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A self-study guide for aspiring machine learning practitioners. Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. Some of the questions answered are learn best practices from Google experts on key machine learning concepts, how does machine learning differ from traditional programming, what is loss, and how do I measure it? And so on.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

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Course Credit: Google

Course Curriculum

ML Concepts
Introduction to Machine Learning 00:03:00
Framing 00:02:00
Descending into ML 00:03:00
Reducing Loss 00:04:00
First Steps with TensorFlow 00:01:00
Generalization 00:05:00
Training and Test Sets 00:02:00
Validation 00:02:00
Representation 00:06:00
Feature Crosses 00:04:00
Regularization for Simplicity 00:04:00
Logistic Regression 00:04:00
Classification 00:07:00
Regularization for Sparsity 00:02:00
Introduction to Neural Networks 00:03:00
Training Neural Networks 00:03:00
Multi-Class Neural Networks 00:04:00
Embeddings 00:15:00
ML Engineering
Production ML Systems 00:01:00
ML Real World Examples
ML Systems in the Real World: Cancer Prediction 00:02:00
ML Systems in the Real World: Literature 00:03:00
ML Systems in the Real World 00:01:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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