Machine Learning Specialist

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The Machine Learning Specialist certification is for the candidates, who wants to learn algorithm coding and formula and other aspects of the data and analytics.  This Machine Learning Courses are the concoction of Data Science with R, Introduction to Machine Learning, Random Forest, General Boosting & Bagging, Support Vector Machines, Neural Networks  and Text Mining with R. The training insights the candidates on the syntax, variables, and types, create functions and use control flow, work with data in R. Moreover, they would be able to gain insight on regression, clustering, classification, including measuring the variable importance through permutation and gaining hands-on experience on solving the algorithm with the complexity of a classifier to gain accuracy.

After the training the candidates would be able to:

  • Develop an understanding of categorical variables and continuous variables, that helps in using the boosting and bagging methods effectively, understanding the NN algorithms work.
  • Understand kernel functions such as: spline kernels, linear, radial basis function and polynomial and Text Mining with R are based on the intelligence of statistics and R.
  • Explore R language fundamentals, including basic syntax, variables, and types
  • Why Support Vector Machines  is called the most high-performing algorithm
  • How neural networks effective in image segmentation
  • How to use the calculus in simpler form
Target audience
  • The IT professionals, who are willing to pursue their career as a Machine Learning Specialist.
Prerequisites

The candidates should have knowledge of the basics of programming, SQL and math and statistic concepts.

1. Data Science with R

  • Exploratory Data Analysis and Visualization
  • R for Data Science
  • Data Mining
  • Data Analysis for Evidence Based Decision Making
  • Industry Applications of Advanced Analytics Models
  • Big Data Analytics with Spark
  • Project Management in Analytics
  • Information to Insight
  • Career Management

2. Introduction to Machine Learning

  • An Introduction
  • The Regression Algorithms
  • The Classifiers: Bayesian and kNN
  • Tree Based Algorithms
  • SVM and Improving Performance

3. Random Forest

  • Single Decision Tree
  • Rise of Ensemble Method
  • Practical Exercises in R on Business Case Studies               

4. General Boosting & Bagging

  • Decision Tree Ensembles: Bagging and Boosting
  • The Case Study: Analysis of Credit Data
  • The Case Study: The Titanic Accident
  • The Case Study: Comparing Algorithms

5. Support Vector Machines

  • Introduction to the Support Vector Machines

6. Neural Networks

  • An Introduction
  • The Perceptron learning procedure
  • The backpropagation learning procedure
  • Learning feature vectors for words
  • Object recognition with neural nets
  • Optimization: How to make the learning go faster
  • Recurrent neural networks
  • More recurrent neural networks
  • Ways to make neural networks generalize better
  • Combining multiple neural networks to improve generalization
  • Hopfield nets and Boltzmann machines
  • Restricted Boltzmann machines (RBMs)
  • Stacking RBMs to make Deep Belief Nets
  • Deep neural nets with generative pre-training
  • Modeling hierarchical structure with neural nets
  • Recent applications of deep neural nets

7. Text Mining with R

  • An Introduction to the Text Mining
  • TM Packages in R
  • Regular Expressions
  • Sentiment Analysis
  • Topic Modelling
  • Network Analysis
  • Clustering

Note: to know about the detailed information about the course modules please feel free to write us or give us a buzz.

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