Top Myths About Machine Learning

People tend to have different opinions when it comes to machine learning. Some have the ideology that machine learning may mark the end of the human race; or a catastrophic end to privacy. Human beings have the idea that machine learning will give rise to superhuman intelligence, but this is not usually the case. On the contrary, it is quite apparent that computers do not have common sense, and there is no way in this world that they can overtake the thinking capacity of human beings.

At times, people believe that machine learning is purely meant for data summarization, but that’s not the case. The reality of the matter is that machine learning tends to predict the future by checking on your history. A good example of future prediction is the type of movies one has watched. In such a situation, the machine will be able to suggest similar films based on your past preferences.

Learning algorithms tend to discover more profound knowledge and not just correlations between events as people tend to assume. According to Pedro Domingos, a mole can be used to explain the rule of learning algorithms. For example, if a mole grows with an irregular shape and color, there is a high possibility that it might have skin cancer.

Another myth is that machine learning does not discover causal relationships but correlations only. Machine learning performs different activities and observes whether the result of the occurrence is due to the other event, or if the outcome means that there is a causal relationship between two different events. A computer can quickly discover the causal relationship by just looking at the past data, without necessarily having to experiment.

Machine learning is also believed to cause hallucinating patterns due to the intense provision of data. However, this is not the case as machine learning experts tend to keep this information at minimum levels. This is achieved by gathering more information which has the same set of attributes.

Many are the times when people assume machine learning ignores preexisting knowledge. In actual sense learning, algorithms use data to refine a pre existing body of knowledge, which is detailed as long as it is fed in a computer in simple terms. Lastly, the idea that machine learning cannot predict hidden events that have occurred previously is purely a misconception. Machine learning predicts rare events with high accuracy through comparison.

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As technology continues to advance so will the field of data science. Much of 2018 was viewed as the year of data breaches and leaks; this year however, will be known for technological disruption and use of Artificial Intelligence. In 2019 we will also see a focus on maturing technologies. Here are some of the top data science trends for 2019:

 

Reinforcement Learning: During 2019 there will be a resurgence of reinforcement learning. Even though this is best described as a “human-like learning behavior,” we will see its use in data science through statistics and algorithms. Concepts using reinforcement learning will start to turn into actual products as well.

 

Augmented Data Management: This latest trend in data science is a game changer for the industry. According to Information Age, “Augmented data management utilises machine learning capabilities and AI technology to make data management categories including data quality, master data management, metadata management, data integration as well as database management systems (DBMSs) self-configuring and self-tuning.” Thanks to augmented data management, more individuals will be able to use data. It also gives high-level data scientists the opportunity to work on more important tasks.

 

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Essential Skills of Data Scientists

Data scientists are in high demand as companies are constantly seeking to get the most value from their resources. Greg Boyd, director at consulting firm Protiviti, says, “as organizations begin to fully capitalize on the use of their internal data assets and examine the integration of hundreds of third-party data sources, the role of the data scientist will continue to expand in relevance.” The rising stars of many businesses are the savvy data scientists who can not only manipulate large amounts of data using complex statistical and visualization techniques but also have a strong acumen that they can derive forward-looking insights. Now, to be an elite data scientists, one must have a particular set of skills. Here are some of the essential traits that data scientists have.

 

Critical Thinking

In order to be able to apply objective analysis of facts on a given topic or problem, they need to be solid critical thinkers. According to Anand Rao who is a global artificial intelligence and innovation lead for data and analytics at PwC, “they need to understand the business problem or decision being made and be able to model or abstract what is critical to solving the problem, versus what is extraneous and can be ignored.”

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Common Myths about Data Science

Even companies that seem to be at the forefront of the digital age are not taking full advantage of their resources. While they may be instituting all of the latest marketing trends, most are ignoring the data that those efforts are helping them collect.

Data Science Requires a Vast Amount of Data

In fact, there are several ways to collect and analyze data in smaller clusters. By looking at the speed at which data was collected, you can get a snapshot of current consumer trends. Looking at a variety of data, on the other hand, tells you how your consumers are finding you and what they’re looking for. Even a history of data can give you insights that you would not otherwise have obtained.

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Data Science Trends for 2018

Data is taking the world by storm. And in today’s digital world, with the internet and other forms of software dominating the markets, it’s important to know what to look forward to in terms of data science. Here are trends that are heating up:

 

Predictive Analytics

There are a certain amount of customers in any business that provide the most profits. These customers can be tracked based on their past behaviors and purchases. Then, using advanced algorithms you can predict what they will buy in the future and when. This gives you the foresight to prepare your business in the best way possible based on powerful data.

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Tips For Python Programming

In the world of data science, Python is becoming widely popular. People are using it in a variety of ways from back-end web servers to even front-end game development, along with everything in between. It has become a real general purpose language and a must-have tool for any programmer’s arsenal. However, besides Python being a multi-use tool, one of the other reasons why it has become so popular is that it is easy to learn. It reads like pseudo-code and is surprisingly agile. Now, while it isn’t the most difficult to learn, picking up any new language in code can be a daunting task, and it is essential to find the right places to learn. Take a look at some of these tips and tricks that can help you with Python.

 

The Modules

One of the things that most people enjoy with Python is that you can create your own functions and modules and put them all together in a separate folder. So, you can write down particular codes that you would use in common in a majority of your work and then convert them in a module and keep it to the side in that separate folder.

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5 Ways To Boost Your Coding Skills

5 Ways To Boost Your Coding Skills by Prem Hirubalan

The world is full of data. Data that requires extraction to turn into something more useful. Many data scientists took courses in computer science which provided necessary coding and programming skills. However, there is always room to improve, and with codes changing all the time, it can get difficult to stay on top of it if you are not working to hone your craft. Here are five ways that can help you improve your coding and programming skills.

 

Read Books

One of the best ways to learn and improve in coding is by reading books. There are two ways to improve yourself. You improve either by learning from your own experience or someone else’s experience. Most of the authors are great programmers themselves so you will find their experiences in that book. Two great suggestions for coding books are Clean Code by Uncle Bob and Effective Java by Joshua Bloch. These books provide first-hand experiences from the authors and contain great advice for finding problems with your code.

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