ML 101 - Intro To Machine Learning

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course overview

This course will take a practical approach to learning machine learning. We'll cover the basics of why things are happening (like why and how the algorithms are able to adapt to (learn from) the data), but we'll spend the majority of the time applying time-tested machine learning libraries and methods to real data. We'll then analyze the results and demonstrate how to calculate the accuracy of the methods applied.

This course is intended for those people interested in learning how to deploy time tested machine learning methodologies on data in their organizations. It is a hands on course requiring a development environment setup for Python coding. The course assumes no prior knowledge in machine learning, but it does require some familiarity with Python programming.

intended audience

  • Data engineers
  • Database specialists
  • Software developers
  • Devops personnel
  • Scientists and researchers

customizable highlights

Linear Regression

  • Of a single variable
  • Multivariate

Logistic regression

  • Intro to classification
  • Classification by logistic regression


  • A single decision tree; hardcoded and machine learning
  • A forest of trees: random forest classifiers

Unsupervised learning

  • Kmeans
  • PCA

Brief intro to Neural Networks

  • NNs using PyTorch