TTML5503: Introduction to AI, AI Programming & Machine Learning | AI / ML JumpStart

About this Course

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.

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.

Audience Profile

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

At Course Completion

Working in a hands-on learnng environment led by our expert practitioner you’ll explore:

· Popular machine learning algorithms, their applicability and limitations

· Practical application of these methods in a machine learning environment

· Using supervised algorithms for classifying and splitting data

· Methods for cleaning and simplifying data

· Machine learning packages and tools

· Neural networks and ensemble methods for complex datasets

· Practical examples of Data Engineering and Machine Learning

Outline

1. What is AI and Machine Learning

· Is machine learning difficult?

· What is artificial intelligence

· Difference between AI and machine learning

· Machine learning examples

2. Types of Machine Learning

· Three different types of machine learning: supervised, unsupervised, and reinforcement learning

· Difference between labeled and unlabeled data

· The difference between regression and classification, and how they are used

3. Linear Regression

· Fitting a line through a set of data points

· Coding the linear regression algorithm in Python

· Using Turi Create to build a linear regression model to predict housing prices in a real dataset

· What is polynomial regression

· Fitting a more complex curve to nonlinear data

· Examples of linear regression

4. Optimizing the Training Process

· What is underfitting and overfitting

· Solutions for avoiding overfitting

· Testing the model complexity graph, and regularization

· Calculating the complexity of the model

· Picking the best model in terms of performance and complexity

5. The perceptron Algorithm

· What is classification

· Sentiment analysis

· How to draw a line that separates points of two colors

· What is a perceptron

· Coding the perceptron algorithm in Python and Turi Create

6. Logistic Classifiers

· Hard assignments and Soft assignments

· The sigmoid function

· Discrete perceptrons vs. Continuous perceptrons

· Logistic regression algorithm for classifying data

· Coding the logistic regression algorithm in Python

7. Measuring Classification Models

· Types of errors a model can make

· The confusion matrix

· what are accuracy, recall, precision, F-score, sensitivity, and specificity

· what is the ROC curve

8. The Naive Bayes Model

· What is Bayes theorem

· Dependent and independent events

· The prior and posterior probabilities

· Calculating conditional probabilities

· using the naive Bayes model

· Coding the naive Bayes algorithm in Python

9. Decision Trees

· What is a decision tree

· Using decision trees for classification and regression

· Building an app-recommendation system using users’ information

· Accuracy, Gini index, and entropy

· Using Scikit-Learn to train a decision tree

10. Neural Networks

· What is a neural network

· Architecture of a neural network: nodes, layers, depth, and activation functions

· Training neural networks

· Potential problems in training neural networks

· Techniques to improve neural network training

· Using neural networks as regression models

Bonus Content / Time Permitting

11. Bonus: Support vector machine and the Kernel methods

· What a support vector machine

· Which of the linear classifiers for a dataset has the best boundary

· Using the kernel method to build nonlinear classifiers

· Coding support vector machines and the kernel method in Scikit-Learn

12. Bonus: Ensemble learning

· What ensemble learning is

· Using bagging to combine classifiers

· Using boosting to combine classifiers

· Ensemble methods: random forests, AdaBoost, gradient boosting, and XGBoost

13. Bonus: Real-World Example: Data Engineering and ML

· Cleaning up and preprocessing data to make it readable by our model

· Using Scikit-Learn to train and evaluate several models

· Using grid search to select good hyperparameters for our model

· Using k-fold cross-validation to be able to use our data for training and validation simultaneously

Prerequisites

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

Take Before: Attending students should have incoming skills equivalent to those in the course(s) below, or should have attended these as a pre-requisite:

· TTPS4800 Introduction to Python (3 days)