What is Machine Learning?
Machine Learning = Is the process of teaching computers to learn patterns from data without explicit programming.
- Instead of rules → we give data → model learns patterns.
Types of Machine Learning:
✅ 1. Supervised Learning:
Where the Model learns from labeled data:
Examples:
✔ Predict house price
✔ Email spam detection
Common Algorithms:
- Linear Regression
- Logistic Regression
- Decision Trees
✅ 2. Unsupervised Learning:
- This is where your model finds patterns in unlabeled data:
Examples:
✔ Customer segmentation
✔ Grouping similar data
Common Algorithms:
- K-Means Clustering
- Hierarchical Clustering
✅ 3. Reinforcement Learning:
👉 Model learns through rewards and penalties
Example:
✔ Game-playing AI
ML Workflow:
👉 Step-by-step process:
1️⃣ Collect Data
2️⃣ Clean Data
3️⃣ Perform EDA
4️⃣ Split Data (Train/Test)
5️⃣ Train Model
6️⃣ Evaluate Model
7️⃣ Deploy Model
🔹 4. Train-Test Split
from sklearn.model_selection import train_test_split
👉 Used to divide data into:
✔ Training data (80%)
✔ Testing data (20%)
6. Why is ML important?
✔ Automates decision-making
✔ Used in AI, recommendations, predictions
✔ Core of modern tech
Happy Learning:
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