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Learn all about Artificial intelligence using Python
Learn all about Artificial intelligence using Python
Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans. These machines can be trained to perform tasks such as recognizing patterns, learning from data, and making decisions.
The following topics are covered. The Book has lessons, example code and quiz.
[1] Introduction to AI and machine
Define artificial intelligence and machine learning
Explain the difference between AI and machine learning
Discuss the various applications of AI and machine learning
Introduce Python as a programming language for AI and machine learning
[2] Setting up a Python development
Install Python and necessary libraries and tools
Set up an integrated development environment (IDE)
Familiarization with Python syntax and data types
[3] Exploring and preprocessing
Load and explore data sets using Python libraries such as Pandas
Clean and preprocess data using techniques such as missing value imputation and normalization
[4] Building and evaluating machine learning
Supervised learning algorithms such as linear regression and support vector machines
Unsupervised learning algorithms such as k-means clustering and principal component analysis
How to evaluate the performance of machine learning models using metrics such as accuracy and mean squared error
[5] Deep learning with neural
Neural networks and deep learning
Build and train neural networks using Python libraries such as TensorFlow and Keras
Techniques for improving the performance of neural networks, such as regularization and model ensembles
[6] Natural language
Introduction to natural language processing (NLP)
Use Python libraries such as NLTK and spaCy to perform tasks such as tokenization, stemming, and part-of-speech tagging
Build and evaluate NLP models for tasks such as language translation and sentiment analysis
Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans. These machines can be trained to perform tasks such as recognizing patterns, learning from data, and making decisions.
The following topics are covered. The Book has lessons, example code and quiz.
[1] Introduction to AI and machine
Define artificial intelligence and machine learning
Explain the difference between AI and machine learning
Discuss the various applications of AI and machine learning
Introduce Python as a programming language for AI and machine learning
[2] Setting up a Python development
Install Python and necessary libraries and tools
Set up an integrated development environment (IDE)
Familiarization with Python syntax and data types
[3] Exploring and preprocessing
Load and explore data sets using Python libraries such as Pandas
Clean and preprocess data using techniques such as missing value imputation and normalization
[4] Building and evaluating machine learning
Supervised learning algorithms such as linear regression and support vector machines
Unsupervised learning algorithms such as k-means clustering and principal component analysis
How to evaluate the performance of machine learning models using metrics such as accuracy and mean squared error
[5] Deep learning with neural
Neural networks and deep learning
Build and train neural networks using Python libraries such as TensorFlow and Keras
Techniques for improving the performance of neural networks, such as regularization and model ensembles
[6] Natural language
Introduction to natural language processing (NLP)
Use Python libraries such as NLTK and spaCy to perform tasks such as tokenization, stemming, and part-of-speech tagging
Build and evaluate NLP models for tasks such as language translation and sentiment analysis
139 pages, Kindle Edition
Published February 15, 2023
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