7 Steps of the Machine Learning Process

Data Collection: The process of extracting raw datasets for the machine learning task. This data can come from a variety of places, ranging from open-source online resources to paid crowdsourcing. The first step of the machine learning process is arguably the most important. If the data you collect is poor quality or irrelevant, then the model you train will be poor quality as well.

Data Processing and Preparation: Once you’ve gathered the relevant data, you need to process it and make sure that it is in a usable format for training a machine learning model. This includes handling missing data, dealing with outliers, etc.

Feature Engineering: Once you’ve collected and processed your dataset, you will likely need to transform some of the features (and sometimes even drop some features) in order to optimize how well a model can be trained on the data.

Model Selection: Based on the dataset, you will choose which model architecture to use. This is one of the main tasks of industry engineers. Rather than attempting to come up with a completely novel model architecture, most tasks can be thoroughly performed with an existing architecture (or combination of model architectures).

Model Training and Data Pipeline: After selecting the model architecture, you will create a data pipeline for training the model. This means creating a continuous stream of batched data observations to efficiently train the model. Since training can take a long time, you want your data pipeline to be as efficient as possible.

Model Validation: After training the model for a sufficient amount of time, you will need to validate the model’s performance on a held-out portion of the overall dataset. This data needs to come from the same underlying distribution as the training dataset, but needs to be different data that the model has not seen before.

Model Persistence: Finally, after training and validating the model’s performance, you need to be able to properly save the model weights and possibly push the model to production. This means setting up a process with which new users can easily use your pre-trained model to make predictions.

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✅ Machine Learning Roadmap: Step-by-Step Guide to Master ML 🤖📊

Whether you’re aiming to be a data scientist, ML engineer, or AI specialist — this roadmap has you covered 👇

📍 1. Math Foundations
⦁ Linear Algebra (vectors, matrices)
⦁ Probability & Statistics basics
⦁ Calculus essentials (derivatives, gradients)

📍 2. Programming & Tools
⦁ Python basics & libraries (NumPy, Pandas)
⦁ Jupyter notebooks for experimentation

📍 3. Data Preprocessing
⦁ Data cleaning & transformation
⦁ Handling missing data & outliers
⦁ Feature engineering & scaling

📍 4. Supervised Learning
⦁ Regression (Linear, Logistic)
⦁ Classification algorithms (KNN, SVM, Decision Trees)
⦁ Model evaluation (accuracy, precision, recall)

📍 5. Unsupervised Learning
⦁ Clustering (K-Means, Hierarchical)
⦁ Dimensionality reduction (PCA, t-SNE)

📍 6. Neural Networks & Deep Learning
⦁ Basics of neural networks
⦁ Frameworks: TensorFlow, PyTorch
⦁ CNNs for images, RNNs for sequences

📍 7. Model Optimization
⦁ Hyperparameter tuning
⦁ Cross-validation & regularization
⦁ Avoiding overfitting & underfitting

📍 8. Natural Language Processing (NLP)
⦁ Text preprocessing
⦁ Common models: Bag-of-Words, Word Embeddings
⦁ Transformers & GPT models basics

📍 9. Deployment & Production
⦁ Model serialization (Pickle, ONNX)
⦁ API creation with Flask or FastAPI
⦁ Monitoring & updating models in production

📍 10. Ethics & Bias
⦁ Understand data bias & fairness
⦁ Responsible AI practices

📍 11. Real Projects & Practice
⦁ Kaggle competitions
⦁ Build projects: Image classifiers, Chatbots, Recommendation systems

📍 12. Apply for ML Roles
⦁ Prepare resume with projects & results
⦁ Practice technical interviews & coding challenges
⦁ Learn business use cases of ML

💡 Pro Tip: Combine ML skills with SQL and cloud platforms like AWS or GCP for career advantage.

💬 Double Tap ♥️ For More!

🤖 Want to become a Machine Learning Engineer? This free roadmap will get you there! 🚀

📚 Math & Statistics
⦁ Probability 🎲
⦁ Inferential statistics 📊
⦁ Regression analysis 📈
⦁ A/B testing 🔍
⦁ Bayesian stats 🔢
⦁ Calculus & Linear algebra 🧮🔠

🐍 Python
⦁ Variables & data types ✏️
⦁ Control flow 🔄
⦁ Functions & modules 🔧
⦁ Error handling ❌
⦁ Data structures 🗂️
⦁ OOP basics 🧱
⦁ APIs 🌐
⦁ Algorithms & data structures 🧠

🧪 ML Prerequisites
⦁ EDA with NumPy & Pandas 🔍
⦁ Data visualization 📉
⦁ Feature engineering 🛠️
⦁ Encoding types 🔐

⚙️ Machine Learning Fundamentals
⦁ Supervised: Linear Regression, KNN, Decision Trees 📊
⦁ Unsupervised: K-Means, PCA, Hierarchical Clustering 🧠
⦁ Reinforcement: Q-Learning, DQN 🕹️
⦁ Solve regression 📈 & classification 🧩 problems

🧠 Neural Networks
⦁ Feedforward networks 🔄
⦁ CNNs for images 🖼️
⦁ RNNs for sequences 📚 
  Use TensorFlow, Keras & PyTorch

🕸️ Deep Learning
⦁ CNNs, RNNs, LSTMs for advanced tasks

🚀 ML Project Deployment
⦁ Version control 🗃️
⦁ CI/CD & automated testing 🔄🚚
⦁ Monitoring & logging 🖥️
⦁ Experiment tracking 🧪
⦁ Feature stores & pipelines 🗂️🛠️
⦁ Infrastructure as Code 🏗️
⦁ Model serving & APIs 🌐

💡 React ❤️ for more!

If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇

1️⃣ 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.


2️⃣ 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 experiments—hypothesis formation, sample size calculation, and sample biases.


3️⃣ 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.


4️⃣ 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.


5️⃣ 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.


6️⃣ 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.

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✅ Top 5 Real-World Data Science Projects for Beginners 📊🚀

1️⃣ Customer Churn Prediction 
🎯 Predict if a customer will leave (telecom, SaaS) 
📁 Dataset: Telco Customer Churn (Kaggle) 
🔍 Techniques: data cleaning, feature selection, logistic regression, random forest 
🌐 Bonus: Build a Streamlit app for churn probability

2️⃣ House Price Prediction 
🎯 Predict house prices from features like area & location 
📁 Dataset: Ames Housing or Kaggle House Price 
🔍 Techniques: EDA, feature engineering, regression models like XGBoost 
📊 Bonus: Visualize with Seaborn

3️⃣ Movie Recommendation System 
🎯 Suggest movies based on user taste 
📁 Dataset: MovieLens or TMDB 
🔍 Techniques: collaborative filtering, cosine similarity, SVD matrix factorization 
💡 Bonus: Streamlit search bar for movie suggestions

4️⃣ Sales Forecasting 
🎯 Predict future sales for products or stores 
📁 Dataset: Retail sales CSV (Walmart) 
🔍 Techniques: time series analysis, ARIMA, Prophet 
📅 Bonus: Plotly charts for trends

5️⃣ Titanic Survival Prediction 
🎯 Predict which passengers survived the Titanic 
📁 Dataset: Titanic Kaggle 
🔍 Techniques: data preprocessing, model training, feature importance 
📉 Bonus: Compare models with accuracy & F1 scores

💼 Why do these projects matter?
⦁  Solve real-world problems
⦁  Practice end-to-end pipelines
⦁  Make your GitHub & portfolio shine

🛠 Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, Streamlit, GitHub

💬 Tap ❤️ for more!

🚀 AI Journey Contest 2025: Test your AI skills!

Join our international online AI competition. Register now for the contest! Award fund — RUB 6.5 mln!

Choose your track:

· 🤖 Agent-as-Judge — build a universal “judge” to evaluate AI-generated texts.

· 🧠 Human-centered AI Assistant — develop a personalized assistant based on GigaChat that mimics human behavior and anticipates preferences. Participants will receive API tokens and a chance to get an additional 1M tokens.

· 💾 GigaMemory — design a long-term memory mechanism for LLMs so the assistant can remember and use important facts in dialogue.

Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.

How to Join
1. Register here: http://bit.ly/46mtD5L
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.

🚀 Ready for a challenge? Join a global developer community and show your AI skills!

What 𝗠𝗟 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 are commonly asked in 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀?

These are fair game in interviews at 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀, 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 & 𝗹𝗮𝗿𝗴𝗲 𝘁𝗲𝗰𝗵.

𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
- Supervised vs. Unsupervised Learning
- Overfitting and Underfitting
- Cross-validation
- Bias-Variance Tradeoff
- Accuracy vs Interpretability
- Accuracy vs Latency

𝗠𝗟 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines
- K-Nearest Neighbors
- Naive Bayes
- Linear Regression
- Ridge and Lasso Regression
- K-Means Clustering
- Hierarchical Clustering
- PCA

𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗦𝘁𝗲𝗽𝘀
- EDA
- Data Cleaning (e.g. missing value imputation)
- Data Preprocessing (e.g. scaling)
- Feature Engineering (e.g. aggregation)
- Feature Selection (e.g. variable importance)
- Model Training (e.g. gradient descent)
- Model Evaluation (e.g. AUC vs Accuracy)
- Model Productionization

𝗛𝘆𝗽𝗲𝗿𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗧𝘂𝗻𝗶𝗻𝗴
- Grid Search
- Random Search
- Bayesian Optimization

𝗠𝗟 𝗖𝗮𝘀𝗲𝘀
- [Capital One] Detect credit card fraudsters
- [Amazon] Forecast monthly sales
- [Airbnb] Estimate lifetime value of a guest

Like if you need similar content 😄👍

Most Asked SQL Interview Questions at MAANG Companies🔥🔥

Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:

1. How do you retrieve all columns from a table?

SELECT * FROM table_name;

2. What SQL statement is used to filter records?

SELECT * FROM table_name
WHERE condition;

The WHERE clause is used to filter records based on a specified condition.

3. How can you join multiple tables? Describe different types of JOINs.

SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;

Types of JOINs:

1. INNER JOIN: Returns records with matching values in both tables

SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;

2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.

SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;

3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.

SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;

4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.

SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;

4. What is the difference between WHERE & HAVING clauses?

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;

5. How do you calculate average, sum, minimum & maximum values in a column?

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;

Hope it helps :)

✅ Data Science Learning Checklist 🧠🔬

📚 Foundations
⦁ What is Data Science & its workflow
⦁ Python/R programming basics
⦁ Statistics & Probability fundamentals
⦁ Data wrangling and cleaning

📊 Data Manipulation & Analysis
⦁ NumPy & Pandas
⦁ Handling missing data & outliers
⦁ Data aggregation & grouping
⦁ Exploratory Data Analysis (EDA)

📈 Data Visualization
⦁ Matplotlib & Seaborn basics
⦁ Interactive viz with Plotly or Tableau
⦁ Dashboard creation
⦁ Storytelling with data

🤖 Machine Learning
⦁ Supervised vs Unsupervised learning
⦁ Regression & classification algorithms
⦁ Model evaluation & validation (cross-validation, metrics)
⦁ Feature engineering & selection

⚙️ Advanced Topics
⦁ Natural Language Processing (NLP) basics
⦁ Time Series analysis
⦁ Deep Learning fundamentals
⦁ Model deployment basics

🛠️ Tools & Platforms
⦁ Jupyter Notebook / Google Colab
⦁ scikit-learn, TensorFlow, PyTorch
⦁ SQL for data querying
⦁ Git & GitHub

📁 Projects to Build
⦁ Customer Segmentation
⦁ Sales Forecasting
⦁ Sentiment Analysis
⦁ Fraud Detection

💡 Practice Platforms:
⦁ Kaggle
⦁ DataCamp
⦁ Datasimplifier

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✅ Data Scientists in Your 20s – Avoid This Trap 🚫🧠

🎯 The Trap? → Passive Learning 
Feels like you’re learning but not truly growing.

🔍 Example:
⦁ Watching endless ML tutorial videos
⦁ Saving notebooks without running or understanding
⦁ Joining courses but not coding models
⦁ Reading research papers without experimenting

End result? 
❌ No models built from scratch 
❌ No real data cleaning done 
❌ No insights or reports delivered

This is passive learning — absorbing without applying. It builds false confidence and slows progress.

🛠️ How to Fix It: 
1️⃣ Learn by doing: Grab real datasets (Kaggle, UCI, public APIs) 
2️⃣ Build projects: Classification, regression, clustering tasks 
3️⃣ Document findings: Share explanations like you’re presenting to stakeholders 
4️⃣ Get feedback: Post code & reports on GitHub, Kaggle, or LinkedIn 
5️⃣ Fail fast: Debug models, tune hyperparameters, iterate frequently

📌 In your 20s, build practical data intuition — not just theory or certificates.

Stop passive watching. 
Start real modeling. 
Start storytelling with data.

That’s how data scientists grow fast in the real world! 🚀

💬 Tap ❤️ if this resonates with you!

AI vs ML vs Deep Learning 🤖

You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not.

AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning.

ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed.

Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc.

Think of it this way:
AI = Science
ML = A chapter in the science
Deep Learning = A paragraph in that chapter.

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If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇

1️⃣ 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.


2️⃣ 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 experiments—hypothesis formation, sample size calculation, and sample biases.


3️⃣ 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.


4️⃣ 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.


5️⃣ 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.


6️⃣ 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.

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The key to starting your data science career:

❌It's not your education
❌It's not your experience

It's how you apply these principles:

1. Learn by working on real datasets
2. Build a portfolio of projects
3. Share your work and insights publicly

No one starts a data scientist, but everyone can become one.

If you're looking for a career in data science, start by:

⟶ Watching tutorials and courses
⟶ Reading expert blogs and papers
⟶ Doing internships or Kaggle competitions
⟶ Building end-to-end projects
⟶ Learning from mentors and peers

You'll be amazed at how quickly you’ll gain confidence and start solving real-world problems.

So, start today and let your data science journey begin!

React ❤️ for more helpful tips

✅ Machine Learning A-Z: From Algorithm to Zenith! 🤖🧠

A: Algorithm - A step-by-step procedure used by a machine learning model to learn patterns from data.

B: Bias - A systematic error in a model's predictions, often stemming from flawed assumptions in the training data or the model itself.

C: Classification - A type of supervised learning where the goal is to assign data points to predefined categories.

D: Deep Learning - A subfield of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to analyze data.

E: Ensemble Learning - A technique that combines multiple machine learning models to improve overall predictive performance.

F: Feature Engineering - The process of selecting, transforming, and creating relevant features from raw data to improve model performance.

G: Gradient Descent - An optimization algorithm used to find the minimum of a function (e.g., the error function of a machine learning model) by iteratively adjusting parameters.

H: Hyperparameter Tuning - The process of finding the optimal set of hyperparameters for a machine learning model to maximize its performance.

I: Imputation - The process of filling in missing values in a dataset with estimated values.

J: Jaccard Index - A measure of similarity between two sets, often used in clustering and recommendation systems.

K: K-Fold Cross-Validation - A technique for evaluating model performance by partitioning the data into k subsets and training/testing the model k times, each time using a different subset as the test set.

L: Loss Function - A function that quantifies the error between the predicted and actual values, guiding the model's learning process.

M: Model - A mathematical representation of a real-world process or phenomenon, learned from data.

N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.

O: Overfitting - A phenomenon where a model learns the training data too well, resulting in poor performance on unseen data.

P: Precision - A metric that measures the proportion of correctly predicted positive instances out of all instances predicted as positive.

Q: Q-Learning - A reinforcement learning algorithm used to learn an optimal policy by estimating the expected reward for each action in a given state.

R: Regression - A type of supervised learning where the goal is to predict a continuous numerical value.

S: Supervised Learning - A machine learning approach where an algorithm learns from labeled training data.

T: Training Data - The dataset used to train a machine learning model.

U: Unsupervised Learning - A machine learning approach where an algorithm learns from unlabeled data by identifying patterns and relationships.

V: Validation Set - A subset of the training data used to tune hyperparameters and monitor model performance during training.

W: Weights - Parameters within a machine learning model that are adjusted during training to minimize the loss function.

X: XGBoost (Extreme Gradient Boosting) - A highly optimized and scalable gradient boosting algorithm widely used in machine learning competitions and real-world applications.

Y: Y-Variable - The dependent variable or target variable that a machine learning model is trying to predict.

Z: Zero-Shot Learning - A type of machine learning where a model can recognize or classify objects it has never seen during training.

Tap ❤️ for more!