Advanced Linear Models for Data Science 1: Least Squares

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Advanced Linear Models for Data Science 1: Least Squares
This course introduces the basic techniques for constructing optimal solutions to linear models and introduces the linearization of data by introducing the linearization standard. We will learn about the implementation details and the main questions that arise from the analysis of data sets that are too large to fit directly on their own. These problems can be solved using the various optimization algorithms available and get their solutions run on real datasets.

To get the most out of this course, learners should have:
• Completed Linear Models for Data Science 1: Foundations or have equivalent experience
• Basic knowledge of Python

Get ready to work with data sets that are too big to fit directly on their own. These problems can be solved using the various optimization algorithms.

The course is structured in four modules, each one revolving around a different aspect of data science:

Weekly Problem Solving
Weekly Problem Solving Using R (or similar language)
Weekly Problem Solving Using Python
Weekly Problem Solving Using Excel
Advanced Machine Learning
This course aims to provide an introduction to advanced machine learning methods. The techniques covered include: (i) supervised learning, (ii) unsupervised learning, (iii) regularization and (iv) configuration optimization. We will learn how to use machine learning frameworks and frameworks for a wide variety of problems. We will also include a high level introduction to the process of using the framework.

A learner with little or no math background is welcome to take this course. Those with some math background but little knowledge of machine learning will be able to understand the concepts introduced in this course. The course is ideal for those that work in industry, for those that want to advance their skill in machine learning, and for those that just want to learn about some of the most important topics in machine learning.

This course can also be considered as an advanced beginner’s course. Machine learning is still in a very early stage where some concepts are being introduced. This course focuses on the most important topics in machine learning:
1) Supervised learning, 2) Unsupervised learning, 3) Regularization, and 4) Configuration optimization.Machine Learning Principles
Supervised Learning
Unsupervised Learning
Regularization and Configuration Optimization
Advanced Materials II: Ceramics
Ceramics are used in all areas of your career, starting as early as your first day on the job. They’re the materials that make your job as a craftsman or engineer or handyman or salesperson or clerk in a company or warehouseman or kitchen mitt technician or laborer or electrician or electrician and boiler or electrician and transmission or water and sewer or water utility. You need to be able to make ceramics, so this course will learn how

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