- Duration: 3 days
- Skill-level: Foundation-level machine learning skills for Intermediate skilled team members. This is not a basic class.
- Targeted Audience: This course is geared for Python experienced developers, analysts or others who are to work developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics
- Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required.
- Delivery Format: This course is available for onsite private classroom presentation.
- Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals.
Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modeling, classification, and regression. You’ll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you’re done, you’ll be ready to successfully build, deploy, and maintain your own powerful ML systems.
Working in a hands-on learning environment, led by our Real-World Machine Learning expert instructor, students will learn about and explore:
- You’ll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming
- you’ll be ready to successfully build, deploy, and maintain your own powerful ML systems.
- It introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.
Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below
- Predicting future behavior
- Performance evaluation and optimization
- Analyzing sentiment and making recommendations
Audience & Pre-Requisites
This course is geared introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.
Pre-Requisites: Students should have
- Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them.
- No prior machine learning experience assumed. Readers should know Python.
- Good foundational mathematics or logic skills
- No machine learning experience or advanced math skills necessary.
- Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
Course Agenda / Topics
- What is machine learning? Understanding how machines learn
- Using data to make decisions
- Following the ML workflow: from data to deployment
- Boosting model performance with advanced techniques
- Real-world data
- Getting started: data collection
- Preprocessing the data for modeling
- Using data visualization
- Modeling and prediction
- Basic machine-learning modeling
- Classification: predicting into buckets
- Regression: predicting numerical values
- Model evaluation and optimization
- Model generalization: assessing predictive accuracy for new data
- Evaluation of classification models
- Evaluation of regression models
- Model optimization through parameter tuning
- Basic feature engineering
- Motivation: why is feature engineering useful?
- Basic feature-engineering processes
- Feature selection
- Example: NYC taxi data
- Data: NYC taxi trip and fare information
- Advanced feature engineering
- Advanced text features
- Image features
- Time-series features
- Advanced NLP example: movie review sentiment
- Exploring the data and use case
- Extracting basic NLP features and building the initial model
- Advanced algorithms and model deployment considerations
- Scaling machine-learning workflows
- Before scaling up
- Scaling ML modeling pipelines
- Scaling predictions
- Example: digital display advertising
- Display advertising
- Digital advertising data
- Feature engineering and modeling strategy
- Size and shape of the data
- Singular value decomposition
- Resource estimation and optimization
- K-nearest neighbors
- Random forests
- Other real-world considerations