: Model learns the noise instead of the underlying pattern.
   - Prevention: Use simpler models, cross-validation, regularization.

5. Bias-Variance Tradeoff:
   - Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.

6. Cross-Validation:
   - Technique to assess model performance by splitting data into multiple subsets for training and validation.

7. Feature Selection Techniques:
   - Filter methods (e.g., correlation analysis)
   - Wrapper methods (e.g., recursive feature elimination)
   - Embedded methods (e.g., Lasso regularization)

8. Assumptions of Linear Regression:
   - Linearity
   - Independence of errors
   - Homoscedasticity (constant variance)
   - No multicollinearity

9. Regularization in Linear Models:
   - Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.

10. Classification vs Regression:
    - Classification: Predicts a categorical outcome (e.g., class labels).
    - Regression: Predicts a continuous numerical outcome (e.g., house price).

11. Dimensionality Reduction Algorithms:
    - Principal Component Analysis (PCA)
    - t-Distributed Stochastic Neighbor Embedding (t-SNE)

12. Decision Tree:
    - Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.

13. Ensemble Methods:
    - Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).

14. Handling Missing or Corrupted Data:
    - Imputation (e.g., mean substitution)
    - Removing rows or columns with missing data
    - Using algorithms robust to missing values

15. Kernels in Support Vector Machines (SVM):
    - Linear kernel
    - Polynomial kernel
    - Radial Basis Function (RBF) kernel

Data Science Interview Resources
👇👇
https://topmate.io/coding/914624

Like for more 😄

🔥 Data Science Roadmap 2025

Step 1: 🐍 Python Basics
Step 2: 📊 Data Analysis (Pandas, NumPy)
Step 3: 📈 Data Visualization (Matplotlib, Seaborn)
Step 4: 🤖 Machine Learning (Scikit-learn)
Step 5: � Deep Learning (TensorFlow/PyTorch)
Step 6: 🗃️ SQL & Big Data (Spark)
Step 7: 🚀 Deploy Models (Flask, FastAPI)
Step 8: 📢 Showcase Projects
Step 9: 💼 Land a Job!

🔓 Pro Tip: Compete on Kaggle

#datascience

Understanding Popular ML Algorithms:

1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes.

2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not.

3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion.

4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions.

5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs.

6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too!

7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech.

8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things.

9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

Want to make a transition to a career in data?

Here is a 7-step plan for each data role

Data Scientist

Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.

Data Analyst

Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.

Data Engineer

SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.

#data

Best practices for writing SQL queries:

Join for more: https://news.1rj.ru/str/learndataanalysis

1- Write SQL keywords in capital letters.

2- Use table aliases with columns when you are joining multiple tables.

3- Never use select *, always mention list of columns in select clause.

4- Add useful comments wherever you write complex logic. Avoid too many comments.

5- Use joins instead of subqueries when possible for better performance.

6- Create CTEs instead of multiple sub queries , it will make your query easy to read.

7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.

8- Never use order by in sub queries , It will unnecessary increase runtime.

9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.

SQL Basics: https://news.1rj.ru/str/sqlanalyst/105

𝗛𝗼𝘄 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 (𝗘𝘃𝗲𝗻 𝗶𝗳 𝗬𝗼𝘂’𝗿𝗲 𝗮 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿!) 📊

Wanna break into data science but feel overwhelmed by too many courses, buzzwords, and conflicting advice? You’re not alone.

Here’s the truth: You don’t need a PhD or 10 certifications. You just need the right skills in the right order.

Let me show you a proven 5-step roadmap that actually works for landing data science roles (even entry-level) 👇

🔹 Step 1: Learn the Core Tools (This is Your Foundation)

Focus on 3 key tools first—don’t overcomplicate:

✅ Python – NumPy, Pandas, Matplotlib, Seaborn
✅ SQL – Joins, Aggregations, Window Functions
✅ Excel – VLOOKUP, Pivot Tables, Data Cleaning

🔹 Step 2: Master Data Cleaning & EDA (Your Real-World Skill)

Real data is messy. Learn how to:

✅ Handle missing data, outliers, and duplicates
✅ Visualize trends using Matplotlib/Seaborn
✅ Use groupby(), merge(), and pivot_table()

🔹 Step 3: Learn ML Basics (No Fancy Math Needed)

Stick to core algorithms first:

✅ Linear & Logistic Regression
✅ Decision Trees & Random Forest
✅ KMeans Clustering + Model Evaluation Metrics

🔹 Step 4: Build Projects That Prove Your Skills

One strong project > 5 courses. Create:

✅ Sales Forecasting using Time Series
✅ Movie Recommendation System
✅ HR Analytics Dashboard using Python + Excel
📍 Upload them on GitHub. Add visuals, write a good README, and share on LinkedIn.

🔹 Step 5: Prep for the Job Hunt (Your Personal Brand Matters)

✅ Create a strong LinkedIn profile with keywords like “Aspiring Data Scientist | Python | SQL | ML”
✅ Add GitHub link + Highlight your Projects
✅ Follow Data Science mentors, engage with content, and network for referrals

🎯 No shortcuts. Just consistent baby steps.

Every pro data scientist once started as a beginner. Stay curious, stay consistent.

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

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Machine learning powers so many things around us – from recommendation systems to self-driving cars!

But understanding the different types of algorithms can be tricky.

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

𝟏. 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

𝐒𝐨𝐦𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.

𝟐. 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.

𝐒𝐨𝐦𝐞 𝐩𝐨𝐩𝐮𝐥𝐚𝐫 𝐮𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.

𝟑. 𝐒𝐞𝐦𝐢-𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

𝐂𝐨𝐦𝐦𝐨𝐧 𝐬𝐞𝐦𝐢-𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.

𝟒. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

𝐏𝐨𝐩𝐮𝐥𝐚𝐫 𝐫𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.

ENJOY LEARNING 👍👍