In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. You will also be able to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.
ofrecido por
H2O
Acerca de este Curso
12,181 vistas recientes
Los estudiantes que toman este Course son
- Data Scientists
- Machine Learning Engineers
- Data Analysts
- Data Engineers
- IT Support
Programa - Qué aprenderás en este curso
Semana
12 horas para completar
H2O AND THE FUNDAMENTALS
11 videos (Total 64 minutos), 4 lecturas, 5 cuestionarios
11 videos
Welcome!2m
What's In Week One?2m
Need To Know5m
Preinstall #1 (with Linux)7m
Preinstall #2 (with Windows)4m
Installing H2O4m
A Quick Deep Learning!19m
AutoML6m
Types Of Models7m
Where To Go With Questions2m
Summary1m
4 lecturas
Further Reading: Course Prerequisites10m
Pre-Install Summary10m
Additional Install Information10m
Further Reading: Getting Help10m
5 ejercicios de práctica
Do You Have What It Takes?10m
Quick Preinstall Check6m
Quick Install Check4m
Model types4m
Week One Exam14m
Semana
24 horas para completar
Trees And Overfitting
15 videos (Total 57 minutos), 1 lectura, 4 cuestionarios
15 videos
Decision Trees3m
Random Forest2m
Random Forest in H2O (Iris)4m
GBM2m
GBM in H2O (Iris)3m
Importing From Client4m
Artificial Data Sets6m
Overfitting and Train/Valid/Test4m
Train/Valid/Test in H2O3m
GBM in H2O (artificial data)4m
Let's Overfit A GBM!4m
Cross-validation in H2O (GBM)7m
About the peer review task3m
Week Two Summary44s
1 lectura
Further Reading: Tree Algorithms10m
3 ejercicios de práctica
Decision Trees4m
Tree Algorithms8m
On cross-validation and over-fitting18m
Semana
32 horas para completar
LINEAR MODELS AND MORE
9 videos (Total 56 minutos), 4 lecturas, 3 cuestionarios
9 videos
Loading From Remote Sources6m
Exporting Data From H2O2m
Exploring With GLMs11m
Naive Bayes4m
Data Manipulation, Statistics11m
Grid Search8m
Applying Grids8m
Summary50s
4 lecturas
More on loading and saving10m
Further Reading: GLMs, Naive Bayes10m
Further Reading: Data Manipulation10m
Further Reading: Grid Search10m
3 ejercicios de práctica
Load/Save4m
GLMs6m
Week Three Exam16m
Semana
44 horas para completar
Deep Learning
11 videos (Total 66 minutos), 2 lecturas, 6 cuestionarios
11 videos
Load & Save Models1m
Binding data tables6m
Merging and joins3m
Neural Networks6m
Deep Learning Part 19m
Deep Learning Part 210m
Deep Learning with Grids11m
Regression with Deep Learning7m
Introducing The Graded Task3m
Summary Of Week Four1m
2 lecturas
More Neural Net Theory10m
Extension Project Ideas10m
5 ejercicios de práctica
Early Stopping6m
Binding4m
Merging4m
Deep Learning Basics6m
More Deep Learning4m
4.5
9 revisionesPrincipales revisiones sobre Practical Machine Learning on H2O
I've taken a lot of Coursera classes and this is one of the better classes. It is a good hands-on course and will help students learn more about not only H2O, but also machine learning.
Great content, a lot of hands-on activities and the instructor is quite good too. By the end of the course, I feel that I have the necessary skills to work with h2o.
Acerca de H2O
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
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