Resumo do curso

This course will teach you how to start from scratch in answering questions about the real world using data. Machine learning happens to be a small part of this process. The model building process involves setting up ways of collecting data, understanding and paying attention to what is important in the data to answer the questions you are asking, finding a statistical, mathematical or a simulation model to gain understanding and make predictions.

All of these things are equally important and model building is a crucial skill to acquire in every field of science. The process stays true to the scientific method, making what you learn through your models useful for gaining an understanding of whatever you are investigating as well as make predictions that hold true to test.

We will take you on a journey through building various models. This process involves asking questions, gathering and manipulating data, building models, and ultimately testing and evaluating them.

Valor do curso
Grátis
Tempo estimado Tempo total entre hoje e dia da formatura depende do seu compromisso semanal. Em média, os nossos graduados completam este nanodegree em 2 meses
2 meses
Nível
avançado
O curso inclui

Videoaulas

Testes interativos

Aulas com profissionais do setor

Ritmo individual de aprendizado

Comunidade de apoio aos alunos

Sua jornada de aprendizagem

Este curso aberto é seu primeiro passo em direção a uma nova carreira com o programa Engenheiro de Machine Learning

Curso Aberto

Model Building and Validation

por AT&T

Enhance your skill set and boost your hirability through innovative, independent learning.

Icon steps 54aa753742d05d598baf005f2bb1b5bb6339a7d544b84089a1eee6acd5a8543d
 
 

Don Dini
Don Dini

Instrutor

Rishi Pravahan
Rishi Pravahan

Instrutor

Pré-requisitos

This is an advanced course, and the ideal students for this class are prepared individuals who have:

  1. Python programming knowledge, familiarity with python tools like Ipython Notebook and data analysis libraries like Scikit-learn, Scipy, and Pandas
  2. Knowledge of descriptive, inferential, and predictive statistics
  3. Knowledge of calculus, especially derivatives and integrals
  4. Knowledge of basic matrix algebra - matrices, vectors, determinant, identity matrix, multiplication, inverse
  5. Taken Intro to Machine learning and have understanding of common supervised learning and unsupervised learning algorithms, such as SVM and k-means clustering

Por que fazer este curso?

Many of you may have already taken a course in machine learning or data science or are familiar with machine learning models.

In this course we will take a more general approach, walking through the questioning, modeling and validation steps of the model building process.

The goal is to get you to practice thinking in depth about a problem and coming up with your own solutions. Many examples we will attempt may not have one correct answer but will require you to work through the problems applying the methods we hope to illustrate throughout this class.

Quais são os recursos?
Vídeos dos instrutores Exercícios práticos Aulas com profissionais do setor