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Machine learning (Course code: ALS08211116)

It is needless to say that a new age of technology and development is at the brink of establishment. Any individual well versed in contemporary technological development will agree that data interpretation and manipulation along with multidisciplinary integration is at the fore-front of this revolution. Machine learning is possibly the biggest technological domain under development for the forthcoming industrial revolution. In essence, machine learning is the science of giving power to the machines to take decisions on their own without being programmed.

It is a combination of computer science, data mining and statistical patterning techniques aiming to find the most efficient and smartest decision process. Machine learning incorporates a wide variety of data interpretation and statistical tools of supervised learning such as parametric/non parametric algorithms, neural networks, kernels etc. along with unsupervised learning such as clustering, dimensionality reduction, deep learning etc. Machine learning is possibly the first level of artificial intelligence that humans are currently developing.

Machine learning technologies and processes inherently allow machines to undergo data analysis through automated analytical model building. With the help of algorithms, machines learn from data iteratively allowing them to find solutions in gaps without being explicitly programmed to search in the gaps. Machine learning at small levels using algorithms have been around for a long period of time but its implementation on big data over and over, faster and faster has only been a development in the recent decade.

Machine learning has also paved way for more complex and deep data mining and Bayesian analysis techniques. The ever growing volume and variety of data demands computational processing that is cheap and more powerful along with affordable storage of data. Machine learning allows the user to quickly and automatically create models that can analyze more complex data and perform all these feats. This will directly lead to creation of system models that allow more profitable opportunities and avoidance of unknown risks.

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Course overview

The machine learning course takes the trainee from the beginning of theoretical fundamentals to complete application practices. Machine learning is a domain which is completely un-implementable until the individual is well versed in the core theoretical fundamentals. Thus, our course focuses on developing the fundamentals of machine learning which is an amalgamation of statistics and mathematical functions. This is available in both modes of online machine learning course and offline too as per the trainees' requirements.


Introduction

  • Introduction to machine learning and history
  • Fundamental mathematics and statistics introduction
  • Linear regression in one variable
  • Linear algebra revisited

More on mathematics and tools

  • Linear regression with multiple variables
  • Using MATLAB in the domain
  • Using OCTAVE in the domain

Data manipulation

  • logistic regression
  • Regularization
  • Support vector machines

Neural networks

  • introduction
  • Learning
  • Implementation

Learning

  • Unsupervised learning
  • Supervised learning
  • Dimensionality reduction
Live Instructor-led & Interactive Online Sessions


Regular Course

Duration : 40 Hours


Capsule Course

Duration : 4-8 Hours


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Training Options

OPTION 1

Weekdays- Cloud Based Training

Mon - Fri 07:00 AM - 09:00 AM(Mon, Wed, Fri)

Weekdays Online Lab

Mon - Fri 07:00 AM - 09:00 AM(Tue, Thur)


OPTION 2

Weekend- Cloud Based Training

Sat-Sun 09:00 AM - 11:00 AM (IST)

Weekend Online Lab

Sat-Sun 11:00 AM - 01:00 PM


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