Machine Learning With Python
3 Days
Overview
This course provides a foundation of the two largest areas in machine learning: supervised and unsupervised learning. The instructor will demonstrate how machine learning techniques are applied to business problems, as well as how to implement these techniques using popular Python libraries. Lessons incorporate both lectures and hands-on exercises with a focus on cultivating practical skills.
This training will make your team competent to apply machine learning concepts to business problems. Your team will then be able to hit the ground running, using their new skills to immediately impact their work.
Course Outcomes
Upon completion of the course, attendees should be able to:
- Understand EDA and apply it for Data analysis
- Define “Machine Learning” and common terminology
- Explain different types of machine learning and the problems each can solve
- Identify if a problem is a regression, classification, or clustering problem
- Identify a useful metric for the business problem and optimize a model against it
- Identify important features for the model
- Train and predict on messy datasets, including data that has outliers and/or missing data
- Estimate the performance of a model on new data
- Identify the strengths and weaknesses when selecting a model for a problem
Training Content
DAY 1: Python for Exploratory Data Analysis (EDA)
- Introduction to Pandas
- Exploratory Data Analysis (EDA)
- Introduction to Statistics
- Univariate & Bivariate Analysis
- Data Cleaning
- Data visualization in Python
DAY 2: Introduction to Supervised Learning & Regression
- Introduction to Machine Learning Terminology & Algorithms
- Linear Regression (Ordinary Least Squares)
- Polynomial Regression
- Overfitting vs Underfitting
- Regularization
- Cross-validation and measuring generalizability
- Overfitting vs Underfitting with Regularization
- Feature engineering
DAY 3: Classification & Clustering
- Building a Classification Model – with a Classic use case
- Ensemble methods
- Unsupervised Learning Problem: Clustering – with a Classic use case
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