Natural Language Processing (NLP) Online Training

Natural Language Processing (NLP) Biggest Strengths

No Need for Feature Engineering

• Natural Language Processing (NLP) is largely responsible for today’s growth in the use of AI. The technology has given computers extraordinary powers, such as the ability to recognize speech almost as good as a human being, a skill too tricky to code by hand.
• Natural Language Processing (NLP) has also transformed computer vision and dramatically improved machine translation. It is now being used to guide and enhance all sorts of key processes in medicine, finance, marketing—and beyond.
• Feature engineering is the process of extracting features from raw data to better describe the underlying problem. It is a fundamental job in machine learning as it improves model accuracy. The process can sometimes require domain knowledge about a given problem.
• To better understand feature engineering, consider the following example.
• In the real estate business, the location of a house has a significant impact on the selling price. Suppose the location is given as the latitude and the longitude. Alone these two numbers are not of any use but put together they represent a location. The act of combining the latitude and the longitude to make one feature is feature engineering.
• One of Natural Language Processing (NLP) ’s main advantages over other machine learning algorithms is its capacity to execute feature engineering on it own. A Natural Language Processing (NLP) algorithm will scan the data to search for features that correlate and combine them to enable faster learning without being explicitly told to do so.
• This ability means that data scientists can sometimes save months of work. Besides, the neural networks that a Natural Language Processing (NLP) algorithm is made of can uncover new, more complex features that human can miss.

Best Results with Unstructured Data

• According to research from Gartner, up to 80% of a company’s data is unstructured because most of it exists in different formats such as texts, pictures, pdf files and more. Unstructured data is hard to analyze for most machine learning algorithms, which means it’s also going unutilized. That is where Natural Language Processing (NLP) can help.
• Natural Language Processing (NLP) algorithms can be trained using different data formats, and still derive insights that are relevant to the purpose of its training. For example, a Natural Language Processing (NLP) algorithm can uncover any existing relations between pictures, social media chatter, industry analysis, weather forecast and more to predict future stock prices of a given company.

No Need for Labeling of Data

• Getting good-quality training data is one of the biggest problems in machine learning because data labeling can be a tedious and expensive job.
• Sometimes, the data labeling process is simple but time-consuming. For example, labeling photos “dog” or “muffin” is an easy task, but an algorithm needs thousands of pictures to tell the difference. Other times, data labeling may require the judgments of highly skilled industry experts, and that is why, for some industries, getting high-quality training data can be very expensive.

Let’s look at the example of Microsoft’s project InnerEye, a tool that uses computer vision to analyze radiological images. To make correct, autonomous decisions, the algorithm requires thousands of well-annotated images where different physical anomalies of the human body are clearly labeled. Such work needs to be done by a radiologist with experience and a trained eye. According to Glassdoor, an average base salary for a radiologist is $290.000 a year, which puts the hourly rate just short of $200. Given that around 4-5 images can be analyzed per hours, proper labeling of all images will be expensive.

• With Natural Language Processing (NLP) , the need for well-labeled data is made obsolete as Natural Language Processing (NLP) algorithms excel at learning without guidelines. Other forms of machine learning are not nearly as successful with this type of learning. In the example above, a Natural Language Processing (NLP) algorithm would be able to detect physical anomalies of the human body, even at earlier stages than human doctors.

Efficient at Delivering High-quality Results

• Humans need rest and fuel. They get tired or hungry and make careless mistakes. That is not the case for neural networks. Once trained correctly, a Natural Language Processing (NLP) brain can perform thousands of repetitive, routine tasks within a shorter period of time than it would take a human being.
• The quality of its work never diminishes, unless the training data includes raw data that does not represent the problem you are trying to solve.
• Natural Language Processing (NLP) algorithms are applied to customer data in CRM systems, social media and other online data to better segment clients, predict churn and detect fraud. The financial industry is relying more and more on Natural Language Processing (NLP) to deliver stock price predictions and execute trades at the right time. In the healthcare industry Natural Language Processing (NLP) networks are exploring the possibility of repurposing known and tested drugs for use against new diseases to shorten the time before the drugs are made available to the general public.

Some of the job profiles for certified DL professionals are:

• Data Scientist
• DL Analyst
• DL Associate
• Natural Language Processing (NLP) R&D Engineer
• Natural Language Processing (NLP) Software Engineer 
• Natural Language Processing (NLP) Analyst/Consultant

Governmental institutions are also turning to Natural Language Processing (NLP) for help to get real-time insights into metric like food production and energy infrastructure by analyzing satellite imagery.

• The list can go on, but one thing is clear: given the use cases and enthusiasm for Natural Language Processing (NLP) , we can expect large investments to be made to further perfect this technology, and more and more of the current challenges to be solved in the future.