Python AI Development: Practice

Python    |    Intermediate
  • 14 videos | 1h 20m 27s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 47 users Rating 4.3 of 47 users (47)
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.

WHAT YOU WILL LEARN

  • 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

  • 2m 4s
  • 2m 37s
    During this video, you will learn how to configure the Python environment for developing AI. FREE ACCESS
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    3.  Understanding Anaconda Distribution
    4m 2s
    After completing this video, you will be able to specify the role of Anaconda in maintaining clear working environments. FREE ACCESS
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    4.  Jupyter Notebooks
    3m 10s
    After completing this video, you will be able to specify the advantages of Jupyter Notebooks and create Jupyter Notebook files connected to an appropriate kernel environment. FREE ACCESS
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    5.  Google Colab
    2m 39s
    After completing this video, you will be able to specify the advantages of the Google Colab environment and create files in the environment. FREE ACCESS
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    6.  Data Exploration
    9m 7s
    Learn how to use large datasets to explore data and statistics. FREE ACCESS
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    7.  Data Pre-processing
    14m 3s
    Learn how to apply pre-processing techniques commonly used in AI development. FREE ACCESS
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    8.  Basic ML Models
    4m 9s
    After completing this video, you will be able to describe the steps needed to create machine learning models and identify guidelines for their use. FREE ACCESS
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    9.  Basic DL Models
    3m 46s
    Upon completion of this video, you will be able to describe the steps needed to create deep learning models and identify guidelines for using them. FREE ACCESS
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    10.  Classifying Hand Written Digits
    10m 37s
    Find out how to work with a handwritten digits dataset and implement a simple classification model in Python. FREE ACCESS
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    11.  Predicting House Prices
    8m 28s
    In this video, you will learn how to work with a housing prices dataset and implement a simple regression model in Python. FREE ACCESS
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    12.  Pilot Distraction Prediction
    7m 15s
    Learn how to create a pilot distraction predictor by applying deep learning techniques. FREE ACCESS
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    13.  Distracted Driver Detection
    7m 4s
    During this video, you will learn how to configure a distracted driver detection device in Python using the pilot distraction predictor. FREE ACCESS
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    14.  Course Summary
    1m 26s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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