✅ Machine Learning Basics for Data Science 🤖📊

🔍 What is Machine Learning (ML)? 
ML lets computers learn from data to make predictions or decisions — without being explicitly programmed.

📂 Types of ML: 
1️⃣ Supervised Learning
⦁ Learns from labeled data (input → output)
⦁ Examples: Predicting house prices, spam detection
⦁ Algorithms: Linear Regression, Logistic Regression, Decision Trees, KNN

2️⃣ Unsupervised Learning
⦁ Finds hidden patterns in unlabeled data
⦁ Examples: Customer segmentation, topic modeling
⦁ Algorithms: K-Means, PCA, Hierarchical Clustering

3️⃣ Reinforcement Learning
⦁ Learns by trial-and-error to maximize rewards
⦁ Examples: Self-driving cars, game-playing bots

🧠 ML Workflow (Step-by-Step):
1. Define the problem
2. Collect & clean data
3. Choose relevant features
4. Select ML algorithm
5. Split data (Train/Test)
6. Train the model
7. Evaluate performance
8. Tune & deploy

📊 Key Concepts to Understand:
⦁ Features & Labels
⦁ Overfitting vs Underfitting
⦁ Train/Test Split & Cross-Validation
⦁ Evaluation metrics like Accuracy, MSE, R²

⚙️ Tools You’ll Use:
⦁ Python
⦁ NumPy, Pandas (data handling)
⦁ Matplotlib, Seaborn (visualization)
⦁ Scikit-learn (ML models)

💡 Mini Project Idea: 
Predict student scores based on study hours using Linear Regression.

Data Science Roadmap: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/1210

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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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Here are some essential SQL tips for beginners 👇👇

◆ Primary Key = Unique Key + Not Null constraint
◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’
◆ LIKE operator is for string data type
◆ COUNT(*), COUNT(1), COUNT(0) all are same
◆ All aggregate functions ignore the NULL values
◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
◆ For row level filtration use WHERE and aggregate level filtration use HAVING
◆ UNION ALL will include duplicates where as UNION excludes duplicates 
◆ If the results will not have any duplicates, use UNION ALL instead of UNION
◆ We have to alias the subquery if we are using the columns in the outer select query
◆ Subqueries can be used as output with NOT IN condition.
◆ CTEs look better than subqueries. Performance wise both are same.
◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
◆ Window functions work at ROW level.
◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.

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