Concerning this Course
You will learn the following in the initial course of the Deep Learning Specialization:
- Create machine-learning models in Python using NumPy and sci-kit-learn, two popular machine-learning libraries.
- Construct and train machine learning supervised models for prediction as well as binary classification tasks, such as regression model and logistic regression.
DeepLearning.AI and Stanford Online collaborated to create the Machine Learning Specialization (ml training Course), a foundational online program. You will understand the fundamentals of deep learning and how to apply these techniques to build genuine Ai in this beginner-friendly program.
Andrew Ng, an Automation visionary who has helped lead critical research at Stanford University as well as ground-breaking work at Deepmind, Baidu, and Landing, teaches this Specialization. AI to advance the field of AI
This three-course Expertise is an updated and updated version of Andrew’s ground-breaking Machine Learning course, which has received 4.9 out of 5 stars and has been taken by over 37 million students since its inception in 2012.
It covers classification techniques, unsupervised learning, and some of the best practices used in Silicon Valley for machine learning and deep learning advancement.
Through the end of such a Specialty, you will have managed to master key concepts and acquired the necessary knowledge to use machine learning to reward actual issues quickly and powerfully. The newly launched Best Machine Learning Course is the ideal place to begin if you want to break in and out of Artificial Intelligence course or establish a career in computer science.
Using widely used machine learning libraries, create a machine learning algorithm in Python. Scikit-learn and NumPy
Create and train machine learning supervised models for prediction and binary classification tasks, such as regression models and logistic regression.
What you could learn in this course is outlined in the syllabus.
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Week 1: Machine Learning Overview
Welcome to the Specialization in Machine Learning! You’ll be connecting millions of other people who have taken this or the earliest course that led to the creation of Coursera and has helped a lot of people of other teaching and learning, like you, explore the amazing experience of machine learning!
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Week 2: Multiple input variable regression
This week, you’ll learn how to use generalized linear with multiple input features. You’ll also learn how to use vectorization, showcase scaling, feature engineering, and polynomial regression to improve the training and performance of your model. You’ll get to practice instituting generalized linear in code at the finish of the week.
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Classification in Week 3
Last week, you’ll learn about classification, the other sort of supervised learning. The logistic regression will be used to predict categories. You’ll learn about the problem of overfitting and how to deal with it using a regularisation technique. At the close of this week, you’ll get to practice incorporating logit with regularisation!
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Learning of Python and Benefits
Commonly Asked Questions
When will I be able to access the lectures and coursework?
The availability of lectures and assignments is determined by your enrolment type. Most involve documenting is free to view if you enroll in a class in audit mode. To gain access to graded coursework and earn a Certificate, you must consider buying the Certificate experience either during or after one’s audit. If you don’t see the compliance option, follow these steps:
There may be no audit option available for this course. Instead, you can try a Free Trial or apply for Student Finance. Instead, the course might very well offer ‘Entire Course, No Certificate.’ This option allows you to view all course materials, complete required assessments, and receive a final grade.
Deploying Machine Learning Model Using Flask
What will I receive if I join this Specialization?
When you enroll in the journey, you gain access to each of the coursework in the Expertise, and when you finish the assignment, you will receive a machine learning certification. Your electronic License will be decided to add to your Successes page, where you can print it or embed it in your LinkedIn profile. You can evaluate the class for free if visitors only want to interpret and view this same course content.
Is financial assistance available?
Yes. If you can’t afford the enrolment fee, you could apply for monetary aid or a fellowship in some learning programs. If financial aid or a fellowship is usable for your chosen learning program, a link to apply will be provided on the lexical features.
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