Microsoft Launches Free Machine Learning Course For Beginners

Microsoft has launched a free MIT-approved learning course titled “Machine Learning For Beginners” to teach students the basics of machine learning. The course duration is 12 weeks.

Read more: https://analyticsindiamag.com/microsoft-launches-free-machine-learning-course-for-beginners/

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Microsoft Launches Free Machine Learning Course For Beginners
Kennith  Kuhic

Kennith Kuhic

1625893241

Microsoft Launches Free Machine Learning Course For Beginners

Microsoft has launched a free MIT-approved learning course titled “Machine Learning For Beginners” to teach students the basics of machine learning. The course duration is 12 weeks.

Azure Cloud advocates and Microsoft student ambassador authors, contributors and reviewers put together the lesson plan that uses pre and post lesson quizzes, videos, knowledge checks, infographics, sketch notes and assignments to equip the students with machine learning skills. The curriculum aims to teach classic machine learning using Scikit-learn as a resource library.

Play The introduction to ML breaks down the key concepts of machine learning, history, introduction to tools and techniques and also the concept of fairness and data bias in ML. The curriculum covers Reinforcement Learning, Natural Language Processing, Time Series Forecasting, Clustering, Regression and Classification with hands-on practical lessons to teach how to use the models inside web applications.

The curriculum covers:

  • What techniques do ML researchers use to build ML models?
  • Getting started with Python and Scikit-learn for regression models
  • Visualization and clean data in preparation for ML
  • Build linear and polynomial regression models
  • Build a web app to use your trained model
  • Sentiment analysis
  • Real-world applications of classical ML

Find the full curriculum here: https://github.com/microsoft/ML-For-Beginners

Microsoft in education

Last year Microsoft launched an initiative to help 25 million people worldwide to acquire digital skills in the pandemic economy. Software development, graphic designing, data analysis, financial analysis and IT administration were among the 10 jobs identified with the greatest number of job openings and steady growth in the past four years. The initiative underlines the importance of digital skills to navigate the post pandemic world.

Microsoft has also listed a few courses on the edX platform. The courses are taught by Microsoft experts and offer hands-on experience with broad reach, cutting-edge technologies in areas including cloud services, mobile development, and data sciences. The courses, ideal for students and working professionals, teaches how to build innovative applications, services, and experiences on the Microsoft platform.

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Ananya Gupta

1595485129

Pros and Cons of Machine Learning Language

Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.

In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.

Pros of Machine learning

  1. **Effectively recognizes patterns and examples **

AI can survey enormous volumes of information and find explicit patterns and examples that would not be evident to people. For example, for an online business site like Amazon, it serves to comprehend the perusing practices and buy chronicles of its clients to help oblige the correct items, arrangements, and updates pertinent to them. It utilizes the outcomes to uncover important promotions to them.

**Do you know the Applications of Machine Learning? **

  1. No human mediation required (mechanization)

With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.

  1. **Constant Improvement **

As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.

  1. **Taking care of multi-dimensional and multi-assortment information **

AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence

  1. **Wide Applications **

You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.

**Cons of Machine Learning **

With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:

1.** Information Acquisition**

AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.

  1. **Time and Resources **

ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
**
Likewise, see the eventual fate of Machine Learning **

  1. **Understanding of Results **

Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.

  1. High mistake weakness

AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.

**Conclusion: **

Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.

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sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Michael  Hamill

Michael Hamill

1617327135

How To Read 43 Machine Learning Books in a Year

The Garrison (armies of militia) of libraries worldwide offer millions of books, such as the Library of Congress in D.C. has over 162 million books, and the New York Public library carries around 53 million books. So many books, so little time in a human’s life.

A number of people have asked me through several of my channels and conferences — how to find time to read books, and what can be done to read more books each month. Some audiences even feel that 43 machine learning books in a year are insufficient, and want more.

I keep discovering new material every day on top of the antiquated books, which still offer good concepts. To get started, I would suggest disconnecting from Netflix, Amazon Video, and regular TV channels. The more you watch any of this stuff, the more you wouldn’t be finding time to read the books.

In 2020, I had managed to read more than 96,120 pieces of books, eBooks, articles, averaging 267 pieces of books, eBooks, research papers, or articles per day. However, on average, people might have read 10 to 30 machine learning books in a year.

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Nora Joy

1604154094

Hire Machine Learning Developers in India

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