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Machine Learning Basics
This course is a beginner-friendly introduction to the world of Machine Learning (ML). It’s designed for students, beginners, and curious minds who want to understand how machines learn from data. You don’t need to be a math expert or a coder — just basic Python knowledge and a willingness to learn. In this course, you’ll: Understand what machine learning is (in simple words) Explore different types of machine learning Learn how to work with data Build your first machine learning models using Python Use real-world examples to make things clear Apply what you learn in a final hands-on mini project By the end, you’ll be able to confidently explain ML concepts, build small ML programs, and understand how companies like Netflix or Google use ML in real life.
Course Chapters
Chapter 1: What is Machine Learning?
Machine Learning involves teaching computers to learn from examples, mirroring human learning processes. It is a subset of Artificial Intelligence, and encompasses various real-world applications like video recommendations and facial recognition. The chapter guides readers through an introductory understanding of machine learning, its working mechanism, and provides a simple implementation using Python.
Chapter 2: Types of Machine Learning
The chapter introduces the three primary types of machine learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. It provides definitions and real-life analogies for each type, explain how machines learn based on examples, and includes simple Python code examples for better understanding. The chapter emphasizes the importance of these learning types in making decisions based on data.
Chapter 3: Understanding NumPy for Machine Learning
NumPy is a powerful library used in Machine Learning for working with numerical data. It enables efficient creation and manipulation of arrays, which are faster and more versatile than traditional Python lists. The chapter covers basic operations, common functions, and practical applications of NumPy in ML, emphasizing its importance in performing fast calculations and managing datasets.
Chapter 4: Understanding Pandas for Machine Learning
Pandas is a pivotal library in Python for data analysis and manipulation, crucial for machine learning tasks. It provides efficient data structures, notably Series and DataFrames, which facilitate the organization and cleaning of data. Key functionalities include reading various data files, filtering, and handling missing values, as well as performing statistical analyses and grouping data to derive insights.
Chapter 5: Data Preprocessing for Machine Learning
Data preprocessing is a crucial step in machine learning that involves cleaning and altering raw data to ensure it is suitable for algorithms. It addresses missing values, encodes categorical data into numerical formats, and scales features to enhance the accuracy of predictions. Effective preprocessing enhances model performance and leads to more reliable outcomes.
Chapter 6: Supervised Learning – Linear Regression
Supervised Learning involves training models on labeled datasets where input features correspond to known outputs. Linear Regression is a fundamental supervised learning algorithm utilized to model relationships between dependent and independent variables. This chapter covers the implementation of linear regression in Python, along with the evaluation of model performance using metrics like Mean Squared Error and R² Score.
Chapter 7: Supervised Learning – Logistic Regression
Logistic Regression is a machine learning algorithm designed for binary classification problems, transforming categorical outcomes into probabilities using the sigmoid function. It distinguishes between regression and classification methods, showcases dataset preparation and model training, and evaluates models' performance through accuracy scores and confusion matrices.
Chapter 8: Model Evaluation Metrics
Evaluation metrics are crucial for assessing the performance of classification models. Various metrics such as confusion matrix, accuracy, precision, recall, F1 score, and ROC curve provide insights into a model's effectiveness, especially in cases where data may be imbalanced. Understanding and applying these metrics ensures a comprehensive evaluation beyond just basic accuracy.
Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance
The chapter focuses on the construction of a machine learning model aimed at predicting student performance based on various parameters. Key components include data loading, exploration, preprocessing, model building using logistic regression, and model evaluation with appropriate metrics. It culminates in visualizing results and even predicting outcomes for new data.