SkillSoft Explore Course – Lambers, Inc.

Aspire AI Apprentice to AI Architect AI Architect Track 1: AI Apprentice

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.

Asset ID	it_aiappddj_02_enus
Course Type	Video Course
Course Category	AI Apprentice to AI Architect

Objectives
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

Objectives

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