Course Link: https://www.coursera.org/learn/art-science-ml

Art and Science of Machine Learning

This course will cover the art and science of machine learning in high school science. We will learn about the computational methods used to design and train models for supervised learning and unsupervised learning in Python. We will also cover the basic techniques for modeling and exploring the data, as well as the types of models that are used. We will also cover the “meat” of the course, modeling and exploring the data. We will cover modeling problems in Python, introducing conditional and unconditional rules, as well as an introduction to supervised learning. We will learn about the state-of-the-art in Python models and how to use them in the real world. We will cover modeling, unsupervised learning, and exploration of the data, with an emphasis on modeling problems that arise from the data itself. We will cover modeling problems in Python, introduction to supervised learning, and an introduction to unsupervised learning. We will cover the state-of-the-art in Python models and how to use them in the real world. We will cover modeling, unsupervised learning, and exploration of the data, with an emphasis on modeling problems that arise from the data itself.

Prerequisites

Learners should be comfortable writing machine learning models using Python, having some basic knowledge of Python programming and statistics, and have previous experience with Python programming (including working with NumPy and Pandas). We will assume that learners are comfortable working with the typical “programming the same thing over and over” routine that is common in machine learning. This means you should be comfortable writing machine learning code in Python, and should have experience working with data in a NumPy/Pandas environment.

Suggested Read

C++ Programming, Chapter 9.3, Part 1, “Introduction to C++” by Michael F. Neibut (NIIHB, C++AM)

This course has been designed to be enjoyable even for those that have mastered advanced computer science. Much of the material will still be familiar to those of you that have taken introductory courses in computer science, but the course will challenge you to think critically and creatively about the issues and problems that you will face in the course. The course will also give you the opportunity to practice and to share your own approaches to the problems that you will solve.

The course has been divided into 4 sections: (1) Machine Learning, (2) Convolutional Neural Networks, (3) Regularization, and (4) Optimization. Each section is introduced and completed using examples from a wide range of fields. The final section of the course provides an overview of the field and a review of what is to come.

The course is intended for advanced computer science majors and high-school students. It will be challenging at first but rewarding after you master the material and become familiar with some of the key concepts

Course Link: https://www.coursera.org/learn/art-science-ml