Machine Learning

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Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method.


Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These add to the overall popularity of the language.

Did you know?

Machine learning involves a computer to be trained using a given data set, and use this training to predict the properties of a given new data.Then if we show the computer a new image, then from the above training, the computer should be able to tell whether this new image is a cat or not.

Why learn Machine Learning

Machine learning and artificial intelligence-based projects are obviously what the future holds. We want better personalization, smarter recommendations, and improved search functionality. Our apps can see, hear, and respond – that’s what artificial intelligence (AI) has brought, enhancing the user experience and creating value across many industries

Course Objective

Learn how to create custom lists and libraries in SharePoint. Customize the website to provide the feel of the corporate website. Create external lists using BDC.

How will I perform the practical sessions in Online training?

For online training, US GlobalSoft provides the virtual environment that helps in accessing each other’s system. The detailed pdf files, reference material, course code are provided to the trainee. Online sessions can be conducted through any of the available requirements like Skype, WebEx, GoToMeeting, Webinar, etc.

Machine Learning

Supervised Learning

  1. What is supervised learning
  2. Algorithms in Supervised learning
  3. Steps in Supervised learning

Regression & Classification

  1. Regressionvs classification
  2. Computation of co-relation coefficient and Analysis
  3. Performance and accuracy measurement of a Model
  4. NaiveBaye’s classifier, Model Training, Validation and Testing
  5. Ordinary Least squares, Variable selection
  6. R-Square coefficient and RMSE as a strength of model, Prediction and confidence interval determination and application
  7. Proviso of Regression, Dummy variables, Types of Regression: Linear and Logistic( Simple and multiple)
  8. Sum of least squares, ROC and AUC curves, Homoscedasticity and Heteroscedasticity, Multicollinearity and vif, Confusion matrix
  9. Techniques to improve accuracy and performance of regression models
  10. Assignment

Decision Trees and Random Forest Test

  1. Introduction to Decision tree Algorithms and it’s applications
  2. Classification and regression trees-CART models,ID3,C4.5, CHAID analysis
  3. Building Decision Trees using R, Decision nodes and leaf nodes
  4. Variable Selection, Parent and child nodes branching
  5. Stopping Criterion, Tree pruning, Depth of a tree, Overfitting
  6. Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
  7. Regression using decision tree
  8. Interpretation of a decision tree using If-else
  9. Pros and cons of a decision tree
  10. Introduction to Random forest test and it’s applications, Why Random forest test?
  11. Tree bagging, Models and algorithms in Random Forest test
  12. Training Data set, Tree grouping and decision making on majority voting
  13. Boosting algorithms-Gradient Boosting, Adaptive boosting-Adaboost , Xgboost ( Advanced)
  14. Accuracy estimation using cross validation

Prepare for Certification

Our training and certification program gives you a solid understanding of the key topics covered on the Oreilly’s Python Certification. In addition to boosting your income potential, getting certified in Python demonstrates your knowledge of the skills necessary to be a successful Python Developer. The certification validates your ability to produce reliable, high-quality results with increased efficiency and consistency.