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Machine Learning Course Description

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Course: Machine Learning Course
Duration: 90 Hours


  • • About Machine Learning Course
    • Installation of Anaconda
    • What is Machine Learning
    • Types of Machine Learning, Supervised Learning and Regression
    • Types of ML,Logistic Regression and Unsupervised Learning

  • • SVM -What is SVM and How do they work
    • SVM-Loading and Examining our dataset
    • SVM-Building and Tweaking our SVM Classification mode

  • • Building the Decision Tree : Decision Tree Learning
    • Building a Decision Tree – Information Gain a Gini Impurity
    • Decision Tree Lab:Building our First Decision Tree
    • Decision Tree Lab:Viewing and Tweaking our Decision Tree

  • • What is Overfitting
    • Random Forest Lab
    • Teamwork
    • Avoiding Overfitted Models

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