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Machine learning made accessible to everyone

Course Overview

This Machine Learning course provides a thorough introduction to the field, starting with an overview of data and Google Colab, a platform used for writing and executing Python in the browser. The course is designed to equip learners with a solid foundation in machine learning concepts, algorithms, and techniques.

Understanding Data and Google Colab

The course begins by introducing learners to the concept of data and its importance in machine learning. Learners will gain an understanding of how data can be represented, stored, and manipulated. Additionally, they will learn about Google Colab, a powerful platform that enables writing and executing Python code directly in the browser.

The Basics of Machine Learning

Following the introduction to data and Google Colab, the course delves into the basics of machine learning. Learners will be introduced to key concepts such as:

  • Features: The characteristics or attributes of a dataset that are used to train a model.
  • Classification: A type of supervised learning where the goal is to predict a categorical label for an input data point.
  • Regression: A type of supervised learning where the goal is to predict a continuous value for an input data point.

The course will guide learners through the process of training a model and preparing data for machine learning tasks. Learners will gain hands-on experience with popular libraries such as Pandas, NumPy, and Matplotlib.

Machine Learning Algorithms

The course covers several machine learning algorithms, each followed by a practical implementation session:

K-Nearest Neighbors (KNN)

  • A simple algorithm that predicts the output based on the majority vote of its k-nearest neighbors.
  • Practical implementation: Learners will implement KNN using Python and a sample dataset.

Naive Bayes

  • A family of algorithms that use Bayes’ theorem to calculate probabilities.
  • Practical implementation: Learners will implement Naive Bayes using Python and a sample dataset.

Logistic Regression

  • A popular algorithm for binary classification problems.
  • Practical implementation: Learners will implement Logistic Regression using Python and a sample dataset.

Support Vector Machine (SVM)

  • A powerful algorithm that can be used for both linear and non-linear classification problems.
  • Practical implementation: Learners will implement SVM using Python and a sample dataset.

Neural Networks

The course then transitions into Neural Networks, introducing TensorFlow, a popular open-source platform for machine learning. Learners will gain hands-on experience in building a Classification Neural Network using TensorFlow.

Building a Classification Neural Network

  • Learners will learn how to build a neural network from scratch using TensorFlow.
  • Practical implementation: Learners will implement a simple neural network using Python and the TensorFlow library.

Linear Regression

The course also covers Linear Regression, a fundamental algorithm in machine learning. Learners will demonstrate how to implement it and use a neuron for Linear Regression.

Implementing Linear Regression

  • Learners will learn how to implement linear regression using Python.
  • Practical implementation: Learners will implement linear regression using Python and a sample dataset.

Using a Neuron for Linear Regression

  • Learners will learn how to use a neuron to perform linear regression.
  • Practical implementation: Learners will implement a simple neural network using Python and the TensorFlow library.

Regression Neural Network

The course further explores how to build a Regression Neural Network using TensorFlow. Learners will gain hands-on experience in implementing a regression model using a neural network.

Building a Regression Neural Network

  • Learners will learn how to build a regression neural network from scratch using TensorFlow.
  • Practical implementation: Learners will implement a simple regression neural network using Python and the TensorFlow library.

K-Means Clustering and Principal Component Analysis (PCA)

The course concludes with practical implementations of K-Means Clustering and Principal Component Analysis (PCA), two techniques used for data clustering and dimensionality reduction, respectively:

K-Means Clustering

  • Learners will learn how to implement k-means clustering using Python.
  • Practical implementation: Learners will implement k-means clustering using Python and a sample dataset.

Principal Component Analysis (PCA)

  • Learners will learn how to implement PCA using Python.
  • Practical implementation: Learners will implement PCA using Python and a sample dataset.

Course Conclusion

Throughout the course, learners gain hands-on experience in implementing various machine learning algorithms. The course provides a solid foundation for further exploration in the field of machine learning.

About the Instructor

The Machine Learning course was developed by Kylie Ying, who can be found on her channel, ycubed.