Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of A. I. This course starts out defining several areas related to Machine Learning and what machine learning covers. The second part of this course describes the Python libraries that are used in analyzing data. And the third part of this course goes through a hands-on exercise to introduce students to getting simple information output. This output uses the Python libraries to create charts and analyze data from a CSV file. Prerequisites to this course are the Introduction to Artificial Intelligence course and the Basic Python Programming course.
Machine Learning Requirements
Definition of Machine Learning
History of Machine Learning
Different Machine Learning Applications
Definition of Machine Learning
Machine Learning Problems
Driving Capabilities
Main Process
Definition of Data Mungering
Machine Learning Cleaning & Improvements
Data Preparation Steps
Relation to Data Mining
Machine Learning & Problem Solving
Capabilities
Processes
Scientific Analysis
Example in Business
Bias in Models
Business Analysis
Algorithm In Math Form Example
Definition of Algorithms
Artificial Neural Networks
Linear Regression Theory & Linear Chart
Linear Regression Results
Python Interpreter and its Compiler
Numpy – is used for its N-dimensional array objects
Pandas – is a data analysis library that includes data frames
Matplotlib – is 2D plotting library for creating graphs and plots
Scikit-learn – the algorithms used for data analysis and data mining tasks
Seaborn – a data visualization library based on Matplotlib
Creating Data Reports and Histograms
Finding out more about the Code
Exercise for Charting
Histograms & Chart