Hands-On Introduction to Artificial Intelligence, AI Programming & Machine Learning (TTML5503)

This hands-on, foundational course explores the fast-changing field of artificial intelligence (AI) programming, logic, search, machine learning, and natural language understanding. You will learn current AI / ML methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. You will leave this course with a practical understanding of core skills, methods and algorithms, and be prepared for continued learning in next-level, more advanced follow on courses that dive deeper into specific skillsets or tools.

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Learning Objectives

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations; practical application of these methods in a machine learning environment; and practical use cases and limitations of algorithms.

  • Working in a hands-on lab environment led by our expert instructor, attendees will explore:
  • Getting Started with Python & Jupyter
  • Statistics and Probability Refresher, and Python Practice
  • Matplotlib and Advanced Probability Concepts
  • Algorithm Overview
  • Predictive Models
  • Applied Machine Learning
  • Recommender Systems
  • Dealing with Data in the Real World
  • Machine Learning on Big Data (with Apache Spark)
  • Testing and Experimental Design
  • GUIs and REST: Build a UI & REST API for your Models


    Course Details

    Course Outline

    1 - Getting Started
  • Installing a Python Data Science Environment
  • Using and understanding IPython (Jupyter) Notebooks
  • Python basics - Part 1
  • Understanding Python code
  • Importing modules
  • Python basics - Part 2
  • Running Python scripts
  • 2 - Statistics and Probability Refresher, and Python Practice
  • Types of data
  • Mean, median, and mode
  • Using mean, median, and mode in Python
  • Standard deviation and variance
  • Probability density function and probability mass function
  • Types of data distributions
  • Percentiles and moments
  • 3 - Matplotlib and Advanced Probability Concepts
  • A crash course in Matplotlib
  • Covariance and correlation
  • Conditional probability
  • Bayes' theore
  • 4 - Algorithm Overview
  • Data Prep
  • Linear Algorithms
  • Non-Linear Algorithms
  • Ensembles
  • 5 - Predictive Models
  • Linear regression
  • Polynomial regression
  • Multivariate regression and predicting car prices
  • Multi-level models
  • 6 - Applied Machine Learning with Python
  • Machine learning and train/test
  • Using train/test to prevent overfitting of a polynomial regression
  • Bayesian methods - Concepts
  • Implementing a spam classifier with Naïve Bayes
  • K-Means clustering
  • 7 - Recommender Systems
  • What are recommender systems?
  • Item-based collaborative filtering
  • How item-based collaborative filtering works?
  • Finding movie similarities
  • Improving the results of movie similarities
  • Making movie recommendations to people
  • Improving the recommendation results
  • 8 - More Applied Machine Learning Techniques
  • K-nearest neighbors - concepts
  • Using KNN to predict a rating for a movie
  • Dimensionality reduction and principal component analysis
  • A PCA example with the Iris dataset
  • Data warehousing overview
  • Reinforcement learning
  • 9 - Dealing with Data in the Real World
  • Bias/variance trade-off
  • K-fold cross-validation to avoid overfitting
  • Data cleaning and normalization
  • Cleaning web log data
  • Normalizing numerical data
  • Detecting outliers
  • 10 - Apache Spark - Machine Learning on Big Data
  • Installing Spark
  • Spark introduction
  • Spark and Resilient Distributed Datasets (RDD)
  • Introducing MLlib
  • Decision Trees in Spark with MLlib
  • K-Means Clustering in Spark
  • TF-IDF
  • Searching Wikipedia with Spark MLlib
  • Using the Spark 2.0 DataFrame API for MLlib
  • 11 - Testing and Experimental Design
  • A/B testing concepts
  • T-test and p-value
  • Measuring t-statistics and p-values using Python
  • Determining how long to run an experiment for
  • A/B test gotchas
  • 12 - GUIs and REST
  • Build a UI for your Models
  • Build a REST API for your Models
  • 13 - What the Future Holds
  • Actual course outline may vary depending on offering center. Contact your sales representative for more information.

    Who is it For?

    Target Audience

    Students attending this class should have a grounding in Enterprise computing. Students attending this course should be familiar with Enterprise IT, have a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying AI.

    Hands-On Introduction to Artificial Intelligence, AI Programming & Machine Learning (TTML5503)

    Course Length : 3 Days

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