English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 52m | 502 MB
Hands-on Python recipes that implement practical examples to help you build artificial intelligence applications
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.
Built for rookie AI enthusiasts across eight realistic projects, this course covers modern techniques that make up the world of Artificial Intelligence.
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
Table of Contents
01 The Course Overview
02 Classification Overview and Evaluation Techniques
03 Decision Trees
04 Prediction with Decision Trees and Student Performance Data
05 Random Forests
06 Predicting Bird Species with Random Forests
07 The Problem of Text Classification
08 Detecting YouTube Comment Spam with Bag of Words and Random Forests
09 Word2Vec Models
10 Detecting Positive_Negative Sentiment in User Reviews
11 Neural Networks
12 Identifying the Genre of a Song Using Audio Analysis and Neural Networks
13 Revising the Spam Detector to Use Neural Networks
14 Overview of Deep Learning and Convolutional Neural Networks
15 Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
16 Revising the Bird Species Identifier to Use Images
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