Machine Learning for Beginners
Start your AI journey with hands-on projects, Python programming, and foundational algorithms.
What You'll Learn
Python Foundations
Master core programming concepts including variables, loops, functions, and data structures with interactive coding exercises.
Data Science Essentials
Explore NumPy for arrays, Pandas for dataframes, and Matplotlib/Seaborn for visualization with real-world datasets.
ML Algorithms
Implement linear regression, decision trees, and k-means clustering using Scikit-Learn.
Model Evaluation
Learn accuracy metrics, cross-validation, and model tuning techniques for optimizing performance.
Hands-On Learning
Apply concepts to real projects in beginner-friendly AI domains.
Iris Classifier
Build your first machine learning model using flower measurements to predict species with Scikit-Learn.
Predict House Prices
Create regression models using housing features to predict market values from historical data.
Digits Recognizer
Train a neural network model to recognize handwritten digits using the MNIST dataset.
Structured Learning Path
Progress through six modules with interactive coding environments and guided exercises.
Module 1
Python Foundations
- Control Structures
- Data Types & Functions
- File I/O & JSON
Module 2
Data Analysis
- Pandas DataFrames
- Data Cleaning
- Exploratory Visualization
Module 3
Machine Learning
- Supervised/Unsupervised
- Preprocessing & Pipelines
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