Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- How it enhances data analysis capabilities
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Typical business applications:
- Forecasting sales
- Segmenting customers
- Predicting churn
2. Transitioning from Data Analysis to Machine Learning
- Review: manipulating data with Pandas
- Shifting from descriptive to predictive analysis
- Formulating a Machine Learning problem
3. Simplified Machine Learning Workflow
- Preparing the dataset
- Partitioning data (train vs test)
- Fitting a model
- Generating predictions
4. Data Preparation for Machine Learning
- Addressing missing values
- Encoding categorical features
- Basic feature selection
- Conceptual overview of scaling
5. Supervised Learning (Practical Application)
Regression
- Linear Regression
- Use case: predicting numerical outcomes (e.g., sales, demand)
Classification
- Logistic Regression
- Use case: binary outcomes (e.g., churn, fraud)
6. Unsupervised Learning
Clustering
- K-means clustering
- Use case: customer segmentation
7. Simplified Model Evaluation
- Comparing train vs test performance
- Accuracy metrics (classification)
- Understanding basic errors (regression)
8. Interpreting Results
- Comprehending model outputs
- Detecting patterns and trends
- Converting results into business insights
9. Comprehensive End-to-End Example
- Loading the dataset
- Cleaning and preparing data
- Training a model
- Assessing performance
- Deriving insights
Requirements
Prerequisites
- Foundational knowledge of Python
- Experience with Pandas and dataset management
- A basic understanding of data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with introductory Python skills
- Professionals who have completed Python for Data Analysis or a similar course
- Individuals new to Machine Learning
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
