Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it complements and expands data analysis capabilities
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Typical business applications:
- Sales forecasting
- Segmenting customers
- Predicting customer churn
2. Transitioning from Data Analysis to Machine Learning
- Review: Handling data with Pandas
- Shifting from descriptive to predictive analytics
- Formulating a Machine Learning problem
3. The Machine Learning Workflow (Simplified)
- Preparing the dataset
- Dividing data into training and testing sets
- Training the model
- Generating predictions
4. Preparing Data for Machine Learning
- Managing missing data
- Encoding categorical features
- Basic feature selection
- Overview of scaling concepts
5. Supervised Learning (Practical Application)
Regression
- Linear Regression
- Application: Forecasting numeric values (e.g., sales figures, demand)
Classification
- Logistic Regression
- Application: Binary outcome scenarios (e.g., churn risk, fraud detection)
6. Unsupervised Learning
Clustering
- K-means clustering
- Application: Customer segmentation
7. Evaluating Models (Simplified)
- Comparing performance on training vs. testing data
- Measuring accuracy in classification tasks
- Understanding basic error metrics for regression
8. Analyzing Results
- Comprehending model outputs
- Recognizing underlying patterns and trends
- Converting analytical results into business insights
9. Comprehensive End-to-End Project
- Loading the dataset
- Cleaning and preparing the data
- Training the model
- Assessing performance
- Deriving key insights
Requirements
Requirements
- Foundational skills in Python
- Experience with Pandas and manipulating datasets
- A general understanding of core data analysis principles
Who Should Attend
- Data Analysts
- Business Analysts with a basic grasp of Python
- Professionals who have finished the Python for Data Analysis course or possess equivalent skills
- Individuals new to the field of Machine Learning
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
