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Advanced Machine Learning

Course Summary

The Advanced Machine Learning training course builds upon Introduction to Machine Learning in Python to further the student's understanding of Machine Learning (ML).

The course begins by evaluating advanced techniques for managing data. Next, students will evaluate and tune models and use ensemble methods. The course concludes by examining time-series data.

This course is meant for students that have taken an introductory level ML course or possess the requisite knowledge.

Purpose
 Learn advanced techniques for managing data and tuning models.
Audience
 Developers and developer teams looking to learn advanced ML techniques.
Role
Data Engineer - Data Scientist - Software Developer
Skill Level
Advanced
Style
Fast Track - Targeted Topic - Workshops
Duration
2 Days
Related Technologies
Python

 

Productivity Objectives
  • Identify how to deal with dirty data, outliers, and time-series data.
  • Evaluate, optimize, and tune your models.
  • Use ensemble methods such as Random Forests, Bagging, and Boosting, as well as create your own.
  • Gain additional experience with "productizing" ML models.

What You'll Learn:

In the Advanced Machine Learning training course, you'll learn:
  • Brief Review of ML Concepts
  • Advanced Feature Engineering
    • Dealing with Dirty Data
    • Feature Value Imputation
    • Engineered Features
    • Denoising With ML Models
  • Optimizing and Training Linear Models
    • Understanding Gradient Descent Optimization
    • L1 and L2 / Elasticnet Optimization
    • Support Vector Machines
    • k-Folds Cross Validation
  • Evaluating Models
    • Metrics for Model Evaluation
    • How to Optimize and Tune Hyperparameters
    • Adjusting Thresholds to Tune for Positive or Negative Performance
    • Adjusting for Class Imbalance
    • Advanced Metrics for Model Evaluation
  • Handling Outliers
    • Dealing with Outliers
    • Detecting Outliers C
    • Partitioning Models to Handle Outliers
  • Recommendations
    • Exploring Similarity Metrics
    • User-based vs. Item-based Recommendations
    • Evaluating Recommendation Results
    • Combining Multiple Models for Recommendations
  • Ensemble Methods
    • Using Multiple Models Together
    • Random Forests and GBTs
    • Understanding Bootstrap Aggregation
    • Combining Heterogenous Models
    • Evaluating Ensembles of Methods
  • Time Series Data
    • Inferring State from Time-Series Data
    • Sequence Prediction
    • ARIMA
    • Using RNNs for Time-Series Data
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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