Advanced Machine Learning Engineering

The Advanced Machine Learning Engineering course is a comprehensive 40-hour program designed to provide students with the necessary skills and knowledge to excel in the field of machine learning engineering. This course delves into the principles and application of supervised and unsupervised learning algorithms, model evaluation and selection, feature engineering and selection, and model deployment and scalability. Through practical exercises and real-world examples, students will gain hands-on experience in developing and deploying machine learning models, selecting appropriate features, evaluating model performance, and ensuring scalability. By completing this course, students will be well-prepared to pursue a career as a Machine Learning Engineer and contribute to the development of intelligent systems. 

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Course Date

1st December 2023

Duration

40 hrs.

Delivery Format

Online Live

Why to Join This Course

Earn a program completion certificate from the prestigious E&ICT Academy, IIT Kanpur.
Utilize Ikigai’s Job Assist feature to enhance your visibility to leading hiring companies.
Attend masterclasses conducted by renowned faculty members from IIT Kanpur.
Engage in hands-on projects tailored to different industry sectors.

Join Our Advanced Machine Learning Engineering Today!

Ready to embark on a transformative learning journey? Don’t miss this opportunity to gain valuable skills and elevate your career. Enroll in our comprehensive course today and join a community of learners dedicated to excellence. Take the first step towards unlocking your full potential!

Course Price

₹19,900.00

Key Features

Earn a program completion certificate.
Utilize Ikigai’s Job Assist feature to enhance your visibility to leading hiring companies.
Attend masterclasses conducted by renowned faculty members from IIT Kanpur.
Top-notch curriculum with integrated labs
Engage in hands-on projects tailored to different industry sectors.
Explore and apply practical tools and frameworks that can significantly enhance your work.
Enjoy smooth access to integrated labs for a seamless learning experience
Conclude the program with capstone projects spanning three distinct domains

Curriculum for Advanced Machine Learning Engineering

Module 1: Supervised and Unsupervised Learning Algorithms (10 hours) 

  • Introduction to supervised and unsupervised learning 
  • Decision trees and ensemble methods 
  • Support Vector Machines (SVM) 
  • Clustering algorithms (e.g., K-means, DBSCAN) 
  • Dimensionality reduction techniques (e.g., PCA, t-SNE) 

Module 2: Model Evaluation and Selection (10 hours) 

  • Evaluation metrics for classification and regression models 
  • Cross-validation and model validation techniques 
  • Bias-Variance trade-off and overfitting 
  • Hyperparameter tuning and model selection 
  • Ensembling and stacking methods 

Module 3: Feature Engineering and Selection (10 hours) 

  • Feature extraction and transformation techniques 
  • Handling missing values and outliers 
  • Feature scaling and normalization 
  • Dimensionality reduction methods 
  • Feature selection algorithms (e.g., Lasso, Recursive Feature Elimination) 

Module 4: Model Deployment and Scalability (10 hours) 

  • Model deployment strategies and considerations 
  • Containerization and cloud deployment platforms 
  • Scalable model architectures (e.g., distributed computing, GPU acceleration) 
  • Model monitoring and performance optimization 
  • Continuous integration and deployment (CI/CD) pipeline 

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Eligibility Criteria

For admission to this Professional Certificate course in Advanced Machine Learning Engineering Course, candidates should have: 

  • Candidates must possess at least a bachelor’s degree from a recognized institution. 
  • Basic programming skills are preferred, though not mandatory. 
  • A foundational understanding of mathematics will be advantageous. 
  • Access to a computer with an internet connection and required software tools is essential. 
  • Prior work experience is not required for enrollment in this course. 

Course Outcomes

Upon completing the course, students will: 

  • Demonstrate proficiency in supervised and unsupervised learning algorithms. 
  • Apply various evaluation metrics and techniques for model selection effectively. 
  • Employ feature engineering and selection methods to improve model performance. 
  • Understand model deployment strategies and scalability considerations. 
  • Gain practical experience in ensembling and stacking for better predictive accuracy. 

FAQ for Advanced Machine Learning Engineering Course

For admission to this Advanced Machine Learning Engineering Course, candidates should have: 

  • A bachelor’s degree with an average of 50 percent or higher marks 
  • Prior work experience is not mandatory 
  • Can be from a programming or non-programming background 

The admission process for this Advanced Machine Learning Engineering Course consists of three simple steps: 

  •  All interested candidates are required to apply through the online application form 
  • An admission panel will shortlist the candidates based on their application 
  • An offer of admission will be made to the selected candidates, which can then be accepted by the candidate by paying the program fee. 

As a part of this Advanced Machine Learning Engineering Course, you will receive the following: 

  •  Masterclasses delivered by distinguished IIT Kanpur faculty 
  • Program completion certificate from E&ICT Academy, IIT Kanpur 
  • Ikigai Career Assistance post-completion of this program 

Upon successful completion of this Advanced Machine Learning Engineering Course, you will be awarded a certificate of completion by E&ICT Academy, IIT Kanpur, and industry-recognized certification from Ikigai for courses in the learning path. 

This Data Science Course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device

We offer 24/7 support through email, chat, and calls. We have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after the completion of your Deep Learning and Generative AI course. 

Contact us using the form on the right side of any page on the Ikigai website, select the Live Chat link, or contact Help & Support. 

Admission Process

The application process consists of three simple steps. An offer of admission will be made to the selected candidates and accepted by the candidates by paying the admission fee.
1

Submit Application 

Tell us a bit about yourself and why you want to do this program 

2

Application Review 

An admission panel will shortlist candidates based on their application 

3

Enrolment

Selected candidates can join the program by paying the admission fee 

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