Machine Learning

  • 08 / Dec / 2019
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Course Descriptions

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Certifications

The Professional Certificate Program in Machine Learning and Artificial Intelligence is designed for: Professionals with at fresher student who hold a bachelor's degree (at a minimum) in a technical area such as computer science, statistics, physics, or electrical engineering. ISO certified certificate will be provided by institute after course completion

Syllabus

  • Topic 01Python Programming
  • Topic 02Python IDE
  • Topic 03Anaconda Package
  • Topic 04Machine Learning Basics
  • Topic 05DataSets
  • Topic 06Independent & Dependent Variable
  • Topic 07Introduction to Pandas
  • Topic 08Reading DataSets
  • Topic 09Dataframes & Series
  • Topic 10Accessing rows & columns
  • Topic 11Operations On Nan values
  • Topic 12Creatinfg Pivots
  • Topic 13Introduction to Numpy
  • Topic 14Introduction tp matplotlib.pyplot
  • Topic 15Data Preprocessing Intuition
  • Topic 16Importing Libraries
  • Topic 17Importing Datasets
  • Topic 18Handling Missing Data
  • Topic 19Categorical Data
  • Topic 20Training & Testing Data
  • Topic 21Feature Scaling
  • Topic 22Simple Linear Regression
  • Topic 23Multi Linear Regression
  • Topic 24Polynomial Regression
  • Topic 25Support Vector Regression
  • Topic 26Decision Tree Regression
  • Topic 27Random Forest Regression
  • Topic 28Logistics Regression
  • Topic 29Classification using KNN
  • Topic 30Classification using SVM
  • Topic 31KMeans Clustering
  • Topic 32Hirarchical Clustering
  • Topic 33Association Rule Mining using Apriori
  • Topic 34Association Rule Mining using Eclat
  • Topic 35Upper Confidence Bound
  • Topic 36Thompson Samplig

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