Advanced Machine Learning Specialization
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COURSE DATES AND LOCATIONS
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INTRODUCTION
In this course, you will begin to learn about machine learning through high level concepts through AWS SageMaker. You’ll start using SageMaker Studio to perform exploratory data analysis.
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COURSE OBJECTIVES
By the end of the course, you‘ll be able to:
- Perform fast machine learning calculations
- Pre-process data in machine learning
- Select different models and identify features in machine learning
- Perform cluster analysis
- Perform detection in machine learning
- Learn about SVMs for classification
- Learn about SVMs for regression
- Learn outlier detection in machine learning
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COURSE AUDIENCE
This course is made for:
- Beginners in data science and Machine Leaning
- Software Developers
- Python Developers
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COURSE OUTLINE
Day One
Introduction & Data Wrangling in Machine Learning
- Essential NumPy for Machine Learning
- Essential Pandas for Machine Learning
Day Two
Linear Models, Trees & Preprocessing in machine learning
- Linear Models for Regression & Classification
- Pre-Processing Techniques using Scikit
- Decision Trees
Day Three
Model Evaluation, Feature Selection & Pipelining in Machine Learning
- Model Selection & Evaluation
- Feature Selection Techniques
- Composite Estimators using Pipelines & Feature Unions
Day Four
Bayes, Nearest Neighbors & Clustering in Machine Learning
- Naive Bayes
- Nearest Neighbors
- Cluster Analysis
Day Five
Methods in Machine Learning
- Anomaly Detection
- Handling Imbalanced Classes
- Support Vector Machine
- Ensemble Methods
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