About this course

What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs.
These skills also go under the names “machine learning” and “data science,” the latter being a broader term than machine learning or predictive analytics but narrower than AI. This course is part of the Machine Learning Operations (MLOps) Program. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions. As you get further into the program, you will learn how to fit that model into a machine learning pipeline.
You will get hands-on experience with the top techniques in supervised learning: linear and logistic regression modeling, decision trees, neural networks, ensembles, and much more.
But most importantly, by the end of this course, you will know
What a predictive model can (and cannot) do, and how its data is structured
How to predict a numerical output, or a class (category)
How to measure the out-of-sample (future)performance of a model

At a Glance:
Institution: Statistics.comX
Subject: Data Analysis & Statistics
Level: Intermediate
Prerequisites:
Python
Statistics
We will present Python code to illustrate how to fit models, so we assume some familiarity with Python. Some exposure to basic statistics is also helpful, more from a comfort perspective than from a need to dive deep into statistical routines.
Language: English
Video Transcript: English
Associated programs:
Professional Certificate in Machine Learning Operations with Microsoft Azure (MLOps with Azure)
Professional Certificate in Machine Learning Operations with Amazon Web Services (MLOps with AWS)
Professional Certificate in Machine Learning Operations with Google Cloud Platform (MLOps with GCP)
Associated skills:Data Science, Logistic Regression, Operations, Predictive Analytics, Forecasting, Supervised Learning, Artificial Intelligence, Machine Learning, Decision Tree Learning, Artificial Neural Networks