• Class and Course

    Intro to AI with Machine Learning, Deep Learning and More

    A five-day, lecture and hands-on lab course for people who have completed the Intro to Python course, people who have completed the Intro to Python for Non-Programmers: Parts 1 & 2 course sequence, or people with equivalent Python programming experience.

    Course Overview

    Intended for Python programmers and based on the innovative textbook Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud (https://deitel.com/IntroToPythonContentsDiagram) by Paul Deitel and Harvey Deitel, this course provides a code-intensive introduction to some of today’s most compelling, leading-edge data science, AI and big data computing technologies, with cool examples on natural language processing, data visualization, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning with a convolutional neural network, sentiment analysis through deep learning with a recurrent neural network, and big data with Spark™ streaming, NoSQL databases and the Internet of Things (IoT).

    Attendees leverage key, open-source, Python data-science libraries, Python AI libraries and infrastructure platforms to maximize productivity, quickly creating powerful applications with minimal code. The course offers extensive hands-on lab coding practice. Deitel LabAssist notes provide hints for each hands-on lab, enabling you to work through many labs efficiently.

    About Your Instructor

    Paul Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is an MIT graduate with 40 years in computing. He is one of the world’s most experienced programming-languages trainers, having taught hundreds of instructor-present and virtual courses worldwide (based on his Pearson Education programming-language college textbooks and professional books) to software developers of all skill levels since 1992. He is a top author on O’Reilly Online Learning where his Python Fundamentals LiveLessons, Java Fundamentals LiveLessons and C# Fundamentals LiveLessons 50+ hour asynchronous video courses have ranked #1 eight of the last 12 years among 6,000 video products from 200+ publishers. Thousands of users have viewed his asynchronous streaming Python Fundamentals LiveLessons videos for over 10.5 million contact minutes. 10,000+ students have attended his live (synchronous), one-day, code-intensive Python Full Throttle and Python Data Science Full Throttle courses for over 2.7 million live contact minutes.

    Together, he and his co-author, Dr. Harvey M. Deitel, are among the world’s best-selling programming-language textbook, professional book, video and interactive multimedia e-learning authors, having written 100+ Pearson Education/Prentice Hall college textbooks and professional books on Python, Java, C++, C, C#, Internet/Web, Android, iOS, Swift, Visual Basic and more. Their books have 100+ translations into Italian, Japanese, German, Russian, Spanish, French, Polish, Simplified Chinese, Traditional Chinese, Korean, Portuguese, Greek, Urdu and Turkish.

    Deitel clients include some of the world’s largest companies, government agencies, branches of the military, and academic institutions—SLB, UCLA Anderson School of Management, Cisco, IBM, Siemens, Sun Microsystems (now Oracle), Dell, Fidelity, NASA at the Kennedy Space Center, White Sands Missile Range, the National Oceanic and Atmospheric Administration (NOAA), the National Severe Storm Laboratory, Rogue Wave Software, Boeing, Puma, iRobot and many more.

    Natural Language Processing

    • Perform natural language processing (NLP) tasks, which are fundamental to many of the forthcoming data science case studies
    • Use the textblob, nltk, textatistic and spacy NLP libraries and their pretrained models to perform various NLP tasks
    • Tokenize text
    • Parts-of-speech tagging
    • Sentiment analysis
    • Language detection and translation
    • Stemming and lemmatization
    • Spell checking and correction capabilities
    • Getting word definitions, synonyms and antonyms
    • Removing stop words from text
    • Creating word clouds with the wordcloud library
    • Determining text readability with textatistic
    • Using the spacy library for named entity recognition and similarity detection

    Data Mining Twitter

    • Twitter’s impact on businesses, brands, reputation, sentiment analysis, predictions and more
    • Register for a free Twitter developer account and get credentials to use their free web services
    • Use the tweepy Twitter API client to data mine Twitter
    • Search past tweets
    • Get a live stream of tweets
    • Access tweet metadata
    • Use natural language processing to prepare tweets for analysis
    • Perform sentiment analysis on tweets
    • Spot trending topics
    • Map tweets using the geopy library to geocode locations into latitudes and longitudes, and the folium library with OpenStreetMap map tiles

    IBM Watson and Cognitive Computing

    • Learn Watson’s range of services via their free Lite tier
    • Try demos of Watson services
    • Register for a free IBM Cloud account and get credentials to use various services
    • Install the Watson Developer Cloud Python SDK to interact with Watson services
    • Develop a traveler’s companion language translator app by using Python to weave together a mashup of the Watson Speech to Text, Language Translator and Text to Speech services

    Machine Learning with scikit-learn

    • Use the scikit-learn (sklearn) library with popular datasets to perform machine learning studies
    • Visualize and explore data with the seaborn and matplotlib libraries
    • Perform supervised machine learning with k-nearest neighbors classification and linear regression
    • Perform multi-classification with the Digits dataset
    • Divide a dataset into training, test and validation sets
    • Tune model hyperparameters with k-fold cross-validation
    • Measure model performance
    • Display a confusion matrix showing classification prediction hits and misses
    • Perform multiple linear regression with the California Housing dataset
    • Perform dimensionality reduction with PCA and TSNE on the Iris and Digits datasets to prepare them for two-dimensional visualizations
    • Perform unsupervised machine learning with k-means clustering and the Iris dataset

    Deep Learning

    • Neural networks and how they enable deep learning
    • Create Keras neural networks in the context of TensorFlow
    • Keras layers, activation functions, loss functions and optimizers
    • Deep Learning for Computer Vision: Convolutional neural network (CNN) trained on the MNIST dataset to recognize handwritten digits
    • Deep Learning for Sentiment Analysis—Recurrent neural network (RNN) trained on the IMDb (Internet Movie Database) dataset to perform binary classification of positive and negative movie reviews
    • Visualize a deep-learning network’s training progress with TensorBoard

    Big Data: Relational Databases

    • Structured Query Language (SQL), SQLite with the sqlite module


    Big Data: NoSQL

    • A brief tour of NoSQL and NewSQL big-data databases
    • Case Study: A MongoDB JSON document database
    • Creating the MongoDB Atlas cluster
    • Streaming tweets into MongoDB


    Big Data: Apache Spark and the pyspark Library

    • Docker and the Jupyter Docker Stacks
    • Word count with Spark
    • Spark Streaming: Counting Twitter hashtags using the pyspark-notebook Docker stack


    Big Data: Internet of Things (IoT) and Dashboards

    • Communicating among apps and IoT devices via publish/subscribe
    • Visualizing streaming data with freeboard.io and seaborn
    • IoT: Simulating an Internet-connected thermostat in Python with dweepy and dweet.io
    • Simulated streaming stock prices with pubNub

    • Python programmers who see exciting AI, machine learning, deep learning, big data and data science technologies popping up everywhere and who want a broad-based, code-intensive introduction to them.
    • Managers contemplating Python projects for their teams using AI, machine learning, deep learning and big data technologies and who want a code-intensive introduction to them.
    • Programmers who have taken the Intro to Python course.
    • R programmers whose organizations are considering adding or switching to Python and who want a code-intensive introduction to Python’s AI, machine learning, deep learning and big-data capabilities. For the best experience, R programmers should first take our Intro to Python course.
    • People who have taken the Intro to Python for Non-Programmers

    • Natural Language Processing
    • Data Mining Twitter with the tweepy library
    • IBM Watson and Cognitive Computing
    • Supervised Machine Learning with the scikit-learn library: Classification and Regression
    • Unsupervised Machine Learning with the scikit-learn library: Dimensionality Reduction and Clustering
    • Deep Learning for Computer Vision with Keras, Tensorflow and a Convolutional Neural Network (CNN)
    • Deep Learning for Sentiment Analysis with Keras, Tensorflow and a Recurrent Neural Network (RNN)
    • Big Data: Relational Databases with the sqlite library
    • Big Data: NoSQL Case Study—A MongoDB JSON Document Database using the pymongo library
    • Big Data: Apache Spark and the pyspark library
    • Big Data: Internet of Things and Dashboards with freeboard.io, dweet.io, dweepy and pubnub

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