Course Description

Improve business outcomes with ML & AI

 

With the adoption of Machine Learning you will develop robust models for analyzing bigger and more complex data and identify opportunities and potential risks. This course provides a foundation of the two largest areas in machine learning: supervised and unsupervised learning. 

You will learn how machine learning techniques are applied to business problems, as well as how to implement these techniques using popular Python libraries. In addition, you will gain experience in extracting useful information and identify patterns in the data.

 

Course Outcomes

  • Understand the fundamentals concepts of machine learning and its challenges

  • Know of the strengths and weaknesses of many popular machine learning approaches

  • Design and implement various machine learning algorithms for different types of problems

Training Content

  • Introduction to Machine Learning
  • Linear Regression
  • Polynomial Regression
  • Overfitting vs Underfitting
  • Cross Validation
  • Loss functions
  • Gradient decent
  • Lasso(L1) and Ridge (L2) regularization
  • Logistic regression
  • KNN
  • SVM
  • Naive Bayes
  • Bagging
  • Decision trees
  • Random forest
  • Boosting
  • Hyperparameter tuning

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