Course description
Predictive Analytics: Basic Modeling Techniques
This course is part of the Machine Learning Operations (MLOps) Program. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions. As you get further into the program, you will learn how to fit that model into a machine learning pipeline.
You will get hands-on experience with the top techniques in supervised learning: linear and logistic regression modeling, decision trees, neural networks, ensembles, and much more.
But most importantly, by the end of this course, you will know
- What a predictive model can (and cannot) do, and how its data is structured
- How to predict a numerical output, or a class (category)
- How to measure the out-of-sample (future)performance of a model
Upcoming start dates
Suitability - Who should attend?
Prerequisites:
- Python
- Statistics
We will present Python code to illustrate how to fit models, so we assume some familiarity with Python. Some exposure to basic statistics is also helpful, more from a comfort perspective than from a need to dive deep into statistical routines.
Outcome / Qualification etc.
What you'll learn
After completing this course, you will be able to:
- Develop a variety of machine learning algorithms for both classification and regression, including linear and logistic regression, decisions trees and neural networks
- Evaluate machine learning model performance with appropriate metrics
- Combine multiple models into ensembles to improve performance
- Explain the special contribution that deep learning has made to machine learning task
Training Course Content
Data Structures; Linear and Logistic Regression
- Classification and Regression
- Rectangular Data
- Regression
- Partitioning and Overfitting
- Illustration - Linear Regression(for verified users)
- Knowledge Check 1.1
- Logistic Regression
- Illustration - Logistic Regression(for verified users)
- Understand and Prepare Data
- Visualization
- CRISP-DM framework
- P-Values
- Knowledge Check 1.2
- Discussion Prompt #1(for verified students, graded)
- Quiz #1(for verified students, graded)
- Exercise #1 - Linear Regression(for verified students, graded)
- Exercise #2 - Logistic Regression(for verified students, graded)
- Summary
Assessing Models; Decision Trees
- Assessing Model Performance: Metrics
- ROC Curve and Gains Chart
- Decision Trees
- Illustration - Classification Tree(for verified users)
- Knowledge Check 2
- Quiz #2(for verified students, graded)
- Exercise #3 - Regression Tree(for verified students, graded)
- Exercise #4 - Classification Tree(for verified students, graded)
- Summary
Ensembles
- Cross validation
- Module 3 Reading
- Ensembles
- Illustration - Ensemble Methods(for verified users)
- Knowledge Check 3
- Discussion Prompt #2(for verified students, graded)
- Quiz #3(for verified students, graded)
- Exercise #5 - Ensemble Methods(for verified students, graded)
- Summary
Neural Networks
- Neural Nets
- Illustration - Neural Nets(for verified users)
- Deep Learning
- Reading
- Knowledge Check 4
- Quiz #4(for verified students, graded)
- Exercise #6 - Neural Nets(for verified students, graded)
- Summary
Course delivery details
This course is offered through Statistics.com, a partner institute of EdX.
5-7 hours per week
Expenses
- Verified Track -$149
- Audit Track - Free