Users Online

· Guests Online: 92

· Members Online: 0

· Total Members: 188
· Newest Member: meenachowdary055

Forum Threads

Newest Threads
No Threads created
Hottest Threads
No Threads created

Latest Articles

· Do while loop
· The for loop in C
· C while loop
· Pointers in C
· Functions in C

Articles Hierarchy

Articles Home » Artificial Intelligence and Machine Learning » Become an AI and Machine Learning Specialist, Part I

Become an AI and Machine Learning Specialist, Part I

Machine Learning and AI Foundations: Value Estimations with Adam Geitgey

1h 4m • COURSE
Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics. In this project-based course, discover how to use machine learning to build a value estimation system that can deduce the value of a home. Follow Adam Geitgey as he walks through how to use sample data to build a machine learning model, and then use that model in your own programs. Although the project featured in this course focuses on real estate, you can use the same approach to solve any kind of value estimation problem with machine learning.
Topics include:
  • Setting up the development environment
  • Building a simple home value estimator
  • Finding the best weights automatically
  • Working with large data sets efficiently
  • Training a supervised machine learning model
  • Exploring a home value data set
  • Deciding how much data is needed
  • Preparing the features
  • Training the value estimator
  • Measuring accuracy with mean absolute error
  • Improving a system
  • Using the machine learning model to make predictions


SECTION 1 INTRODUCTION
01_01-Welcome
01_02-What you should know
01_03-Using the exercise files
01_04-Set up the development environment



SECTION 2 WHAT IS MACHINE LEARNING AND VALUE PREDICTION
02_01-What is machine learning
02_02-Supervised machine learning for value prediction
02_03-Build a simple home value estimator
02_03-Build a simple home value estimator
02_05-Cool uses of value prediction



SECTION 3 AN OVERVIEW OF BUILDING A MACHINE LEARNING SYSTEM
03_01-Introduction to NumPy, scikitlearn, and pandas
03_02-Think in vectors_ How to work with large data sets efficiently
03_03-The basic workflow for training a supervised machine learning model
03_04-Gradient boosting_ A versatile machine learning algorithm



SECTION 4 TRAINING DATA
04_01-Explore a home value data set
04_02-Standard conventions for naming training data
04_03-Decide how much data you need



SECTION 5 FEATURES
05_01-Feature engineering
05_02-Choose the best features for home value prediction
05_03-Use as few features as possible_ The curse of dimensionality



SECTION 6 CODING OUR SYSTEM
06_01-Prepare the features
06_02-Training vs. testing data
06_03-Train the value estimator
06_04-Measure accuracy with mean absolute error



SECTION 7 IMPROVING OUR SYSTEM
07_01-Overfitting and underfitting
07_02-The brute force solution_ Grid search
07_03-Feature selection



SECTION 8 USING THE ESTIMATOR IN A REAL-WORLD PROGRAM
08_01-Predict values for new data
08_01-Predict values for new data



SECTION 9 CONCLUSION
09_01-Wrapup



· Posted by Superadmin on January 21 2019 · 0 Comments · 1264 Reads · Print
Page 5 of 6: 123456

Comments

No Comments have been Posted.

Post Comment

Please Login to Post a Comment.

Ratings

Rating is available to Members only.

Please login or register to vote.
No Ratings have been Posted.
Render time: 0.74 seconds
10,833,172 unique visits