Artificial Intelligence (AI) and Machine Learning (ML) are propelling advanced business solutions across industries. Algorithm-based [machine learning development...
Artificial Intelligence (AI) and Machine Learning (ML) are propelling advanced business solutions across industries. Algorithm-based machine learning development services are overcoming the business challenges of processing heavy data volumes. With accuracy and efficiency, cloud technologies like Google cloud AutoML are encouraging the development of dynamic machine learning solutions. At Oodles, we are testing the model performance of AutoML Natural Language to build domain-specific solutions for global businesses. Let’s explore how cloud AutoML for machine learning solutions is triggering automation beyond human intelligence.
How does Google AutoML Natural Language work?
AutoML Natural Language runs on large volumes of data and algorithms. The cloud service enables businesses to perform supervised machine learning by training a system to identify patterns from labeled data. With AutoML Natural Language service, businesses can easily build custom and cloud-based machine learning solutions for specific tasks like content recognition from textual data. On a deeper level, machine learning in AutoML Natural Language involves three major steps-
The first step to build a domain-specific model is to organize data in the form of inputs and answers. While inputs include labeled examples of certain text that a business wants to classify, answers include categories to be predicted by the ML model. A bare minimum of 1000 examples is essential to train the model efficiently.
After training AutoML Natural Language with rich and goal-oriented data, it is time to evaluate its performance. The main idea is to assess what model’s output on test examples with techniques such as score threshold, true positives and negatives, precision, and recall.
AutoML uses Rest API to generate predictions by analyzing a wide array of data structures over the cloud.
AutoML Natural Language Use Cases
a) AutoML Text Classification
b) Sentiment Analysis
c) Entity Extraction
Sorting and processing an explosion of unstructured textual information is a major challenge for digital businesses. With AutoML, businesses can categorize complex digital content such as blogs, articles, news stories, social media posts, etc. AutoML Natural Language supports both native and scanned PDFs in the English language with a training capacity of up to 1 million documents. From eCommerce to healthcare, businesses can use AutoML’s text classification in the following ways-
a) Classify and translate real-time eCommerce customer query data into definite product names to optimize the supply chain.
b) Gauge the sentiment and emotions of users across social media tweets to track and evaluate user feedback on certain business services.
c) Categorizing candidate qualifications from resumes and CVs to save manual labor in sorting and classifying candidate capabilities.
Difference between Google AutoML Natural Language and Natural Language API
Google Cloud AutoML is a gateway for businesses seeking to build personalized machine learning solutions than the standard Natural Language API. Customization is the key differentiator between Google AutoML Natural Language and standard Natural Language API. While Natural Language API proves effective for chatbot development services, AutoML is more essential for driving insights and predictions from textual and user data.
Deploying Cloud AutoML for Machine Learning Solutions with Oodles AI
As we leap into the new year 2020, big data analytics powered by machine learning technologies are poised to generate greater value for businesses. We, at Oodles, are combining our forces with emerging cloud-based technologies to develop business-oriented machine learning solutions.
Our machine learning development services are effective at handling prodigious volumes of business and consumer data to extract actionable insights and predictions. Our team has developed various cloud-based machine learning systems including an automated Diabetic Prediction System that identifies diabetes in patients with over 90% accuracy. In addition, our AI team has experiential knowledge in IBM Watson, Azure, and AWS Cloud consulting services.
This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.
Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial
It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.
Below topics are explained in this Machine Learning course for beginners:
Basics of Machine Learning - 01:46
Why Machine Learning - 09:18
What is Machine Learning - 13:25
Types of Machine Learning - 18:32
Supervised Learning - 18:44
Reinforcement Learning - 21:06
Supervised VS Unsupervised - 22:26
Linear Regression - 23:38
Introduction to Machine Learning - 25:08
Application of Linear Regression - 26:40
Understanding Linear Regression - 27:19
Regression Equation - 28:00
Multiple Linear Regression - 35:57
Logistic Regression - 55:45
What is Logistic Regression - 56:04
What is Linear Regression - 59:35
Comparing Linear & Logistic Regression - 01:05:28
What is K-Means Clustering - 01:26:20
How does K-Means Clustering work - 01:38:00
What is Decision Tree - 02:15:15
How does Decision Tree work - 02:25:15
Random Forest Tutorial - 02:39:56
Why Random Forest - 02:41:52
What is Random Forest - 02:43:21
How does Decision Tree work- 02:52:02
K-Nearest Neighbors Algorithm Tutorial - 03:22:02
Why KNN - 03:24:11
What is KNN - 03:24:24
How do we choose 'K' - 03:25:38
When do we use KNN - 03:27:37
Applications of Support Vector Machine - 03:48:31
Why Support Vector Machine - 03:48:55
What Support Vector Machine - 03:50:34
Advantages of Support Vector Machine - 03:54:54
What is Naive Bayes - 04:13:06
Where is Naive Bayes used - 04:17:45
Top 10 Application of Machine Learning - 04:54:48
How to become a Machine Learning Engineer - 04:59:46
Machine Learning Interview Questions - 05:09:03
This Machine Learning tutorial for beginners will enable you to learn Machine Learning algorithms with python examples. Become a pro in Machine Learning.
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Machine learning is changing the dimensions of business in many industries. A report projects that the value added by machine learning systems shall reach up to $3.9 Trillion by 2022.Machine lear...
Machine learning is proving it's worth in many industries like manufacturing, financial services, healthcare, and retail, to name a few. We hope that we have dispelled some of the myths associated with Machine Learning. It wouldn't be MLan incorrect to say that we have both overestimated and underestimated the potential of Machine learning systems.