Linux Academy – Machine Learning with Azure
English | Size: 1.33 GB
Category: CBTs
This course begins with explaining the need of Machine Learning and how it originated from Aritificial Intelligence and gave rise to deep learning. We explain important concepts in ML including categories of algorithms, statistical and computer science terms used in model creation, feature engineering, overfitting, generalization, underfitting and cross validation. We also dive into the topic of data science and discuss why ML is an important part of data science.
Machine learning is the ability for computer programs to perform specific tasks without being explicitly programmed to do so. These computer programs are backed by self-learning algorithms that rely on training data to learn and improve on accuracy incrementally. When it comes to training data, more is better. The larger the training data set, the more accurate the machine learning program at performing its tasks. Although machine learning has been around since the 1950s, when the first computer program to play the game of checkers was built, the recent “big data era” and the increased availability of tools have pushed the momentum of machine learning. Adoption of machine learning has accelerated where enterprise data is used to build predictive analytical functions that enable business-wide profitable decisions.
The Cloud and Machine Learning
Machine learning is a hot topic, but the industry is also faced with a shortage of skilled machine learning engineers. Most enterprises lack the technical expertise required for building machine learning solutions from scratch. Cloud giants have grabbed this opportunity to fill this void by offering ML APIs and infrastructure that simplify building ML applications. Cloud giants like Amazon, Microsoft, Google, and IBM not only have the financial capability to build out the infrastructure but also foster the best machine learning talent in the world. Besides, cloud services are all about large-scale processing of massive amounts of data. Data being the golden resource for the success of machine learning, cloud and ML is like a match made in heaven.
Linux Academy’s Big Data Offerings
Linux Academy is committed to the success of its students and after providing exemplary certification training in the area of Linux and AWS, we are expanding our offerings to other in-demand fields like machine learning and big data. Our newest launch in this area is the course on ‘Machine Learning with Azure’. As the trend on machine learning seems to be dominated by cloud services, we have chosen Azure to launch our training on this subject. Machine Learning with Azure not only provides an interactive, easy-to-use workspace for getting up to speed quickly for newbies but also offers packaged services like Text Analytics, Face Recognition, and Translator API in addition to common enterprise scenarios like customer churn prediction, recommendation engines, sentiment analysis and more. Azure excels in providing an ML service that caters to beginners, expert users, and even business users. Although Azure ML comes with well-documented services, it is easy to get overwhelmed with mountains of information, especially if you are a beginner. Linux Academy’s goal is to further simplify your learning process and offers a streamlined approach at getting started with this new tool.
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