Course Snapshot
- Duration: 2 days
- Skill-level: Foundation-level Data Machine Learning skills for Intermediate skilled team members. This is not a basic class.
- Targeted Audience: This course is geared for those who wants to build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.
- 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, or remote instructor led delivery, or CBT/WBT (by request).
- Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals.
In Machine Learning you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you’re done working through these fun and informative projects, you’ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems.
Working in a hands-on learning environment, led by Machine Learning expert instructor, students will learn about and explore:
- you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects
- you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice
- you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms.
- You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more.
Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below
- Code fundamental ML algorithms from scratch
- Collect and clean data for training models
- Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow
- Apply ML to complex datasets with images and text
- Deploy ML models to a production-ready environment
Audience & Pre-Requisites
This course is for readers want to learn the essentials of machine learning by completing a carefully designed set of real-world projects.
Pre-Requisites: Students should have
- Basic to Intermediate IT Skills.
- Good foundational mathematics or logic skills
- For readers with existing programming skills.
- No previous machine learning experience required
Course Agenda / Topics
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- Introduction to machine learning
- Machine learning
- Machine learning process
- Modeling and model validation
- Machine learning for regression
- Car-price prediction project
- Exploratory data analysis
- Machine learning for regression
- Predicting the price
- Machine learning for classification
- Churn prediction project
- Feature engineering
- Machine learning for classification
- Evaluation metrics for classification
- Evaluation metrics
- Confusion table
- ROC curve and AUC score
- Parameter tuning
- Deploying machine learning models
- Churn prediction model
- Model serving
- Managing dependencies
- Deployment
