Machine Learning (ML) – Introduction

Machine Learning is a part of Artificial Intelligence (AI) where computers learn from data and improve their performance without being explicitly programmed.

👉 Instead of writing rules manually, we train models using data.

Simple example:

• Traditional programming: Rules + Data → Answer
• Machine Learning: Data + Answers → Rules (Model)

🧠 Why Machine Learning Is Important

• Automates decision-making
• Finds patterns humans can’t easily see
• Improves over time with more data

Used in:

• Recommendation systems (Netflix, YouTube)
• Spam detection
• Image & face recognition
• Voice assistants
• Fraud detection

📊 Types of Machine Learning

1️⃣ Supervised Learning

Model learns from labeled data (input + output known).

Examples:

• Email spam detection
• House price prediction

Common algorithms:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)

2️⃣ Unsupervised Learning

Model works with unlabeled data (no predefined output).

Examples:

• Customer segmentation
• Pattern discovery

Common algorithms:

• K-Means Clustering
• Hierarchical Clustering
• PCA (Dimensionality Reduction)

3️⃣ Reinforcement Learning

Model learns by trial and error using rewards and penalties.

Examples:

• Game AI (chess, Go)
• Robotics
• Self-driving simulations

🔁 Machine Learning Workflow

1. Collect data
2. Clean & preprocess data
3. Split data (train / test)
4. Choose algorithm
5. Train the model
6. Evaluate performance
7. Improve & deploy

🧰 Tools & Technologies

• Languages: Python (most popular), R
• Libraries:
• NumPy, Pandas
• Scikit-learn
• TensorFlow, PyTorch
• Visualization: Matplotlib, Seaborn

📈 Key ML Concepts (Interview Important)

• Feature & label
• Training vs testing data
• Overfitting & underfitting
• Accuracy, precision, recall
• Bias vs variance
• Model evaluation
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