This course will give an opportunity to gain expertise in one of the most fascinating and fastest growing areas of Computer Science that covers fascinating and compelling topics related to human intelligence and its applications in industry, defense, healthcare, agriculture and many other areas.

What is Machine Learning ?

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects.

These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.

Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, deep learning is actually a sub-field of machine learning, and neural networks is a sub-field of deep learning.

Bill Gates

Co-founder of Microsoft and Bill & Melinda Gates Foundation

“A Break Through In Machine Learning will be worth TEN Microsofts”

Duration: 12 MONTHS 

Schedule:

Weekdays – 2 Hrs / 3 days a week

Weekends – 3 Hrs / Sat & Sun

SEMESTER 1

Unit 1: Machine Learning
Unit 2: Foundations for ML
Unit 3: “R”
Unit 4: Python
Unit 5: Data Pre-Processing
Unit 6: Introduction Regression

SEMESTER 2

Unit 1: Classification
Unit 2: Clustering
Unit 3: Association
Case Studies

DELIVERY METHODOLOGY

  • LIVE ONLINE SESSIONS CONDUCTED BY TRAINER AS PER FIXED ACADEMIC CALENDAR
  • PRACTICAL SESSIONS IN LABS OR VIA REMOTE ACCESS
  • RECORDED VIDEO SESSIONS AVAILABLE
  • ASSIGNMENTS & EXERCISES
  • PROJECTS

     

Students who have successfully completed their course as per Terms and conditions will be eligible for the certification.

Certification in Cyber Security will be provided jointly by St. Xavier’s Technical Institute and L.A Bootcamps.

St. Xavier’s Technical Institute is affiliated to Directorate of Technical Education and approved by All India Council for Technical Education (AICTE) and autonomous institute.

www.xaviertech.com

ADMISSION CRITERIA

  • 12th Pass / Appearing Students
  • Diploma students (any stream) Studying or Passed out students
  • BE/BTech (Any stream) Studying or Passed out students
  • Graduate (any stream ) Studying or Passed out students
  • Working professionals in the IT & Tech sector

Video surveillance, traffic alerts on your smartphones, facial recognition software, personalized product recommendations as you browse a website — these are all examples of real-life machine learning applications.

This vitally important field, a subdiscipline of artificial intelligence, is attracting a lot of attention lately — both for its technological breakthroughs and lucrative career opportunities. Employment website Indeed.com has listed machine learning engineer as #1 among The Best Jobs in the U.S., citing a 344% rate of growth.

Career Paths in Machine Learning

  1. Machine Learning Engineer – Builds and manages platforms for machine learning projects
  2. Data Scientist -  Collects, analyzes and interprets complex sets of data by using machine learning and predictive analytics
  3. Natural Language Processing (NLP) Scientist - Works with computers to “understand, interpret and manipulate human language.” Draws from computer science and computational linguistics to bridge the gap between human communications and computer understanding
  4. Business Intelligence (BI) Developer -  Analyzes data sets for business and market trends
  5. Human-Centered Machine Learning Designer - Responsible for the “design, development and deployment of information systems that learn from and collaborate with humans in a deep, significant way.”
  6. Software Engineer - Researches, designs, implements and supports software solutions.+ Oversees the whole system and uses engineering concepts to develop software.
  7. Software Developer - Responsible for the entire software development process
  8. Computational Linguist - Teaches computers how to understand human language.

L.A Bootcamps will provide 100% Placement Assistance to all students who successfully complete the course and extend below complimentary placement services to the students:

  • Resume Building assistance
  • Career Mentoring with Industry Professionals
  • Interview Preparation sessions
  • Career Fairs
  • Internship opportunities for Freshers

Never settle for less

Enroll for the course today and pay later with 0% EMI options through dili