1 Introduction to AI & ML

What is Artificial Intelligence (AI)?

• Making machines think, reason, and act like humans
  

• Examples:
  

  • Chatbots

  • Face recognition

  • Voice assistants

  • Recommendation systems
    
    

What is Machine Learning (ML)?

• Subset of AI

• Machines learn from data instead of being explicitly programmed
  
  

AI vs ML vs Deep Learning

• Term Meaning

• AI Broad concept of intelligent machines

• ML Learning from data

• DL ML using neural networks

2 Why Python for AI & ML?

• Simple syntax

• Huge ecosystem

• Industry standard

• Strong community
  
  

Popular Python Libraries

• NumPy – numerical operations

• Pandas – data handling

• Matplotlib / Seaborn – visualization

• Scikit-learn – ML algorithms

• TensorFlow / PyTorch – Deep Learning

  
  
  

3 Python Prerequisites for ML

Core Python

• Variables, Data Types

• Conditional statements

• Loops

• Functions

• Lists, Tuples, Sets, Dictionaries
  
  

Math for ML

• Mean, Median, Mode

• Variance, Standard Deviation

• Probability basics

• Linear equations
  
  

4 NumPy for Machine Learning

• Arrays vs Lists

• Array indexing & slicing

• Reshaping arrays

• Mathematical operations

• Broadcasting

  
  

5 Pandas for Data Analysis

• Series & DataFrame

• Reading CSV / Excel files

• Data filtering

• Handling missing values

• GroupBy operations

  
  

6 Data Visualization

• Line plots

• Bar charts

• Histograms

• Scatter plots

• Heatmaps

7 Machine Learning Basics

Types of Machine Learning

1. Supervised Learning

• Data with labels
  
  

• Examples:
  

  • Prediction

  • Classification
    
    

Algorithms:

• Linear Regression

• Logistic Regression

• KNN

• Decision Tree

• Random Forest

2. Unsupervised Learning

• Data without labels
  
  

Algorithms:

• K-Means Clustering

• Hierarchical Clustering

• PCA
  
  

3. Reinforcement Learning (Intro)

• Learning by reward & punishment
  
  

• Used in:
  

  • Games

  • Robotics
    

8 Machine Learning Workflow

1. Collect Data

2. Clean Data

3. Feature Selection

4. Split Dataset

5. Train Model

6. Test Model

7. Evaluate Accuracy

8. Improve Model
   

9 Scikit-learn (Core ML Library)

Common Steps

• Train-test split

• Model training

• Prediction

• Accuracy evaluation
  

10 Important ML Algorithms (With Use-Cases)

Algorithm

Linear Regression

House price prediction

Logistic Regression

Spam detection

KNN

Recommendation

Decision Tree

Rule-based decisions

Random Forest

High accuracy models

K-Means

Customer segmentation

11 Model Evaluation

• Accuracy

• Precision

• Recall

• F1-score

• Confusion Matrix
  

12 Introduction to Deep Learning

• Neural Networks

• Perceptron

• Activation Functions

• Backpropagation
  
  

Libraries:

• TensorFlow

• Keras

• PyTorch
  

13 Real-Time AI/ML Applications

• Stock price prediction

• Chatbots

• Image classification

• Fraud detection

• Recommendation systems
  

Projects

• Student marks prediction

• House price prediction

• Email spam classifier

• Customer segmentation

• Simple chatbot
  

Artificial Intelligence and Machine Learning

Duration: 4 months