Course Date
1st December 2023
Duration
40 hrs.
Delivery Format
Online Live
Why to Join This Course
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
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.
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FAQ for Advanced Machine Learning Engineering Course
Admission Process
Submit Application
Tell us a bit about yourself and why you want to do this program
Application Review
An admission panel will shortlist candidates based on their application
Enrolment
Selected candidates can join the program by paying the admission fee