This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
このコースはPython Data Products for Predictive Analytics の専門講座に属します。
Design Thinking and Predictive Analytics for Data Products
提供：
シラバス - 本コースの学習内容
週
12時間で修了
Week 1: Supervised Learning & Regression
Welcome to the second course in this specialization! This week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of supervised learning and regression.
5件のビデオ （合計46分）, 4 readings, 3 quizzes
5件のビデオ
Time-Series Regression8 分
Autoregression6 分
4件の学習用教材
Syllabus10 分
Course Materials10 分
Set Up Your System10 分
Recap: Mathematical Notation10 分
3の練習問題
Review: Supervised Learning4 分
Review: Regression4 分
Supervised Learning & Regression10 分
週
21時間で修了
Week 2: Features
This week, we will learn what features are in a dataset and how we can work with them through cleaning, manipulation, and analysis in Jupyter notebooks.
4件のビデオ （合計29分）, 3 quizzes
4件のビデオ
Missing Values7 分
3の練習問題
Review: Getting Features
Review: Working with Features
Features10 分
週
31時間で修了
Week 3: Classification
This week, we will learn about classification and several ways you can implement it, such as K-nearest neighbors, logistic regression, and support vector machines.
4件のビデオ （合計31分）, 3 quizzes
4件のビデオ
Introduction to Support Vector Machines10 分
3の練習問題
Review: Classification and K-Nearest Neighbors6 分
Review: Logistic Regression and Support Vector Machines5 分
Classification10 分
週
41時間で修了
Week 4: Gradient Descent
This week, we will learn the importance of properly training and testing a model. We will also implement gradient descent in both Python and TensorFlow.
5件のビデオ （合計36分）, 3 quizzes
5件のビデオ
Gradient Descent in TensorFlow6 分
Livecoding: Tensorflow7 分
3の練習問題
Review: Classification and Training4 分
Review: Gradient Descent4 分
More on Classification15 分
講師
Julian McAuley
Assistant ProfessorComputer Science
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
カリフォルニア大学サンディエゴ校について
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Python Data Products for Predictive Analyticsの専門講座について
Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego.
This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills.
Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets.
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