This video gets you into deep dive in Machine Learning world like what exactly is Machine Learning, Introduction to #machinelearning ,Types of data, Stages of Machine Learning, #ML Algorithms Types like Supervised Learning and Unsupervised Learning, Performance , tools and technique along with Case study.The video also covers about what ML can't do.
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In today’s world, Machine Learning has become the most influential and powerful technology.
Machine learning is the process of giving training to computer systems about how to make accurate predictions when fed data.
Software engineering combined human-created rules with data to create answers to a problem which are Traditionally but machine learning uses data and answers to discover the rules behind a problem.
When we google something or listen to a song, watch video on youtube, take a photo.
Eliminating Manual Tasks : Duplication of data is the biggest dispute faced by the business today.
If any organization is using machine learning for their business than its simply end entry tasks and its incomparable accuracy saves both money & time.
Artificial intelligence matches human performance by learning, deriving conclusions on its own, and executing a clearly defined task.
Machine Learning is an artificial intelligence technology that uses data to automatically discover patterns and trends, resulting in deriving more accurate predictions of future events.Many organizations are leveraging ML and AI to advance their product development, sales, and overall customer experience.
Industries like manufacturing, construction, energy etc.
Most companies understand the value of a safety culture and have ideologies about what a hyper-vigilant safety culture looks like — where employees are on high alert of their surroundings, perpetually evaluating risks for the task at hand and taking precautions accordingly.
Algorithms can identify objects, edges, and velocity because of the advancements of Machine Vision.Workplace Safety Solutions through AI Computer Vision:Process and Production Safety - Majority of the workplace facilities have a CCTV system installed on the premises.
Vision-based object detection and tracking use feature such as shape, color, motion etc.
Machine learning (ML) and artificial intelligence (AI) have started to gain traction over the past years, and today, nearly every emerging startup is trying to leverage these technologies to attract funding and disrupt traditional markets.
And it’s true that companies using “AI” and “ML” as buzzwords in their pitch are more likely to attract external investments than their counterparts working with traditional and mainstream tech.But still, apart from all this hype around machine learning, how applicable is it for solving real-life, everyday problems and when does it make sense to use it instead of/together with traditional software programming?
Let’s start exploring the issue by describing the various types of machine learning and its basic principles.Machine Learning vs Traditional ProgrammingTo better understand how machine learning works, let’s look at how it differs from traditional programming.First of all, machine learning does not replace traditional programming, and a software developer will never use machine learning algorithms to create a website.
For example, ML can be used to build predictive algorithms for an online trading platform, while the platform’s UI, data visualization and other components will be implemented in a mainstream programming language such as Ruby, Python, or Java.The rule of thumb: only use machine learning when traditional programming methods are not effective/feasible for solving a particular problem.To better exemplify it, let’s consider a classical machine learning problem of exchange rate forecasting and see how it can be solved with the help of both techniques.In this article, we looked at three types of machine learning: supervised, unsupervised, and reinforcement.
Each of them has areas of practical application in real-world conditions and its own distinctive features.Supervised ML is by far the most developed and applicable form of machine learning to date.
Now there are dozens of ready-made classical algorithms for machine learning, as well as various Deep Learning algorithms for solving more complex problems, such as image, text, and voice processing.On the other hand, unsupervised machine learning is much less applicable in real life.
You might think machine learning is far-fetched from real life and something used by scientists with exceptional qualifications.
Believe me; you are mistaken here.
You’d be surprised to learn that you deal with this phenomenon in your daily activities.Netflix and Spotify suggestions, your favorite eCommerce chatbots, Grammarly and other spellcheckers, and many more.
All of those, as mentioned above, are shining examples of machine learning technology in our lives.This trend has become an essential part of software development.
And to build a machine learning app is no longer a crazy idea.
Your business may benefit from ML applications and bring additional value to the market.
Looking back at the 1990s, technology was limited to computers, the internet, emails, wired telephony, but advancements in technology have led to a sweeping change in its role as an indispensable necessity in our lives and society.
In the subsequent sections, we will see the expanse of AI and ML in various use cases as well as understand their role in one of the most advanced forms of biometric security — facial recognition.What Is Artificial Intelligence (AI)?AI is a technology that simulates human intelligence processes using machines to make cognitive decisions.
Secondly, it adds intelligence to products in the areas of automation, conversational platforms, smart machines, and bots.
Lastly, Artificial Intelligence helps in monetizing data for businesses to stay ahead of the curve.What Is Machine Learning (Ml)?Machine Learning is a subset of AI that mainly focuses on using data and algorithms to mimic human learning.
It converts unstructured data to manageable groups for processing through a process known as dimensionality reduction.On the other hand, neural networks also known as artificial neural networks comprise node layers — an input layer, multiple hidden layers, and an output layer.
In the basic neural network, two or three layers are present whereas a deep neural network consists of more than three layers.How Does ML Work?An ML algorithm has three components:Decision processError FunctionModel OptimizationIn the decision process, an initial input is analysed to make a prediction or estimation of the pattern in the data.
