machine learning training in vizag

About Machine Learning Foundation

Machine Learning (ML) is a different approach where computer learns the rules of solving complex problems without explicity programmed. Machine Learning algorithms are at the core and important piece of data science.

This course – Machine Learning Foundation, is designed to provide a wholistic understanding of various ML algorithms with high level theory and hands on application of ML algorithms to classis data sets.

Machine Learning Foundation – Course Objectives

  • Introduce Machine Learning with wholistic approach.
  • Discuss high level theory of popular Machine Learning Algorithms
  • Hands on coding of popular ML algorithms on classic data sets
  • Access the knowledge through International Association of Business Analytics (IABAC™) framework.

Course Prerequisites

  • Introduce Machine Learning with wholistic approach.
  • Discuss high level theory of popular Machine Learning Algorithms
  • Hands on coding of popular ML algorithms on classic data sets
  • Access the knowledge through International Association of Business Analytics (IABAC™) framework.

Why Machine Learning Foundation?

In recent years, Machine Learning has taken over a main stream business and evolved has a career track by itself. A quick search in job portals reveals about 20,000 Machine Learning job opportunities on daily basis in USA alone. This course lays a solid foundation for ML aspirants with high level theory and concept along with hands on coding of popuplar Machine Learning algorithms : Linear and Logistic Regression, K-means clusturing, SVM (Support Vector Machines), KNN (K -Nearest Neighbours) and Neural Networks.

Who should choose this course – Machine Learning Foundation?

  • Fresh colleague graduates, who are looking to career options in Data Science
  • Senior professionals, who want gain solid foundation on Machine Learning to manager Data Science projects
  • Candidates pursuing Data Scientist tracks

Advantages of Machine Learning Foundation

This course provides a solid foundation in Machine Learning as the syllabus is aligned with international market requirements.  As a part of the course, an certification assessment is conducted and candidate achieving minimum qualifing score receive a global certification, carring immense value of the testimony of their ML knowledge.

Syllabus : 

Introduction to Machine Learning

  • What is Machine Learning(ML)?
  • Data preprocessing for ML algorithms
  • Creating your first Prediction Model
  • Types of ML algorithms : Supervised and Unsupervised.
  • Basics of Classification , Regression and Clustering algorithms

Machine Learning Algorithms

  • Supervised Machine Learning algorithms
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Logistic Regression
  • Classification Trees
  • Unsupervised Machine Learning algorithms
  • Clustering with K-means
  • Hierarchial Clustering

Data Processing for Machine Learning

  • Data Mugging
  • Outlier Analysis
  • Treating for missing values
  • Normalization vs Standardization of data

Building and Training Machine Learning models

  • Choosing Machine Learning algorithm
  • Coding ML with Python Scikit-Learn package
  • Training and Model evaluation

Hands on case study: Property Estimations with ML

  • House value estimation – Case study.