TTML5503 - Introduction to AI, AI Programming and Machine Learning (AI / ML JumpStart)

Introduction to Artificial Intelligence (AI) & Machine Learning (AI & ML JumpStart) is a three-day, foundation-level, hands-on course that explores the fast-changing field of artificial intelligence (AI). programming, logic, search, machine learning, and natural language understanding. Students will learn current AI / ML methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence.

In this course, we will cut through the math and learn exactly how machine learning algorithms work. Although there is clearly a requirement for the students to have an aptitude for math, this course is about focusing on the algorithms that will be used to create machine learning models. Using clear explanations, simple pure Python code (no libraries!) and step-by-step labs, you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.

This course presents a wide variety of related technologies, concepts and skills in a fast-paced, hands-on format, providing students with a solid foundation for understanding and getting a jumpstart into working with AI and machine learning. Each topic area presents a specific challenge area, current progress, and approaches to the presented problem. Attendees will exit the course with practical understanding of related 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.

Student Testimonials

Instructor did a great job, from experience this subject can be a bit dry to teach but he was able to keep it very engaging and made it much easier to focus. Student
Excellent presentation skills, subject matter knowledge, and command of the environment. Student
Instructor was outstanding. Knowledgeable, presented well, and class timing was perfect. Student

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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. This course is ideally suited for a wide variety of technical learners who need an introduction to the core skills, concepts and technologies related to AI programming and machine learning. Attendees might include:
Developers aspiring to be a 'Data Scientist' or Machine Learning engineers
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand data science techniques
Information Architects who want to gain expertise in Machine Learning algorithms
Analytics professionals who want to work in machine learning or artificial intelligence
Graduates looking to build a career in Data Science and machine learning
Experienced professionals who would like to harness machine learning in their fields to get more insight about customers
Pre-Requisites: Students should have attended or have incoming skills equivalent to those in this course:
Solid basic Python Skills. Attendees without Python background may view labs as follow along exercises or team with others to complete them.
Good foundational mathematics in Linear Algebra and Probability
Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Detailed Class Syllabus

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

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

Matplotlib and Advanced Probability Concepts

A crash course in Matplotlib
Covariance and correlation
Conditional probability
Bayes' theorem

Algorithm Overview

Data Prep
Linear Algorithms
Simple Linear Algorithms
Multivariate Linear Regression
Logistic Regression
Non-Linear Algorithms
Classification Trees (CARTs)
Naive Bayes
k-Nearest Neighbors
Bootstrap Aggregation
Random Forest

Predictive Models

Linear regression
Polynomial regression
Multivariate regression and predicting car prices
Multi-level models

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
Clustering people based on income and age
Measuring entropy
Decision trees - Concepts
Decision trees - Predicting hiring decisions using Python
Ensemble learning
Support vector machine overview
Using SVM to cluster people by using scikit-learn

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

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

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

Apache Spark Basics | 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
Searching Wikipedia with Spark MLlib
Using the Spark 2.0 DataFrame API for MLlib

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


Build a UI for your Models
Build a REST API for your Models