This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll learn how to:  

• Understand the main concepts and principles of predictive analytics 
• Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects 
• Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations 
• Learn to deploy a predictive model's results as an interactive application 
• Learn about the stages involved in producing complete predictive analytics solutions 
• Understand how to define a problem, propose a solution, and prepare a dataset 
• Use visualizations to explore relationships and gain insights into the dataset 
• Learn to build regression and classification models using scikit-learn 
• Use Keras to build powerful neural network models that produce accurate predictions 
• Serve a model's predictions as a web application 

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals. 

This course is ideally suited for Python developers, data analysts, and aspiring data scientists looking to expand their skills into AI and Machine Learning. It is also highly beneficial for product managers and business leaders aiming to acquire a hands-on understanding of AI's impact on product development and business strategy.  

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills: 

• Basic Understanding of Python as well as familiarity with Python Libraries (Pandas and Numpy, etc.) 
• Basic Math and Problem-Solving Skills 
• Understanding of Basic Data Structures 

 Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We'll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation. 

The Predictive Analytics Process 

• Technical requirements 
• What is predictive analytics? 
• Reviewing important concepts of predictive analytics 
• The predictive analytics process 
• A quick tour of Python's data science stack 

Problem Understanding and Data Preparation 

• Technical requirements 
• Understanding the business problem and proposing a solution 
• Practical project - diamond prices 
• Practical project - credit card default 

Dataset Understanding – Exploratory Data Analysis 

• Technical requirements 
• What is EDA? 
• Univariate EDA 
• Bivariate EDA 
• Introduction to graphical multivariate EDA 

Predicting Numerical Values with Machine Learning 

• Technical requirements 
• Introduction to ML 
• Practical considerations before modeling 
• MLR 
• Lasso regression 
• KNN 
• Training versus testing error 

Predicting Categories with Machine Learning 

• Technical requirements 
• Classification tasks 
• Credit card default dataset 
• Logistic regression 
• Classification trees 
• Random forests 
• Training versus testing error 
• Multiclass classification 
• Naive Bayes classifiers 

Introducing Neural Nets for Predictive Analytics 

• Technical requirements 
• Introducing neural network models 
• Introducing TensorFlow and Keras 
• Regressing with neural networks 
• Classification with neural networks 
• The dark art of training neural networks 

Model Evaluation 

• Technical requirements 
• Evaluation of regression models 
• Evaluation for classification models 
• The k-fold cross-validation 

Model Tuning and Improving Performance 

• Technical requirements 
• Hyperparameter tuning 
• Improving performance 

Implementing a Model with Dash 

• Technical requirements 
• Model communication and/or deployment phase 
• Introducing Dash 
• Implementing a predictive model as a web application