Kickstart your journey with a FREE Stanford Machine Learning course!!!!
Currently, Machine Learning is the hottest technology across the world which everyone wants to acknowledge because it’s a future.
According to Stack Overflow, a Machine Learning specialist is the top 5 paying technologies almost $63,216 per year that’s INSANE. Nowadays Machine Learning is used in every technology we can say that we all are covered with so many technologies.
It has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
As we have discussed all the importance and how it will be the future but how to learn them? Basically, It’s a very obvious question from a beginner from where I should start learning M. L.
If you want to learn Machine Learning but don’t have any knowledge regarding ML and don’t know which resources are best?
Then Stanford University is providing a FREE Stanford machine learning course on machine learning. In this blog, we will discuss the course overview, syllabus, duration, paid certificate available, and many more things.
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Stanford Machine Learning Overview
Basically, In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
More importantly, In the Stanford machine learning course, you’ll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.
This Stanford machine learning course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include:
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The Stanford machine learning course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Stanford Machine Learning Syllabus(Ist part)
- To begin with, they introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed.
Linear Regression with One Variable
- It predicts a real-valued output based on an input value. Infact they will discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.
Linear Algebra Review
- As a newbie basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.
Linear Regression with Multiple Variables
- Basically, In this module, they show how linear regression can be extended to accommodate multiple input features. Last they also discuss best practices for implementing linear regression.
- Furthermore, the course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.
- Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, they introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
- Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, they introduce regularization, which helps prevent models from overfitting the training data.
Stanford Machine Learning Syllabus(IInd part)
Neural Networks: Representation
- It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
Neural Networks: Learning
- In this module, they introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.
Advice for Applying Machine Learning
- Applying machine learning in practice is not always straightforward. In this module, they share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.
Machine Learning System Design
- In this module, they discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
Support Vector Machines
- Support vector machines, or SVMs, is a machine learning algorithm for classification. They introduce the idea and intuitions behind SVMs and discuss how to use it in practice.
- We use unsupervised learning to build models that help us understand our data better. They discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.
Stanford Machine Learning Syllabus(IIIrd Part)
- In this module, They introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.
- Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. They show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
- When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, they introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
Large Scale Machine Learning
- Machine learning works best when there is an abundance of data to leverage for training. In this module, they discuss how to apply machine learning algorithms with large datasets.
Application Example: Photo OCR
- Identifying and recognizing objects, words, and digits in an image is a challenging task. Lastly, they discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.
Basically, If you want to kickstart your M. L. journey then this is one of the best courses from where you can start. It’s the best course for beginners to understand the theoretical underpinnings but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
I hope this course will help you. If you find it helpful then please share it with your friend and colleagues and show some support. Let me know if you have any doubts please comment down.
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