Machine Learning Free Course – Stanford University Course includes 61 hrs video content and enrolled by 4,764,681 students and received a 4.9 average review out of 5. It's a Free Course. Enroll it before the instructor makes it to the paid course.
What you will learn
- Introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed.
- Linear regression predicts a real-valued output based on an input value
- This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.
- How linear regression can be extended to accommodate multiple input features. Practices for implementing linear regression.
- Designed to help you understand how to implement the learning algorithms in practice
- Introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
- Introduce regularization, which helps prevent models from overfitting the training data.
- Neural Networks: Representation
- Introduce the backpropagation algorithm that is used to help learn parameters for a neural network.
- Advice for Applying Machine Learning
- How to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
- The idea and intuitions behind SVMs and discuss how to use it in practice.
- We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.
- Show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.
- How a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
- Recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
- How to apply the machine learning algorithms with large datasets.
- How to apply the machine learning algorithms with large datasets.
After completing each course in specialization and complete the hands-on project, you’ll earn a Certificate.
Course Rating : 4.9
Can I download Machine Learning Free Course – Stanford University course?
Course videos can be downloaded for offline viewing using the Udemy mobile app on Android and iOS. Some courses may allow downloading lectures on a computer only if the instructor has enabled it, but this is not guaranteed and is not the primary method Udemy supports.Can I get a certificate after completing the course?
Yes. After successful completion of the Machine Learning Free Course – Stanford University, learners will receive a certificate of completion. The certificate confirms that the course has been completed and can be downloaded and shared on resumes or professional profiles.Are there any other coupons available for this course?
Additional coupons for this course may be available from time to time. Coupon availability is time-limited and may change or expire at any time, so it’s recommended to check regularly for updated offers.Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.
