Python AI Development: Introduction

Python AI Development: Introduction

• discover the key concepts covered in this course
• describe the Python programming language and recognize its role in AI development
• differentiate between interpreted and compiled programming languages
• recognize major AI tools used in the industry
• identify the advantages of using Python when developing AI
• describe factors that make Python one of the most popular programming languages
• compare the performance and functionality of Python AI toolbox to R AI toolbox
• compare the performance and functionality of Python AI toolbox to C++ AI toolbox
• compare the performance and functionality of Python AI toolbox to Java AI toolbox
• compare the performance and functionality of Python AI toolbox to new languages
• describe the role Python AI development plays in the industry
• describe and distinguish between multiple Python AI libraries
• troubleshoot Python environment setup and package installation
• list multiple ways of utilizing Python inside a software system
• describe the prospects of using Python for AI in the future
• summarize the key concepts covered in this course

Python is one of the most popular programming languages and programming AI in this language has many advantages. In this course, you'll learn about the differences between Python and other programming languages used for AI, Python's role in the industry, and cases where using Python can be beneficial. You'll also examine multiple Python tools, libraries, and use environments and recognize the direction in which this language is developing.

Python AI Development: Practice

Python AI Development: Practice

• discover the key concepts covered in this course
• configure the Python environment for developing AI
• specify the role of Anaconda in keeping clear working environments
• specify the advantages of Jupyter Notebooks and create Jupyter Notebook files connected to appropriate kernel environment
• specify the advantages of the Google Colab environment and create files in the environment
• use large datasets for exploring data and statistics
• apply pre-processing techniques commonly used in AI development
• describe the steps needed to create machine learning models and identify guidelines for using them
• describe the steps needed to create deep learning models and identify guidelines for using them
• work with a hand written digits dataset and implement a simple classification model in Python
• work with a housing prices dataset and implement a simple regression model in Python
• create a pilot distraction predictor by applying deep learning techniques
• configure a distracted driver detection device in Python using the pilot distraction predictor
• summarize the key concepts covered in this course

In this course, you'll learn about development of AI with Python, starting with simple projects and ending with comprehensive systems. You'll examine various Python environments and ways to set them up and begin coding, leaving you with everything you need to begin building your own AI solutions in Python.