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.
Assessment
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.
Certification
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.
Having an Official Edukite Certification is a great way to celebrate and share your success. You can:
- Add the certificate to your CV or resume and brighten up your career
- Show it to prove your success
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 | ||
Assessment | |||
Submit Your Assignment | 00:00:00 | ||
Certification | 00:00:00 |
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