Introduction to AI, AI Programming & Machine Learning

Geared for technical professionals, our Introduction to AI & Machine Learning JumpStart course is a three-day, hands-on workshop style event designed to get you quickly up and running with latest skills, tools and tech in essential AI and ML, demystifying the field of artificial intelligence without drowning you in mathematics. Whether you're a budding developer or a tech enthusiast, we'll guide you through the foundations of AI and machine learning, and equip you with the knowledge, problem-solving skills and confidence needed to apply this innovative tech in real-world scenarios.

The course is rich with hands-on activities, challenge labs, knowledge checks, valuable discussions and focused projects that can be done individually or in groups. Working in a hands-on learning environment, guided by our engaging AI expert, you’ll explore AI and Machine Learning essentials, practical examples, tools and best practices. You’ll learn how to integrate AI and machine learning principles into real-world projects, enabling you to innovate in areas like product development, customer experience enhancement, and complex problem-solving. You’ll explore the differences and applications of supervised, unsupervised, and reinforcement learning, laying the groundwork for exploration and utilization in diverse contexts. You’ll learn how to employ AI and machine learning concepts for making informed, data-driven decisions that can have far-reaching impacts on various aspects of business and technology.

Throughout the course you’ll gain highly-valuable, expert guided experience using cutting-edge tools and algorithms through hands-on labs, ensuring that you can confidently apply these new skills and concepts in practical scenarios. You’ll leave the event well-versed and ready to apply key AI and Machine Learning concepts in your work. Whether you’ll be coding algorithms, classifying data, or optimizing machine learning models, you’ll have the essentials skills needed to tackle any AI-related project.


05/15/24 - TTV - Virtual-Instructor Led - Virtual-Instructor Led	(click to enroll)
07/01/24 - TTV - Virtual-Instructor Led - Virtual-Instructor Led	(click to enroll)
08/26/24 - TTV - Virtual-Instructor Led - Virtual-Instructor Led	(click to enroll)
10/07/24 - TTV - Virtual-Instructor Led - Virtual-Instructor Led	(click to enroll)
11/18/24 - TTV - Virtual-Instructor Led - Virtual-Instructor Led	(click to enroll)

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

Grasp AI & Machine Learning Basics: You'll start your journey by understanding what AI and Machine Learning are, distinguishing between them, and discovering how they're applied in various fields. You'll also get a good look at practical examples of Machine Learning.
Decode Types of Machine Learning: You'll navigate through the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and gain insight into their distinctive applications, and explore their practical application.
Master Data Prep: You’ll learn and apply critical methods for cleaning and simplifying data
Master Algorithms: You’ll explore popular machine learning algorithms, their applicability and limitations
Get Hands-On: You'll learn how to code linear regression and logistic regression algorithms in Python, and gain hands-on experience applying them in real-world scenarios in a machine learning environment working with various machine learning packages and tools.
Optimize Machine Learning Models: You'll dig into the art of model optimization, learning how to prevent underfitting and overfitting to ensure your machine learning models are accurate and reliable.
Conquer Classification: You'll uncover the secrets of the perceptron algorithm and logistic classifiers, learning how to classify data effectively and carry out sentiment analysis like a pro.
Responsible AI Development: You’ll gain insight into the ethical considerations and responsible practices in AI, ensuring that solutions are developed with a consciousness of privacy, bias, and societal implications.
Venture into Generative AI: You'll step into the fascinating world of Generative AI and Generative Adversarial Networks (GANs), exploring their structure, functionality, and the concept of latent space in generative models. After the course, you'll understand how these advanced AI models can contribute to your projects.

Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional AI, machine learning, data science, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs.

Viewing outline for:

lease note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation.

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

11. Responsible AI: Navigating the Grey Areas

• Understanding Ethical Implications in AI

• Grasp the moral complexities in recommendation systems.

• Bias and Fairness in Recommenders

• Dissect potential biases in AI-driven recommendations.

12. Introduction to Generative AI

• Understanding Generative AI

• How Generative AI fits into the broader AI and Machine Learning landscape

• Differences between generative and discriminative models

• Introduction to Generative Adversarial Networks (GANs)

• Understanding the concept of latent space in generative models

• Basic structure and components of GANs: generator and discriminator

13. OPTIONAL: Applications of Generative AI in Business

• Improving customer experience: Using generative AI for personalized content creation, such as emails, ads, and product descriptions

• Product development: Using GANs for generating new ideas for products, fashion designs, and more

• Data augmentation: How generative models can create additional training data for other machine learning models, improving their performance

• Content creation: Using AI for generating realistic images, music, text, and more

• Risk management: Using generative AI to simulate different business scenarios and outcomes

• Healthcare: Generating synthetic medical data for research while preserving patient privacy

Bonus Content / Time Permitting

14. 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

15. 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

16. 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

Pre-Requisites: Students should have attended or have incoming skills equivalent to those in this course:Basic Understanding of Python as well as familiarity with Python Libraries (Pandas and Numpy, etc.). Attendees without Python background may view labs as follow along exercises or team with others to complete them.Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and suBasic Math and Problem-Solving SkillsUnderstanding of Basic Data Structures

This introductory-level hands-on course is suited for a wide variety of technical learners who need an introduction to the core skills, concepts, tech, tools and skills related to AI programming and machine learning.

Suitable 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

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

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