What You'll Learn

1. Introduction to AI & ML

Understand the difference between AI, ML, and Deep Learning. Learn about supervised, unsupervised, and reinforcement learning paradigms.

  • History and evolution of AI
  • Types of machine learning
  • Real-world applications

2. Python for Data Science

Essential Python libraries and tools for machine learning development.

# Essential imports for ML
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

3. Data Preprocessing

Learn to clean, transform, and prepare data for machine learning models.

# Data cleaning example
df = pd.read_csv('data.csv')
df.dropna(inplace=True)  # Remove missing values
df['feature'] = (df['feature'] - df['feature'].mean()) / df['feature'].std()  # Normalize

4. Your First ML Model

Build and evaluate a simple linear regression model.

# Simple linear regression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)