Machine Learning Technology

Posted by Anjali Bajpai on June 25th, 2019

Introduction:-

                      Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Some machine learning methods

Supervised Machine learning algorithms are often categorized as  or unsupervised.

• Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

• In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

• Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiringabled data generally doesn’t require additional resources.

• Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Advantages of machine learning as a career option

1. Great opportunities to progress

The growth is enormous and very diverse, slipping its tentacles into a variety of industries – from finance and healthcare to transport and education.

As a machine learning engineer, you will not only witness the technological revolution but be a part of it.

2. Work that matters

Machine learning engineering will allow you to work and build real-world products, right from autonomous cars to security drones.

These are not some numbers you crunch into a spreadsheet only to never hear about them again.

3. Direct link to Data Science

As a data scientist, you’ll be able to analyze data and extract value from it.

As a machine learning engineer, you’ll be able to make use of that information to train a machine learning model to predict results.

Disadvantages of machine learning as a career option

1. Demanding job

Training models, handling data as well as making and testing prototypes on a daily basis can lead to mental exhaustion.

2. It takes time and resources for machine learning to yield tangible results

Machine learning occurs over time.

So, there will be a period when your interface or algorithm won’t be developed enough for your company’s needs. The precise amount of time required will depend upon the nature of data, data source and how it is to be used.

3. Need to stay updated

As mentioned earlier, machine learning is a rapidly evolving field. Due to this, machine learning engineers are required to spend a considerable amount of time learning about the latest updates in the field.

Conclusion

It is best to first know both sides of the coin.

Research well about the options in front of you and then make an informed choice. Weigh the pros and cons of machine learning before you decide whether or not to make a career in this field.

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