Machine Learning Engineer Career Track

Sign up to our 4-month Instructor-led Core Data Science career track

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This is where Data Science meets Engineering. The ML Engineer Career track's primary focus is to equip aspiring data professionals to design and develop scalable data science solutions. In this course you'll learn the fundamentals of data science, understand data science lifecycle, refresh your machine learning skills, learn about data science solution deployment strategies and MLOps, model deployment via Azure Machine Learning, introduction to model retraining and pipelines etc.

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Job Facts

Machine learning engineers are responsible for taking experimental data science models and helping scale them out to production-level models. The skill set required is quite niche. A typical ML engineer is expected to have knowledge on API Development, Data Engineering, Data Science, DevOps etc. However, finding such a mix is often a challenge hence, ML engineers are also equally sought after in the market.

According to popular job sites, an ML Engineer could earn on an average of $114K per year which is very close to that of a beginner Data Scientist

What makes this course unique?

This is the first ML Engineer Career Track with a job guarantee. With support from our career services team, we will help you find a job upon the completion of the course. But if you don’t, we’ll refund your tuition

Who is this course for?

ML Engineer Career Track is designed to help an aspirant with some prior experience in either application development, data engineering or Machine learning to transition into this niche role that aims at operationalizing, deploying and scaling ML solutions into production environments.

To sign up for this course you are expected to have prior knowledge in the following

  1) Experience with Python

            a) Prior working experience with Lists, Tuples and Dictionaries

            b) Beginner to Intermediate knowledge on data wrangling using Pandas and Numpy

What will you learn?

This course will guide you towards an ML Engineer role in the industry. You will learn how to build and deploy ML solutions, MLOps, learn to leverage cloud platforms to deploy and scale ML models. Understand various architectures and build production grade solutions

Skills Gained

Refresh your knowledge on Python programming and advanced python concepts such as OOP, Exception handling, building python packages etc.

Refresh your knowledge on key ML concepts such as Supervised and Unsupervised learning

Learn how to deploy a simple ML Model using Flask

Deploying models using Azure Machine Learning

Hands on experience via Projects and Assignments

This specialization have 8 courses content

Module 1

Introduction to Data Science

Description: This is an introductory module which is focused towards providing a brief overview on Data Science, Technology trends, Industry Trends, Glossary, Data Science Lifecycle etc.

Skills gained

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  • • What are Analytics and Data Science?

Module 2

Python Programming for Data Science

Description: This module focusses on refreshing basic concepts of python programming and introducing advanced concepts such as OOP, Exception handling, Module building etc.

Skills gained

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  • • Environment Setup and IDE’s in python

Module 3

Supervised Learning

Description: Learn about supervised learning techniques such as regression and classification algorithms.

Skills gained

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  • • Linear Regression

Module 4

Unsupervised Learning

Description: Unsupervised learning unlike its counterpart, is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels.

Skills gained

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  • • Clustering

Module 5

Text Mining and NLP

Description: this module focusses on extracting meaningful insight from unstructured data such as text

Skills gained

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  • • Text Preprocessing – Stemming, Lemmatization, Word Clouds etc.

Module 6

Introduction to Neural Networks and Deep Learning

Description: Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns.

Skills gained

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  • • Artificial Neural Networks

Module 7

Introduction to Data Engineering and Big Data

Description: This module is focused towards building the breadth with data science.

Skills gained

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  • • Basics of SQL- CRUD Operations, Aggregations etc.

Module 8

Deploying ML Models: Cloud Platforms

Description: While the previous module provides an insight into how models can be converted into REST Endpoints, this module focuses on performing the same with cloud platforms

Skills gained

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  • • Introduction to Azure Machine Learning

Why Us?

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We also provide features like :-
  • Lifetime content access to the LMS
  • 24/7 coding support
  • Placement assistance
  • Live projects

Limited students per batch !!

Apply now and click here to send us your elevator pitch on why to pursue this course?

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