Artificial Intelligence (AI) in Python: A H2O Approach
Artificial Intelligence (AI) in Python: A H2O Approach
Artificial Intelligence (AI) in Python: A H2O Approach
Artificial Intelligence (AI) in Python: A H2O Approach

Artificial Intelligence (AI) in Python: A H2O Approach

4 Hours
Deal Price$14.99
Suggested Price
$50.00
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Artificial Intelligence (AI) in Python: A H2O Approach
$14.99$50.0070% OFF
Artificial Intelligence (AI) In Python: A H2O Approach
Artificial Intelligence (AI) In Python: A H2O Approach
Artificial Intelligence (AI) In Python: A H2O Approach
Artificial Intelligence (AI) In Python: A H2O Approach

42 Lessons (4h)

  • Welcome To The World of Python AI
    What is this course about?3:29
    Data and code
    What is AI?9:51
    Introduction to the Python Data Science Environment10:57
    Upgraded Python3 Installation5:44
    The IPython Ecosystem19:13
  • Read in and Preprocess Data From External Data Sources
    Introduction to Pandas12:06
    Read CSV5:42
    Read Excel5:31
    Read HTML5:58
    Introduction to Pandas For Basic EDA4:30
  • Introduction to H2O
    What is H2O?
    More H2O Installation2:11
    Getting Used To the H2O Framework2:12
    Read in Data as H2O Frame4:23
    Convert To H2O Frame2:43
  • What is Machine Learning (ML)?
    Theory Behind ML5:32
  • Supervised Learning With H2O
    What Is Supervised Classification?10:10
    Supervised Classification Accuracy4:19
    Theory of GLM5:25
    Set up GLMs11:35
    Test GLM Performance9:48
    Select Optimum GLM Parameters: Grid search10:03
    Random Forest For Binary Classification17:50
    Implement a Random Forest Model7:49
    Gradient Boosting Machine (GBM) For Regression11:11
    Search or GBM Parameters7:02
    XGB Theory2:02
    XGBoost for Binary Classification5:15
    XGBoost For Multiclass Classification5:12
    Search For the Best H2O Model:Select the Best Machine Learning Model5:20
  • Unsupervised Learning
    What Is Unsupervised Classification?1:38
    Principal Component Analysis (PCA) Theory2:37
    PCA6:14
    k-means theory1:57
    k-means11:08
  • Neural Networks With H2O
    Theoretical Introduction
    What are Activation Functions?5:50
    Implement Deep Learning for Binary Classification8:01
    Theory Behind Autoencoders1:46
    Set up Autoencoder4:06
    Implement the Autoencoder3:11
Artificial Intelligence (AI) in Python: A H2O Approach
$14.99$50.0070% OFF
DescriptionInstructorImportant DetailsRelated Products

Master 4 Hours of Content on Powerful Python Package for Machine Learning, Deep Learning, & More

MS
Minerva SinghMinerva Singh | Bestselling Instructor & Data Scientist
4.3/5 Instructor Rating: ★ ★ ★ ★ ★ ★
 Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Description

This course covers the main aspects of the H2O package for data science in Python. If you take this course, you can do away with taking other courses or buying books on Python-based data science as you will have the keys to a mighty Python supported data science framework. In this age of big data, companies worldwide use Python to sift through the avalanche of information at their disposal. By becoming proficient in machine learning, neural networks, and deep learning via a powerful framework, H2O in Python, you can give your company a competitive edge and boost your career to the next level!

4.7/5 average rating: ★ ★ ★ ★ ★ ★

  • Access 42 lectures & 4 hours of content 24/7
  • Use the Python/Anaconda environment for practical data science
  • Learn the important concepts associated with supervised & unsupervised learning
  • Implement supervised & unsupervised learning on real-life data
  • Implement Artificial Neural Networks (ANN) & Deep
  • Neural Networks (DNN) on real-life data

Specs

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Certificate of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Prior exposure to common machine learning terms
  • Prior exposure to what neural networks are

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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