Python Artificial Intelligence Projects for Beginners

Watch Python Artificial Intelligence Projects for Beginners

  • 2017
  • 1 Season

Python Artificial Intelligence Projects for Beginners from Packt Publishing is a comprehensive online training program that teaches learners how to build AI projects using Python. The course is designed for beginners who are interested in learning the basics of AI and how to code it in Python. The course covers various AI techniques and algorithms such as machine learning, natural language processing, and computer vision. The aim of the course is to provide learners with hands-on experience in building AI projects from scratch.

The course is taught by expert instructors who have years of experience in AI and Python. The instructors start by explaining the basics of Python programming language including variables, data types, operators, loops, and functions. After mastering Python programming, learners move on to learning AI techniques such as machine learning, natural language processing, and computer vision.

The course covers several machine learning concepts such as supervised learning, unsupervised learning, reinforcement learning, and deep learning. Learners get to work with popular machine learning libraries such as scikit-learn, TensorFlow, and Keras. The course demonstrates how to train machine learning models using real-world datasets.

The course also covers natural language processing (NLP) techniques such as sentiment analysis, text summarization, and speech recognition. Learners get to work with popular NLP libraries such as NLTK and spaCy. The course demonstrates how to build chatbots that understand and respond to natural language queries.

Computer vision is another critical aspect of AI covered in the course. Learners get to work with popular computer vision libraries such as OpenCV and scikit-image. The course demonstrates how to build image classification models that can classify images into different categories.

The course includes several hands-on projects that learners can work on. The projects are designed to provide practical experience in building AI projects from scratch. One of the projects in the course is building a sentiment analysis model. Learners get to train a machine learning model that can classify text as positive or negative. Another project is building a chatbot. Learners get to build a chatbot that can understand natural language queries and respond appropriately.

The course also includes several quizzes and exercises that learners can use to test their understanding of the concepts covered in the course. The quizzes and exercises are designed to reinforce the concepts taught in the course.

In conclusion, Python Artificial Intelligence Projects for Beginners from Packt Publishing is an excellent course for learners who are interested in learning AI and how to code it in Python. The course covers various AI techniques and algorithms such as machine learning, natural language processing, and computer vision. The course provides hands-on experience in building AI projects from scratch. The course is taught by expert instructors who have years of experience in AI and Python. The course is ideal for beginners who want to learn AI and Python programming from scratch.

Python Artificial Intelligence Projects for Beginners is a series that is currently running and has 1 seasons (16 episodes). The series first aired on December 26, 2017.

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Seasons
Revising the Bird Species Identifier to Use Images
16. Revising the Bird Species Identifier to Use Images
December 26, 2017
In this video, we will see how to use more sophisticated CNNs to classify images.
Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
15. Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
December 26, 2017
In this video, we will see how to use CNNs to classify images.
Overview of Deep Learning and Convolutional Neural Networks
14. Overview of Deep Learning and Convolutional Neural Networks
December 26, 2017
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.
Revising the Spam Detector to Use Neural Networks
13. Revising the Spam Detector to Use Neural Networks
December 26, 2017
In this video, we will see how to use neural networks for text data.
Identifying the Genre of a Song Using Audio Analysis and Neural Networks
12. Identifying the Genre of a Song Using Audio Analysis and Neural Networks
December 26, 2017
In this video, how we can identify the genre of a song.
Neural Networks
11. Neural Networks
December 26, 2017
In this video, we will see what neural networks are, why are they named this way, and how do they work.
Detecting Positive/Negative Sentiment in User Reviews
10. Detecting Positive/Negative Sentiment in User Reviews
December 26, 2017
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.
Word2Vec Models
9. Word2Vec Models
December 26, 2017
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.
Detecting YouTube Comment Spam with Bag of Words and Random Forests
8. Detecting YouTube Comment Spam with Bag of Words and Random Forests
December 26, 2017
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.
The Problem of Text Classification
7. The Problem of Text Classification
December 26, 2017
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.
Predicting Bird Species with Random Forests
6. Predicting Bird Species with Random Forests
December 26, 2017
In this video, we will see how we can predict the species of a bird based on various attributes.
Random Forests
5. Random Forests
December 26, 2017
In this video, we will see what a random forest is, how it works, and what is it good for.
Prediction with Decision Trees and Student Performance Data
4. Prediction with Decision Trees and Student Performance Data
December 26, 2017
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.
Decision Trees
3. Decision Trees
December 26, 2017
In this video, we will see what a decision tree is, how it works, and what is it good for.
Classification Overview and Evaluation Techniques
2. Classification Overview and Evaluation Techniques
December 26, 2017
In this video, we will see what classification is and also, we will see how well a system is performing in this task.
The Course Overview
1. The Course Overview
December 26, 2017
This video gives an overview of the entire course.
Description
Where to Watch Python Artificial Intelligence Projects for Beginners
Python Artificial Intelligence Projects for Beginners is available for streaming on the Packt Publishing website, both individual episodes and full seasons. You can also watch Python Artificial Intelligence Projects for Beginners on demand at Amazon.
  • Premiere Date
    December 26, 2017