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Master Applied Machine Learning

Acquire essential skills in Machine Learning, including data preprocessing, model training, evaluation, and deployment, enabling you to address real-world problems effectively.

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4.8/5
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4.8/5
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4.9/5
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Prerequisites and Eligibility

Basic knowledge of statistics and statistical analysis is beneficial

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What You Will Learn
1
Business Requirements for ML

Translate business requirements into software needs and document best practices for ML projects.

2
Build Machine Learning apps

Build Machine Learning apps across sectors such as finance, manufacturing, sports and medicine.

3
ML Model Interpretability

Master the techniques to explain Machine Learning models graphically and mathematically.

4
ML Pipelines & Optimization

Learn to model pipelines, data preparation, hyperparameter tuning, class imbalances and GPU acceleration.

5
ML Deployment & MLOps

Deploy Machine Learning models using frameworks like FastAPI and developing skills in MLOps.

6
ML Best Practices & Model Drift

Industry best practices for Machine Learning, including handling model drift and dependency management.

Why choose upGrad KnowledgeHut

The upGrad KnowledgeHut Edge

Superior Outcomes

Focus on skilled-based outcomes with advanced insights from our state-of-the art learning platform.

Immersive Learning

Go beyond just videos and learn with recall quizzes, interactive eBooks, case studies and more.

World-Class Instructors

Course instructors and designers from top businesses including Google, Amazon, Twitter and IBM.

Real-World Learning

Get an intimate, insider look at companies in the field through real-world case studies.

Industry-Vetted Curriculum

Curriculum primed for industry relevance and developed with guidance from industry advisory boards.

Continual Support

Learn better with support along the way. Get 24/7 help, stay unblocked and ramp up your skills.
Curriculum

1. Scoping Out Business Requirements

Learning Objectives:

Learn to identify business needs and translate them into software requirements, while documenting best practices and planning Machine Learning projects with automated data analysis.

Topics

  • What Do Businesses Need?
  • Translating Business Needs to Software Requirements and Software
  • Documenting Best Practices
  • Planning ML Projects
  • Automated Data Analysis

2. ML Projects in Different Sectors

Learning Objectives:

Explore Machine Learning applications in various sectors like banking, finance, customer segmentation, manufacturing, recommendation systems and sports, focusing on data exploration, preprocessing, and model building.

Topics

  • ML in Banking and Finance
  • ML in Customer Segmentation
  • ML in Manufacturing
  • ML in Recommendation Systems
  • ML in Sports
  • ML in Stock Market trading
  • ML in Medicine-Data Exploration
  • ML in Medicine - Preprocessing and Model Building

3. ML for Explainability

Learning Objectives:

Understand different techniques for explaining Machine Learning models, including graphical and mathematical explanations, and grasp the concept of local and global model explanations.

Topics

  • Graphical Explanations of supervised learning
  • Graphical Explanations of unsupervised learning
  • Mathematical Explainations of ML Models
  • Experimental Explainations of ML Models
  • Explaining Similarity in ML Models​
  • Local and Global Explainations of ML Models

4. Modeling Pipelines

Learning Objectives:

Gain knowledge of ML model and data preparation pipelines, and explore techniques for hyperparameter tuning, handling class imbalances, data augmentation, privacy considerations, and GPU acceleration.

Topics

  • ML Model Pipelines
  • Data preparation Pipelines
  • Hyperparameter Tuning-Data Preparation
  • Hyperparameter Tuning with Sklearn-Pipeline
  • Model Output Access
  • Class Imbalances
  • Data Augmentation - Overview
  • Privacy
  • Anonymizing data
  • GPU Acceleration

5. Deployment of ML Models and MLOps

Learning Objectives:

Understand the differences between local and deployed ML models, learn about the deployment framework FastAPI, and explore automated model predictions, performance logging, and MLOps concepts.

Topics

  • Local vs. Deployed Models
  • ML Deployment Framework: FastAPI
  • MLOps: Main Ideas
  • FastAPI as an Ops Framework
  • Automated Model Predictions
  • Automated Performance Logging

6. Best Practices in the Machine Learning Industry

Learning Objectives:

Learn industry best practices such as regular re-training, adjusting for model drift, dependency management, character encoding, data parsing, knowledge transfers, and documentation to ensure model efficacy over time.

Topics

  • Regular Re-Training
  • Adjusting for Model Drift
  • Dependency Management
  • Character Encoding
  • Data Parsing
  • Knowledge Transfers and Documentation
  • Re-assessing if a Model is Still Worthwhile
Frequently Asked Questions

FAQs

1. Will there be any other learning material other than self-paced videos?

Yes, you will experience KnowledgeHut's immersive learning in an on-demand format. This will include e-learning material to help you:

LEARN with recall quizzes, interactive eBooks, and case studies

ASSESS your skills progression with diagnostic, module-level, and final assessments

PRACTICE with real-world simulations and Cloud Labs

GAIN INSIGHTS with real-time reports and analytics on how you're progressing, your learning challenges, and suggestions of sections to revisit to build competency in the required areas.

2. Can I pursue this course along with my full-time job?

Yes, our online course is designed to give you flexibility to skill up as per your convenience. The course is delivered in a Self-Paced mode so that you can balance your work and learning as per your schedule.

3. Will I earn a certificate on completion of this online course?

Yes! Upon passing this online course, you will receive a signed certificate of completion from KnowledgeHut. Thousands of KnowledgeHut alumni use their course certificate to demonstrate skills to employers and their networks.

More than the certificate, however, you will get to showcase your newly acquired skills by working on real-world projects and adding these to your portfolio. KnowledgeHut’s online courses is well-regarded by industry experts, who contribute to our curriculum and use our tech programs to train their own teams.

4. Can I cancel my enrolment? Do I get a refund?

You can cancel your enrolment and receive refunds in line with our Cancellations and Refunds policy found at https://www.knowledgehut.com/refund-policy

Course Comparison

How Our Course Compares
YouTube VideosOnline CoursesupGrad KnowledgeHut
On-Demand Videos
Immersive Learning Experience
Structured Curriculum
Course Curated by Industry Experts
Auto-Graded Assessments
Lifetime Access to Courseware
Hands-On with Cloud Labs