Our experts help you dive deep into essential tech skills, navigate through challenges, and prepare you to use what you've learned with confidence, and the platform provides you with the path and resources for long term success.  

Some of the core topics you'll explore include: 

• Regression Analysis: Master the technique to understand and predict the relationship between dependent and independent variables. 
• Binary and Multiclass Classification: Learn to categorize data into distinct categories or classes. 
• Hyperparameter Tuning: Fine-tune machine learning algorithms to optimize their performance. 
• Feature Engineering: Acquire the skill to select and transform variables to improve model accuracy. 
• Handling Imbalanced Datasets: Develop strategies to work with datasets where target classes are unevenly distributed. 
• Dimensionality Reduction: Grasp methods to reduce the number of random variables and ensure models are efficient. 
• Ensemble Learning: Understand how to combine multiple models to enhance prediction accuracy. 
• Model Evaluation: Become adept at assessing the performance of machine learning algorithms. 
• Python Programming for AI: Gain proficiency in utilizing Python for building AI-driven applications. 
• Generalization Techniques: Learn to build models that perform well on unseen data. 
• Data Preprocessing: Understand techniques for cleaning, transforming, and normalizing raw data for optimal model training. 
• Advanced Algorithms: Dive deep into sophisticated machine learning algorithms to tackle complex data tasks. 
• Ethics in AI and Machine Learning: Explore and emphasize the ethical considerations, security and privacy issues in Ai and Machine Learning. 
• Using ChatGPT and Other Tools: Using relevant AI tools to increase efficiency and productivity 
• Building a Complete AI Driven Application: You'll also have a hands-on experience building an AI app in a capstone project. 

If your group requires other topics or specific business use cases, labs or data sets, we can adjust the content or labs to best suit your needs 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.  

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. 

Introduction and Regression 

• Understanding the Python ecosystem for data science  
• Review of Python libraries relevant to data science  
• Basics of regression analysis  
• Linear regression in Python  
• Multiple regression analysis  
• Hands-on Lab: Regression Analysis with Python 

Classification and Cluster Analysis 

• Understand and implement binary and multiclass classification.  
• Implement and assess the quality of a cluster analysis. 
• Logistic regression for binary classification  
• Performance metrics for binary classification  
• Hands-On Lab: Binary Classification  
• Overview of multiclass classification  
• Understanding and implementing RandomForest  
• Hands-On Lab: Multiclass Classification with RandomForest  
• Introduction to cluster analysis  
• K-Means clustering in Python  
• Assessing cluster quality  
• Hands-On Lab: Cluster Analysis  

Model Performance, Generalization, and Hyperparameter Tuning 

• Evaluate model performance using relevant metrics.  
• Understand and implement techniques for model generalization.  
• Learn about hyperparameters and methods for tuning them. 
• Understanding confusion matrix, precision, recall, F1 score  
• ROC and AUC analysis  
• Hands-On Lab: Model Performance Assessment  
• Understanding overfitting and underfitting  
• Cross-validation for model generalization  
• Hands-On Lab: Model Generalization Techniques  
• Introduction to hyperparameters and their importance  
• Grid search and random search for hyperparameter tuning  
• Hands-On Lab: Hyperparameter Tuning with Python 

Model Interpretation, Dataset Analysis, Data Preparation 

• Learn techniques for interpreting model coefficients and understanding feature importance. 
• Hands-On Lab: Machine Learning Model Interpretation  
• Techniques for data exploration and visualization  
• Learn methods for data exploration, visualization, univariate, and multivariate analysis.  
• Hands-On Lab: Dataset Analysis with Python  
• Dealing with missing values  
• Outlier detection and handling  
• Encoding categorical variables  
• Hands-On Lab: Data Preparation with Python 

Feature Engineering, Imbalanced Datasets, Dimensionality Reduction, and Ensemble Learning 

• Learn techniques for feature engineering and handling imbalanced datasets. 
• Understand and implement dimensionality reduction techniques.  
• Hands-On Lab: Feature Engineering and Dimensionality Reduction  
• Learn about ensemble learning methods and their implementation. 
• Implementing ensemble learning methods  
• Hands-On Lab: Ensemble Learning with Python 

Capstone Project / Workshop 

• Students will build their own AI investor using Python. Students will gain an understanding of the stock market approach from a purely data driven perspective, and will use that to build a stock investor. Students will be able to customize the investor (aggressive or defensive).  
• Hands-On Lab: Project Workshop 
• Apply learned techniques to a given problem statement.  
• Understand how to troubleshoot and improve model performance. 

OPTIONAL / Additional Time Required / Project Presentations and Course Wrap-Up 

• Present the final project and receive feedback.  
• Review the key learning outcomes from the course.