Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- Extending the scope of data analysis
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Common business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. Transitioning from Data Analysis to Machine Learning
- Review: Working with data in Pandas
- Shifting from descriptive to predictive analysis
- Defining a Machine Learning problem
3. Simplified Machine Learning Workflow
- Dataset preparation
- Data splitting (training vs. testing)
- Model training
- Generating predictions
4. Data Preparation for Machine Learning
- Addressing missing values
- Encoding categorical variables
- Feature selection (basic techniques)
- Scaling (conceptual introduction)
5. Supervised Learning (Hands-on Session)
Regression
- Linear Regression
- Application: Predicting numerical values (e.g., sales, demand)
Classification
- Logistic Regression
- Application: Binary outcomes (e.g., churn, fraud detection)
6. Unsupervised Learning
Clustering
- K-means clustering
- Application: Customer segmentation
7. Simplified Model Evaluation
- Comparing training vs. testing performance
- Accuracy in classification
- Fundamental understanding of errors in regression
8. Interpreting Results
- Understanding model outputs
- Identifying patterns and trends
- Converting results into business insights
9. Practical End-to-End Example
- Loading the dataset
- Preparing and cleaning data
- Training a model
- Evaluating performance
- Extracting insights
Requirements
Prerequisites
- Fundamental knowledge of Python
- Familiarity with Pandas and dataset manipulation
- Understanding of core data analysis concepts
Target Audience
- Data Analysts
- Business Analysts with basic Python proficiency
- Professionals who have completed the Python for Data Analysis course or possess equivalent skills
- Beginners in the field of 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
