Python Artificial Intelligence Projects for Beginners

Python Artificial Intelligence Projects for Beginners

Sesong 1
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 programming because of its flexibility. Built for rookie AI enthusiasts across eight realistic projects, this course covers modern techniques that make up the world of AI.
201716 episoderALLE
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Episoder

  1. S1 E1 - The Course Overview

    26. desember 2017
    2min
    ALLE
    This video gives an overview of the entire course.
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  2. S1 E2 - Classification Overview and Evaluation Techniques

    26. desember 2017
    5min
    ALLE
    In this video, we will see what classification is and also, we will see how well a system is performing in this task.
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  3. S1 E3 - Decision Trees

    26. desember 2017
    4min
    ALLE
    In this video, we will see what a decision tree is, how it works, and what is it good for.
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  4. S1 E4 - Prediction with Decision Trees and Student Performance Data

    26. desember 2017
    7min
    ALLE
    In this video, we will see how we can predict a student's performance in a course. We can do so by looking at the student's prior performance and some facts about their home life. A decision tree classifier can be used to learn how to predict performance based on these historical attributes.
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  5. S1 E5 - Random Forests

    26. desember 2017
    2min
    ALLE
    In this video, we will see what a random forest is, how it works, and what is it good for.
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  6. S1 E6 - Predicting Bird Species with Random Forests

    26. desember 2017
    9min
    ALLE
    In this video, we will see how we can predict the species of a bird based on various attributes.
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  7. S1 E7 - The Problem of Text Classification

    26. desember 2017
    6min
    ALLE
    In this video, we will see how we can manipulate text in order to use a classification technique, such as random forests. One popular approach is the bag-of-words model.
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  8. S1 E8 - Detecting YouTube Comment Spam with Bag of Words and Random Forests

    26. desember 2017
    9min
    ALLE
    In this video, we will see how we can identify spam comments on a website. We look at a YouTube spam dataset to practice with bag-of-words and random forests to solve this problem.
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  9. S1 E9 - Word2Vec Models

    26. desember 2017
    5min
    ALLE
    Is there any other way to represent text, perhaps more accurately? Indeed, Word2Vec is a more sophisticated approach that can yield more accurate results and this video explains that.
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  10. S1 E10 - Detecting Positive/Negative Sentiment in User Reviews

    26. desember 2017
    8min
    ALLE
    In this video, we will see how we can identify positive and negative product reviews and how to use word2vec and doc2vec models to find which documents are similar to each other.
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  11. S1 E11 - Neural Networks

    26. desember 2017
    4min
    ALLE
    In this video, we will see what neural networks are, why are they named this way, and how do they work.
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  12. S1 E12 - Identifying the Genre of a Song Using Audio Analysis and Neural Networks

    26. desember 2017
    11min
    ALLE
    In this video, how we can identify the genre of a song.
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  13. S1 E13 - Revising the Spam Detector to Use Neural Networks

    26. desember 2017
    9min
    ALLE
    In this video, we will see how to use neural networks for text data.
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  14. S1 E14 - Overview of Deep Learning and Convolutional Neural Networks

    26. desember 2017
    6min
    ALLE
    In this video, we will see what deep learning is, why is it a popular term today, how does it work, and what are popular techniques in deep learning.
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  15. S1 E15 - Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks

    26. desember 2017
    15min
    ALLE
    In this video, we will see how to use CNNs to classify images.
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  16. S1 E16 - Revising the Bird Species Identifier to Use Images

    26. desember 2017
    12min
    ALLE
    In this video, we will see how to use more sophisticated CNNs to classify images.
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