Intro to Machine Learning with PyTorch

  • 0.0
3 months

Brief Introduction

Machine learning is changing countless industries, from health care to finance to market predictions. Currently, the demand for machine learning engineers far exceeds the supply. In this program, you’ll apply machine learning techniques to a variety of real-world tasks, such as customer segmentation and image classification. This program is designed to teach you foundational machine learning skills that data scientists and machine learning engineers use day-to-day.

Course Summary

Learn the fundamentals of machine learning with this comprehensive course from Udacity. Get hands-on experience with real-world projects and gain the skills you need to advance your career in the field of AI.

Key Learning Points

  • Learn the basics of machine learning and data analysis
  • Work on real-world projects to gain practical experience
  • Develop skills in Python, statistics, and machine learning algorithms

Job Positions & Salaries of people who have taken this course might have

    • USA: $112,000
    • India: ₹1,000,000
    • Spain: €35,000
    • USA: $112,000
    • India: ₹1,000,000
    • Spain: €35,000

    • USA: $117,000
    • India: ₹1,200,000
    • Spain: €40,000
    • USA: $112,000
    • India: ₹1,000,000
    • Spain: €35,000

    • USA: $117,000
    • India: ₹1,200,000
    • Spain: €40,000

    • USA: $140,000
    • India: ₹2,000,000
    • Spain: €50,000

Related Topics for further study


Learning Outcomes

  • Develop a strong foundation in machine learning
  • Gain practical experience through real-world projects
  • Acquire skills that will help advance your career in AI

Prerequisites or good to have knowledge before taking this course

  • Basic understanding of programming concepts
  • Familiarity with Python is helpful but not required

Course Difficulty Level

Intermediate

Course Format

  • Self-paced online course
  • Real-world projects
  • Personalized feedback from instructors

Similar Courses

  • Applied Data Science with Python
  • Machine Learning A-Z

Related Education Paths


Notable People in This Field

  • Andrew Ng
  • Fei-Fei Li

Related Books

Description

Build a solid foundation in Supervised, Unsupervised, and Deep Learning. Then, use these skills to test and deploy machine learning models in a production environment.

Knowledge

  • This program emphasizes practical coding skills that demonstrate your ability to apply machine learning techniques to a variety of business and research tasks. It is designed for people who are new to machine learning and want to build foundational skills in machine learning algorithms and techniques to either advance within their current field or position themselves to learn more advanced skills for a career transition.

Outline

  • Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects. Learn foundational machine learning techniques - from data manipulation to unsupervised and supervised algorithms in PyTorch and scikit-learn.

Summary of User Reviews

The Intro to Machine Learning Nanodegree is highly recommended by users for its comprehensive and practical curriculum. Many users praised the course's emphasis on real-world applications, making it a great choice for those wanting to apply machine learning techniques in their profession.

Key Aspect Users Liked About This Course

Emphasis on practical applications

Pros from User Reviews

  • Comprehensive curriculum covering a range of machine learning techniques
  • Real-world projects provide hands-on experience with practical applications
  • Engaging content and knowledgeable instructors
  • Flexible pacing and personalized support for students

Cons from User Reviews

  • Some users found the course challenging and struggled with the pace
  • Occasional technical issues with the online platform
  • Limited opportunities for interaction with peers
Available now
3 months
Cezanne Camacho, Mat Leonard, Luis Serrano, Dan Romuald Mbanga, Jennifer Staab, Sean Carrell, Josh Bernhard, Jay Alammar, Andrew Paster
Kaggle, AWS
Udacity

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