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

• Explore 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. 
• Introduction to Generative AI & Prompt Engineering Basics: Get a quick look at Generative AI and prompt engineering, understanding their significance, recent advancements, potential applications, and best practices for effective interaction and overcoming common challenges. 

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

Please 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.  

What is AI and Machine Learning 

• Is machine learning difficult? 
• What is artificial intelligence   
• Difference between AI and machine learning 
• Machine learning examples 

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 

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  

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 

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 

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 

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 

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 

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 

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 

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. 

 Introduction to Generative AI 

• What is Generative AI 
• Why is Generative AI important? 
• Examples of how Generative AI works in the industry 
• What does Generative AI do? 
• Recent advancements in Generative AI 
• Potential applications and limitations 

Prompt Engineering Basics 

• Quick start to prompt engineering 
• How to interact with AI models 
• Practical examples and exercises 
• Best practices for crafting effective prompts 
• Common challenges and how to address them 

Bonus Content / Time Permitting 

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 

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  

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