In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business.
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课程信息
ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
您将学到的内容有
Train and evaluate linear regression models.
Train binary and multi-class classification models.
Evaluate and tune classification models to improve their performance.
Train and evaluate clustering models to find useful patterns in unsupervised data.
您将获得的技能
- Machine Learning
- clustering
- classification
- Linear Regression
- Machine Learning (ML) Algorithms
ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
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CertNexus
CertNexus is a vendor-neutral certification body, providing emerging technology certifications and micro-credentials for Business, Data, Development, IT, and Security professionals. CertNexus’ exams meet the most rigorous development standards possible which outlines a global framework for developing personnel certification programs to narrow the widening skills gap.
授课大纲 - 您将从这门课程中学到什么
Build Linear Regression Models Using Linear Algebra
In the preceding course, you went through the overall machine learning workflow from start to finish. Now it's time to start digging into the algorithms that make up machine learning. This will help you select the most appropriate algorithm(s) for your own purposes, as well as how best to apply them to solve a problem. A good place to start is with simple linear regression.
Build Regularized and Iterative Linear Regression Models
The simple model you created earlier works well in many cases, but that doesn't mean it's the optimal approach. Linear regression can be enhanced by the process of regularization, which will often improve the skill of your machine learning model. In addition, an iterative approach to regression can take over where the closed-form solution falls short. In this module, you'll apply both techniques.
Train Classification Models
Besides linear regression, the other major type of supervised machine learning outcome is classification. To begin with, you'll train some binary classification models using a few different algorithms. Then, you'll train a model to handle cases in which there are multiple ways to classify a data example. Each algorithm may be ideal for solving a certain type of classification problem, so you need to be aware of how they differ.
Evaluate and Tune Classification Models
It's not enough to just train a model you think is best, and then call it a day. Unless you're using a very simple dataset or you get lucky, the default parameters aren't going to give you the best possible model for solving the problem. So, in this module, you'll evaluate your classification models to see how they're performing, then you'll attempt to improve their skill.
关于 经过 CertNexus 认证的人工智能从业人员 专业证书
The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demnstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP.

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