Machine Learning Free Online Course 2020
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Enroll in the Machine Learning Free Online Course for 2020, offered through Coursera, the world's leading online learning platform. This exceptional course, brought to you by Stanford University, is designed to empower students from all corners of the globe with the knowledge and skills needed to excel in machine learning. Don't miss out on the opportunity to also apply for USIP Free Online Courses 2020 with Free Certificates.
This machine learning course is taught by Andrew Ng, a renowned figure in the field. Dr. Ng is the Co-founder of Coursera and holds an Adjunct Professorship in Computer Science at Stanford University. Stanford University is a globally recognized institution, renowned for its exceptional teaching and research, consistently ranking among the world's top universities. You may also be interested in SDG Academy Free Online Courses 2020 from United Nations.
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Machine learning is a scientific field that focuses on enabling computers to learn and perform tasks without requiring explicit programming. Over the past decade, machine learning has revolutionized various industries, resulting in groundbreaking advancements such as self-driving cars, accurate speech recognition, efficient web search, and a deeper understanding of the human genome.
This course offers a comprehensive exploration of cutting-edge machine learning techniques, providing hands-on experience in implementing and applying them. You'll delve into the theoretical foundations of learning while gaining practical skills to effectively tackle new problems. Moreover, the course will expose you to the best practices employed by Silicon Valley in the realm of machine learning and artificial intelligence innovation.
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This course will teach you about:
Week 1: Introduction
Embark on a journey into the realm of Machine Learning! This module delves into the fundamental concept of enabling computers to acquire knowledge from data, eliminating the need for explicit programming. The Course Wiki is currently under development. For the most comprehensive and current information, kindly refer to the resources tab.
Week 2: Linear Regression with Multiple Variables
What if your input data contains multiple values? This module explores how linear regression can be adapted to handle scenarios with several input features. We will also delve into best practices for effectively implementing linear regression.
Week 3: Logistic Regression
Logistic regression is a statistical method that helps us categorize data into distinct groups. Imagine using it to determine if an email is spam or not. This module delves into the concept of classification, explores the cost function associated with logistic regression, and examines its application to classifying data into multiple categories.
Week 4: Neural Networks - Representation
Neural networks, inspired by the human brain's structure, are ubiquitous in modern applications. They are essential for tasks like voice recognition, enabling your phone to understand your spoken commands. Furthermore, the machines that automatically read digits on checks utilize neural networks for their functionality.
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Week 5: Neural Networks - Learning
This module delves into the backpropagation algorithm, a fundamental technique for training neural networks. By understanding its workings, you will be equipped to implement your own neural network capable of recognizing digits.
Week 6: Guidance on Implementing Machine Learning
The practical application of machine learning is not always a simple process. This module aims to provide guidance on best practices for implementing machine learning in real-world scenarios. We will also explore the most effective methods for evaluating the performance of the trained models.
Week 7: Support Vector Machines
Support vector machines, commonly known as SVMs, are a powerful machine learning algorithm designed for classification tasks. This guide delves into the fundamental concepts and intuitive understanding behind SVMs, providing practical insights into their implementation and application.
Week 8: Unsupervised Learning
Unsupervised learning techniques are employed to construct models that enhance our comprehension of data. This document delves into the k-Means clustering algorithm, a method that empowers us to discern groupings within unlabeled data points.
Week 9: Anomaly Detection
When faced with a substantial collection of data points, we might be interested in identifying those that deviate considerably from the average. This is particularly relevant in manufacturing, where we aim to pinpoint defects or irregularities. We'll explore how a dataset can be represented using a Gaussian distribution, and subsequently leverage this model to detect anomalies.
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Week 10: Large Scale Machine Learning
Machine learning algorithms thrive on extensive data, leveraging it for effective training. This module delves into the application of machine learning techniques in the context of large datasets.
Week 11: Application Example - Photo OCR
The process of identifying and recognizing objects, words, and digits within an image presents a significant challenge. This document explores the construction of a pipeline designed to address this problem, along with methods for analyzing and enhancing the system's performance.
Enrolling is simple! Just follow the link below. If you don't have an account, create one. Otherwise, log in to your existing account, enroll in the course, and begin learning.
Important Note: While this course is entirely free to access, you will need to pay if you want to receive a certificate upon completion. If you are unable to afford the certificate fee, you can apply for financial assistance through Coursera. Coursera offers financial aid to eligible learners facing financial hardship. To apply for financial aid, locate the "Financial Aid" link situated below the "Enroll" button on the left side of the page. You will be directed to complete an application form. You will receive notification of your approval status after submitting the application.
