Gearing up for Machine Learning Trends in 2020

Gearing up for Machine Learning Trends in 2020

Artificial intelligence (AI) is all set to enter the new year 2020 with greater might. The new year heralds a paradigm shift for global business infrastructures with [AI development services](https://artificialintelligence.oodles.io/ "AI...

Artificial intelligence (AI) is all set to enter the new year 2020 with greater might. The new year heralds a paradigm shift for global business infrastructures with AI development services and emerging machine learning (ML) technologies. At Oodles, we are looking forward to deploying machine learning techniques across wider business applications and processes. Here are the upcoming machine-learning trends for 2020 encompassing on-premise and cloud machine learning solutions to augment global business infrastructures.

  1. Machine Learning Algorithms bond with IoT
    From interconnected computing devices to smart home appliances, the Internet of Things (IoT) has garnered mainstream adoption with expansive technological advancements.

While IoT is analogous to machinery, AI and machine learning algorithms are touted as the brain-power to capitalize on this machinery.

It is possible by channelizing the high volumes of data collected by IoT devices to extract actionable insights. In 2020, machine learning will be used to process, analyze and use everyday data collected and stored in information and communication devices such as-

a) Using electricity consumption data from smart appliances such as air conditioners and refrigerators to make more energy-efficient and sustainable products.

b) Analyzing on-road sensors, CCTV cameras, and driving patterns to build secure autonomous vehicles.

c) Integrating wearable gadgets with machine learning techniques to design a more personalized and user-centric experience.

  1. Conversational Analytics
    The last decade saw a paradigm shift in the way businesses interact with their customers. Natural language processing (NLP) based conversational interfaces with text and voice-enabled communication windows are steadily gaining momentum across industries. The new year is set to generate greater value from the unstructured data collected through voice-controlled channels.

Conversational analytics is an AI-powered technology that curates the data from conversational interfaces and chatbots to extract audience insights.

Machine learning constitutes the building blocks of conversational analytics wherein ML algorithms extract contextual details of the target audience. eCommerce, marketing, and banking businesses are most likely to benefit from conversational analytics to improve customer services efficiently.

  1. Augmented Data Management
    Since massive digitization, businesses have been struggling with complex data management systems to administrate critical databases and formulate effective business strategies. More so, the traditional data management systems require manual intervention leading to unreliable, insecure, and unscalable data processing.

Machine learning is projected to become the driving force for data-centric businesses in the new year. Augmented data management is the upcoming AI-based technique that combines machine learning algorithms to monitor and large data volumes for critical decision-making. With augmented data management systems, businesses can essentially integrate automation into their existing data management models to accelerate decision-making efforts.

The process incorporates both ML-based structured and unstructured techniques to identify, process, link, and classify raw databases. Though, organizations need to carefully analyze their information architecture and objectives before capitalizing on automated data management systems.

Watching for Machine Learning Trends 2020 with Oodles AI
Technology is making remarkable strides with each passing year. At Oodles, we are entering the new year equipped with better and more diversified AI solutions to optimize business outputs. We are constantly exploring new AI, machine learning, and deep learning applications to amplify business operations significantly.

Our machine learning development capabilities extend to computer vision, chatbot development, deep analytics, natural language processing, and RPA services. Our machine learning development services are available for both on-premise and cloud-based business infrastructures. Under our cloud-based ML services, we offer AWS, Google, IBM Watson, and Microsoft Azure consulting services.

Reach out to our AI development team to know more about our artificial intelligence and machine learning services.

Top 10 Technologies to Learn in 2020 | Trending Technologies 2020

Top 10 Technologies to Learn in 2020 | Trending Technologies 2020

Intellipaat Online Training: https://intellipaat.com/ In this Intellipaat's top 10 technologies to learn in 2020 video, you will learn all the trending techn...

In this Intellipaat's top 10 technologies to learn in 2020 video, you will learn all the trending technologies in the market in 2020. The end goal of this video is to educate you about the latest technologies to learn and all the top 10 trending technologies you can watch for in order to make a fantastic career in IT technologies in 2020.

What is Machine Learning? | Basics of Machine Learning ( 2020)

What is Machine Learning? | Basics of Machine Learning ( 2020)

What is Machine Learning? | Basics of Machine Learning ( 2020) will help you understand the basics of Machine Learning and exactly what is Machine Learning, you will have a fair idea about the history and how Machine learning has evolved over the years up till decade.

This video will help you understand the basics of Machine Learning and exactly what is Machine Learning, you will have a fair idea about the history and how Machine learning has evolved over the years up till decade.

Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

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:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03