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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    MLBOCAL21E09

Who should attend & recommended skills:

Those with basic IT & programming experience who wish to learn ML essentials

Who should attend & recommended skills

  • This course is geared for those who want to learn the essentials of machine learning by completing a carefully designed set of real-world projects and build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.
  • Foundation-level Data Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Programming: Basic (1-2 years’ experience)
  • Machine Learning: Not required

About this course

In Machine Learning you will learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you will start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You will 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 are done working through these fun and informative projects, you will have a comprehensive machine learning skill set you can apply to practical on-the-job problems.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by Machine Learning expert instructor, students will learn about and explore:
  • Creating and deploying Python-based machine learning models for a variety of increasingly challenging projects
  • The essentials of machine learning by completing a carefully designed set of real-world projects.
  • Starting with the basic concepts of ML then tackling your first challenge: creating a car price predictor using linear regression algorithms.
  • Advancing through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more.
  • Coding fundamental ML algorithms from scratch
  • Collecting and cleaning data for training models
  • Using popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow
  • Applying ML to complex datasets with images and text
  • Deploying ML models to a production-ready environment

Course breakdown / modules

  • Machine learning
  • Machine learning process
  • Modeling and model validation

  • Car-price prediction project
  • Exploratory data analysis
  • Machine learning for regression
  • Predicting the price

  • Churn prediction project
  • Feature engineering
  • Machine learning for classification

  • Evaluation metrics
  • Confusion table
  • ROC curve and AUC score
  • Parameter tuning

  • Churn prediction model
  • Model serving
  • Managing dependencies
  • Deployment