Practical Artificial Intelligence

Introduces two instructors who cover data science, AI, and practical programming for healthcare and IT; outlines four success criteria: personal, professional, global culture, and programming, with Trainova mentoring.

Examine information technologies as computer and communication systems that collect, store, secure, and transmit data across cloud, internet, and 5g, affecting education, health care, industry, commerce, and manufacturing.

Explore artificial intelligence fundamentals, including machine learning, natural language processing, and robotic systems that learn, sense, and solve problems, with examples like voice-to-text and face recognition.

Explore how artificial intelligence already shapes daily life, from voice typing and translation to Google Docs, Alexa, and smart cameras, and recognize the rise of digital characters and deepfakes.

Natural language processing enables fast information retrieval from unstructured data, reducing errors and enabling data-driven decisions via end-to-end pipelines with document classification, sentiment analysis, and named entity recognition.

Discover the AI software we will use this course and how to install it on your computer, including Python under Anaconda, Anaconda prompt, pip, Google Colab, and Visual Studio Code.

Install the Anaconda Python distribution on Windows, macOS, or Linux, choose 64-bit vs 32-bit, and use Navigator to manage Python versions and data science packages such as pandas and matplotlib.

Explore setting up anaconda and launching spyder to write and run python code. Learn to create variables, print messages, and run code cells in spyder using the navigator environment.

Learn to install and run Python packages using pip and PyPI, search for relevant packages, and run simple code to play musical notes with loops and range control.

Set up Anaconda, launch a Jupyter notebook, install cake mix, read Excel data, and plot salary bars, while mastering ctrl-c to stop execution and keeping the console clean.

Explore using Jupiter notebook and Google Colab to execute code cells, install packages, and visualize salary data with plots for highest and lowest salaries.

Explore R as a statistics-focused alternative to Python, highlighting its free availability and shorter code, and learn to install and run R via Anaconda, R project site, or Google Colab.

explore how to use the ipad for robotic process automation, automating emails, excel data work, and data extraction within automation cloud, with attended and unattended robots.

Open an excel file on the iPad using UiPath Studio and read a worksheet range, then convert its data into a data table and display it via a message box.

Explore Power BI for business intelligence, covering data collection, analysis, dashboards, and graphical analytics with Power BI Desktop and Power BI service for self-service analytics.

Install Power BI Desktop from the Microsoft Store, import an Excel data sample, transform and shape the data, and build interactive visuals and slicers for dashboards.

Explore customizing and publishing Power BI dashboards by adding slicers, visuals, and cross reports; publish to workspace, export to PowerPoint, PDF, or Excel, and share with colleagues.

Automate music with Python in Spyder by generating random notes, handling octaves and timing, and playing musical beeps. Build a music folder and reuse Twinkle Twinkle Little Star as examples.

Define a make new notes function to generate random notes with timing, append them to a music list, build a make music sequence, and play the file.

Learn how to save simulated music to a time-stamped text file by creating a music list, writing notes as strings, and using the schedule package to automate tasks in Python.

Learn to automate music by scheduling tasks, executing Python cells, and using while loops with sleep intervals to generate, save, and play new tracks.

This lecture teaches face recognition from digital images using Google Colab and GPU runtime, employing the face_recognition library in Python to detect faces and draw bounding rectangles.

this lecture builds a digital assistant for pet care by creating a virtual pet class, naming the pet, tracking hunger and mood, and enabling talk, feed, and play.

Explore practical NLP techniques by building a binary spam classifier for email text, using data loading, exploration, and simple preprocessing to prepare text for machine learning.

Develop a limits function for a text processing pipeline by iterating over documents, tokenizing, tagging parts of speech, and applying adjective, verb, and adverb stemmer limitations to clean the corpus.

Apply email data cleaning and text preprocessing, convert clean text to tf-idf features, and train and evaluate a machine learning classifier with a train/test split, using a classification report.

Learn to set up a mobile development environment and build cross-platform apps with npm, Ionic, and Capacitor, then run and test Android and iOS apps.

Explore practical app styling techniques with external images, text styling, and a complete about page using Ionic cards, placeholders, and responsive layout.

Create a MySQL database and course table with an auto-incremented id, course name, explanation, duration, start date, start time, a timestamp, and utf8 encoding, then insert sample data.

Practical artificial intelligence teaches how to use Python to automatically upload course data from Excel into a MySQL database via PyMySQL, including establishing connections and querying the courses table.

Prioritize AI project ideas, set realistic milestones and deadlines, and seek support to avoid burnout. Pursue ambitious work aligned with speed, convenience, awesome experiences, and leading-edge trends.