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Northeastern University
Machine Learning for Engineers: Algorithms and Applications
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  2. Computer Science
  3. Algorithms
Northeastern University

Machine Learning for Engineers: Algorithms and Applications

Qurat-ul-Ain Azim

Instructor: Qurat-ul-Ain Azim

Included with Coursera Plus

•Learn more
4 modules
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

4 modules
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
  • About
  • Modules
  • Recommendations
  • Testimonials

Skills you'll gain

  • Statistical Analysis
  • Predictive Modeling
  • Statistical Machine Learning
  • PyTorch (Machine Learning Library)
  • Machine Learning
  • Statistical Modeling
  • Unsupervised Learning
  • Statistical Methods
  • Applied Machine Learning
  • Deep Learning
  • Dimensionality Reduction
  • Complex Problem Solving
  • Artificial Intelligence and Machine Learning (AI/ML)
  • Regression Analysis
  • Machine Learning Algorithms
  • Supervised Learning
  • Algorithms

Details to know

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Assessments

7 assignments

Taught in English

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There are 4 modules in this course

This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods). The course will also discuss recent applications of machine learning, such as computer vision, data mining, natural language processing, speech recognition and robotics. Students will learn the implementation of selected machine learning algorithms via python and PyTorch.

This week provides an introduction to the field of statistical learning, exploring its scope and practical applications across various domains. Students will analyze how statistical learning techniques are used to make predictions, infer relationships, and uncover patterns in complex datasets. The module also offers a review of the key concepts essential for success in the course, including statistical models, data handling, and learning algorithms. By the end of the module, you will have a solid understanding of statistical learning principles and be prepared to apply them in real-world scenarios, laying the foundation for deeper exploration in machine learning and data science.

What's included

1 video7 readings1 assignment1 discussion prompt

1 video•Total 5 minutes
  • Statistical Learning Overview•5 minutes•Preview module
7 readings•Total 208 minutes
  • Course Overview•1 minute
  • Syllabus - Introduction to Service Innovation and Management•10 minutes
  • Academic Integrity•1 minute
  • Statistical Learning Overview•1 minute
  • Probability Tutorial•75 minutes
  • Calculus Tutorial•60 minutes
  • Linear Algebra Tutorial•60 minutes
1 assignment•Total 10 minutes
  • Check Your Knowledge: Statistical Learning Overview•10 minutes
1 discussion prompt•Total 10 minutes
  • Meet Your Fellow Learners•10 minutes

This week introduces you to the concept of Maximum Likelihood Estimation (MLE) and its application in statistical modeling. You will gain a thorough understanding of how to mathematically implement MLE and apply it to real-world datasets. The week will revisit foundational concepts of convex optimization, offering a solid foundation in optimization techniques. Additionally, the iterative process of the gradient descent algorithm will be explored, allowing you to understand and implement this method for finding optimal solutions in machine learning models. Through a combination of theoretical knowledge and practical application, you will build essential skills in statistical estimation and optimization, preparing for advanced studies in machine learning and data analysis.

What's included

2 videos3 readings2 assignments2 discussion prompts

2 videos•Total 12 minutes
  • Maximum Likelihood Estimation•5 minutes•Preview module
  • Gradient Descent•7 minutes
3 readings•Total 108 minutes
  • Maximum Likelihood Estimation•7 minutes
  • Convex Optimization•65 minutes
  • Gradient Descent•36 minutes
2 assignments•Total 13 minutes
  • Check Your Knowledge: Maximum Likelihood Estimation•5 minutes
  • Check Your Knowledge: Gradient Descent•8 minutes
2 discussion prompts•Total 90 minutes
  • Maximum Likelihood Estimation•45 minutes
  • Gradient Descent•45 minutes

In this module, you will gain a comprehensive understanding of supervised machine learning from model training to evaluation. You’ll interpret each step in the learning process and apply training and evaluation techniques to real-world data. This will enable you to fit and assess models, while addressing issues like overfitting and underfitting. By exploring the bias-variance trade-off, you can optimize models for greater accuracy and reliability. Cross-validation methods are also covered, equipping students with robust tools for model assessment and performance analysis. This week will combine theoretical insights preparing you for the advanced work in machine learning.

What's included

2 videos4 readings2 assignments

2 videos•Total 11 minutes
  • Components of a Learning Process•5 minutes•Preview module
  • Bias Variance Trade-Off•6 minutes
4 readings•Total 312 minutes
  • Components of a Learning Process•50 minutes
  • Overfitting vs Underfitting•7 minutes
  • Model Training and Evaluation•180 minutes
  • Bias Variance Trade-Off•75 minutes
2 assignments•Total 15 minutes
  • Check Your Knowledge: Components of a Learning Process•10 minutes
  • Check Your Knowledge: Bias Variance Trade-Off•5 minutes

This module, we will focus on the foundational principles of linear regression, a key technique in predictive modeling. You will learn to apply linear regression models and derive the ordinary least squares (OLS) formulation, gaining insight into how OLS is used to fit data accurately. We will also cover solution methods, including gradient descent and convex optimization, which provides a toolkit for efficient model training. You will explore regularization techniques to enhance model robustness and prevent overfitting. By implementing these regularized regression models in Python, you will gain hands-on experience in model optimization.

What's included

2 videos2 readings2 assignments1 discussion prompt

2 videos•Total 10 minutes
  • Linear Regression Overview•5 minutes•Preview module
  • Regularization for Linear Regression•5 minutes
2 readings•Total 181 minutes
  • Linear Regression Model Formulation•1 minute
  • Gradient Descent•180 minutes
2 assignments•Total 18 minutes
  • Check Your Knowledge: Linear Regression Model Formulation•10 minutes
  • Check Your Knowledge: Regularization•8 minutes
1 discussion prompt•Total 45 minutes
  • Linear Regression Model Formulation•45 minutes

Earn a career certificate

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Instructor

Qurat-ul-Ain Azim
Qurat-ul-Ain Azim
Northeastern University
2 Courses•459 learners

Offered by

Northeastern University

Offered by

Northeastern University

Founded in 1898, Northeastern is a global research university with a distinctive, experience-driven approach to education and discovery. The university is a leader in experiential learning, powered by the world’s most far-reaching cooperative education program. The spirit of collaboration guides a use-inspired research enterprise focused on solving global challenges in health, security, and sustainability.

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