Introduction to Machine Learning

Course Description

Embark on an exciting journey into the world of machine learning with this introductory course that brings science fiction to reality. Discover how everyday technologies like speech recognition, drones, and self-driving cars have revolutionized our world through data analysis and decision-making algorithms. This hands-on course will guide you through the fascinating realm of data exploration, relationship discovery, and basic algorithm implementation, offering a fresh perspective on how machines can understand and interpret the world around us.

What Students Will Learn

  • Mastery of Python programming and the Colab notebook environment
  • Understanding of dependent and independent variables
  • Creation of linear and polynomial regression models for data relationships
  • Recognition of data distribution patterns
  • Identification and management of noise in data distributions
  • Implementation of classification models for data categorization
  • Practical application of machine learning concepts to real-world scenarios

Prerequisites

  • High school (grade 8) math proficiency
  • Understanding of equations of lines and polynomial curves
  • Ability to calculate averages and standard deviations
  • Curiosity about machine learning
  • Willingness to experiment with computer programming

Course Coverage

  • Introduction to various forms of data and their analysis
  • Python programming basics for data exploration and visualization
  • Relationship modeling between variables using regression techniques
  • Understanding data distributions and their implications
  • Noise identification and management in data sets
  • Classification algorithms for grouping data
  • Practical applications of machine learning in everyday technologies

Who This Course Is For

This course is ideal for high school students, career changers, and anyone with a curiosity about machine learning. No prior programming experience is required, making it perfect for beginners who want to explore the exciting field of artificial intelligence and data analysis.

Real-World Applications

  • Developing predictive models for business forecasting
  • Creating classification systems for medical diagnoses
  • Designing recommendation algorithms for e-commerce platforms
  • Implementing speech recognition software for virtual assistants
  • Enhancing autonomous vehicle technologies
  • Improving data-driven decision-making in various industries

Syllabus

Module 1: How to represent and manipulate data

  • Examples of numerical data
  • Introduction to Python and Colab notebook
  • Data manipulation techniques (loading, filtering, grouping)
  • Correlation analysis
  • Data visualization (line plots, scatter plots, histograms, correlation matrices)

Module 2: Reverse engineering nature

  • Understanding dependent and independent variables
  • Linear and polynomial model intuition
  • Implementing linear regression using Python libraries
  • Model quality comparison (mean-squared-error and R^2 values)
  • Higher-order polynomial fitting
  • Overfitting concepts

Module 3: Distributions and Latent Variables

  • Uniform and Gaussian distributions
  • Distribution mean and standard deviation
  • Noise in distributions (biased and unbiased)

Module 4: How machines think

  • Data categorization based on conditions
  • Linear regression for binary classification
  • Support vector classifier implementation
  • Logistic regression for probabilistic classification
  • Training and test set division techniques

This comprehensive course will equip you with the foundational knowledge and practical skills needed to embark on an exciting career in machine learning and artificial intelligence.