Python Artificial Intelligence Projects for Beginners [Video]

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Python Artificial Intelligence Projects for Beginners [Video]

Joshua Eckroth

Hands-on Python recipes that implement practical examples to help you build artificial intelligence applications

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Video Details

ISBN 139781788394345
Course Length1 hours 52 minutes

Video Description

Artificial Intelligence (AI)is one of the hottest fields in computer science right now and has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, as well as its great support for open source libraries such as Scikit-learn and Keras.
Built for rookie AI enthusiasts across eight realistic projects, this course covers modern techniques that make up the world of AI. You’ll start with your first project that covers decision trees for classifying data using Scikit-learn libraries. Next, you will build a classifier using random forests. Then you will learn about text processing techniques and practice with bag-of-words and word2vec models. Further, you will be introduced to deep learning and neural networks and practice with projects that make use of Keras and convolutional neural networks.

By the end of this video course, you will be confident to build your own AI projects with Python and be ready to take on more advanced content as you go ahead.

Style and Approach

Built for rookie AI enthusiasts across eight realistic projects, this course covers modern techniques that make up the world of Artificial Intelligence.

Table of Contents

Building Your Own Prediction Models
The Course Overview
Classification Overview and Evaluation Techniques
Decision Trees
Prediction with Decision Trees and Student Performance Data
Random Forests
Predicting Bird Species with Random Forests
Applications for Comment Classification
The Problem of Text Classification
Detecting YouTube Comment Spam with Bag of Words and Random Forests
Word2Vec Models
Detecting Positive/Negative Sentiment in User Reviews
Deep Learning
Neural Networks
Identifying the Genre of a Song Using Audio Analysis and Neural Networks
Revising the Spam Detector to Use Neural Networks
Overview of Deep Learning and Convolutional Neural Networks
Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
Revising the Bird Species Identifier to Use Images

What You Will Learn

  • Classify text and images according to predefined categories
  • Make use of neural networks, decision trees, random forests for classification
  • Apply deep learning on text and images
  • Extend pre-trained deep learning models

Authors

Table of Contents

Building Your Own Prediction Models
The Course Overview
Classification Overview and Evaluation Techniques
Decision Trees
Prediction with Decision Trees and Student Performance Data
Random Forests
Predicting Bird Species with Random Forests
Applications for Comment Classification
The Problem of Text Classification
Detecting YouTube Comment Spam with Bag of Words and Random Forests
Word2Vec Models
Detecting Positive/Negative Sentiment in User Reviews
Deep Learning
Neural Networks
Identifying the Genre of a Song Using Audio Analysis and Neural Networks
Revising the Spam Detector to Use Neural Networks
Overview of Deep Learning and Convolutional Neural Networks
Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
Revising the Bird Species Identifier to Use Images

Video Details

ISBN 139781788394345
Course Length1 hours 52 minutes
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