Course Outline
1. Introduction to Machine Learning
- Defining Machine Learning
- Extending data analysis capabilities
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Key business applications:
- Sales forecasting
- Customer segmentation
- Churn prediction
2. From Data Analysis to Machine Learning
- Review: Manipulating data with Pandas
- Shifting from descriptive to predictive analytics
- Formulating a Machine Learning problem
3. Simplified Machine Learning Workflow
- Dataset preparation
- Data partitioning (training vs. testing)
- Model training
- Generating predictions
4. Data Preparation for Machine Learning
- Managing missing data points
- Transforming categorical variables
- Basic feature selection techniques
- Conceptual overview of scaling methods
5. Supervised Learning (Practical Application)
Regression
- Linear Regression
- Application: Forecasting numerical metrics (e.g., sales volume, demand)
Classification
- Logistic Regression
- Application: Binary classification tasks (e.g., customer churn, fraud detection)
6. Unsupervised Learning
Clustering
- K-means clustering algorithm
- Application: Segmenting customer bases
7. Model Evaluation (Simplified)
- Comparing training and testing performance
- Accuracy metrics for classification models
- Understanding error metrics in regression models
8. Interpreting Results
- Decoding model outputs
- Identifying underlying patterns and trends
- Converting analytical results into strategic business insights
9. End-to-End Practical Example
- Loading the dataset
- Data cleaning and preparation
- Training the model
- Assessing performance
- Deriving actionable insights
Requirements
Prerequisites
- Foundational proficiency in Python
- Comfort with using Pandas to manage datasets
- A working understanding of basic data analysis principles
Target Audience
- Data Analysts
- Business Analysts possessing basic Python skills
- Professionals who have completed the Python for Data Analysis course or an equivalent program
- Individuals new to 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
