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.
Este curso faz parte do Programa de cursos integrados Python Data Products for Predictive Analytics
Design Thinking and Predictive Analytics for Data Products
oferecido por
Programa de cursos integrados Python Data Products for Predictive AnalyticsUniversidade da Califórnia, San Diego
Programa - O que você aprenderá com este curso
Semana
12 horas para concluir
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 vídeos (Total 46 mín.), 4 leituras, 3 testes
5 videos
Supervised Learning: Regression9min
Regression in Python10min
Time-Series Regression8min
Autoregression6min
4 leituras
Syllabus10min
Course Materials10min
Set Up Your System10min
Recap: Mathematical Notation10min
3 exercícios práticos
Review: Supervised Learning4min
Review: Regression4min
Supervised Learning & Regression10min
Semana
21 hora para concluir
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 vídeos (Total 29 mín.), 1 leitura, 3 testes
4 videos
Features from Temporal Data8min
Feature Transformations4min
Missing Values7min
1 leituras
Supplementary Notebook for Features3min
3 exercícios práticos
Review: Getting Features
Review: Working with Features
Features10min
Semana
31 hora para concluir
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 vídeos (Total 31 mín.), 3 testes
4 videos
Classification: Nearest Neighbors4min
Classification: Logistic Regression10min
Introduction to Support Vector Machines10min
3 exercícios práticos
Review: Classification and K-Nearest Neighbors6min
Review: Logistic Regression and Support Vector Machines5min
Classification10min
Semana
41 hora para concluir
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 vídeos (Total 36 mín.), 3 testes
5 videos
Introduction to Training and Testing6min
Gradient Descent in Python8min
Gradient Descent in TensorFlow6min
Livecoding: Tensorflow7min
3 exercícios práticos
Review: Classification and Training4min
Review: Gradient Descent4min
More on Classification15min
Instrutores
Julian McAuley
Assistant ProfessorComputer Science
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
Sobre Universidade da Califórnia, San Diego
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.
Sobre Programa de cursos integrados Python Data Products for Predictive Analytics
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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