1.1 machine learning basics-python deep machine learning, 1.1-python

1.1 machine learning basics-python deep machine learning, 1.1-python

Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang

Video tutorial: http://pan.baidu.com/s/1kVNe5EJ

 

 

1. course Introduction

2. Machine Learning (ML)

2.1 concept: involves multiple disciplines, including probability theory, statistics, closeness theory, Convex analysis, and algorithm complexity theory. It is dedicated to studying how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures so that they can continuously improve their own performance.

2.2 subject positioning: the Core of Artificial Intelligence, AI, is the fundamental way to make computers intelligent. Its application covers all fields of Artificial Intelligence, it mainly uses induction, synthesis, rather than deduction.

2.3 definition: Explore and develop a series of algorithms to enable computers to learn and model themselves through data without explicit external indications, and use the created model and new input to make predictions.

Arthur Samuel (1959): a discipline that enables computers to learn independently without being instructed by external programs

Langley (1996): "machine learning is an artificial intelligence science. The main research object in this field is artificial intelligence, especially how to improve the performance of specific algorithms in Experience learning"

Tom Michell (1997): "machine learning is a study of computer algorithms that can be automatically improved through experience"

2.4: Learning: For experience E (experience) and a series of task T (tasks) and performance measurement P, if the accumulation of experience E follows, for a defined task T that can improve performance P, it means that the computer has the learning ability.

Example: Chess, speech recognition, self-driving cars, etc.

3. Application of machine learning:

Speech Recognition

Automatic driving

Language Translation

Computer Vision

Recommendation System

Drones

Identify Spam

4. Demo:

Face Recognition

Self-driving car

E-commerce Recommendation System

5. Real Estate Market Demand: LinkedIn all vocational skills demand first: machine learning, data mining and statistical analysis Talents