Course Curriculum

Curriculum

  • Introduction of Artificial Intelligence, Machine Learning and Data Mining.
  • Brief introduction to Machine Learning for AI.
  • Classification of Machine Learning.
  • Difference between Machine Learning and Artificial Intelligence.
  • Machine Learning and Data Mining Techniques.
  • Types of Learning.
  • Machine Learning System Design.
  • Supervised Learning- Regression Classification.
  • Future scope of Machine Learning and Artificial Intelligence.

  • Naive Bayes Classification
  • Back-propagation
  • Logistic Regression
  • Support Vector Machines (SVM)
  • Random Forest
  • Decision Tree
  • k-Nearest Neighbors (KNN)
  • K-Means Clustering

  • Introduction to python and anaconda
  • Conditional Statements
  • Looping, Control Statements
  • Lists, Tuple ,Dictionaries
  • String Manipulation
  • Functions
  • Installing Packages
  • Introduction of Various Tool
  • Introduction of Anaconda
  • Working on spyder ,Jupyter notebook

  • Installing library and packages for machine
  • learning and data science
  • Matplotlib
  • Scipy and Numpy
  • Pandas
  • IPython toolkit
  • scikit-learn

  • Installing Opencv library
  • Introduction of Opencv and its function
  • Reading and Writing Image and Video
  • Creating Different Shape
  • Introduction of Haar-Casecade Classifier
  • Working with images and videos

Projects

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