1- What is generalization?

The term ‘generalization’ refers to the model’s capability to adapt and react properly to previously unseen, new data, which has been drawn from the same distribution as the one used to build the model. In other words, generalization examines how well a model can digest new data and make correct predictions after getting trained on a training set.

How well a model is able to generalize is the key to its success. If you train a model too well on training data, it will be incapable of generalizing. In such cases, it will end up making erroneous predictions when it’s given new data. This would make the model ineffective even though it’s capable of making correct predictions for the training data set. This is known as overfitting. The inverse (underfitting) is also true, which happens when you train a model with inadequate data. In cases of underfitting, your model would fail to make accurate predictions even with the training data. This would make the model just as useless as overfitting.

2- The ideal solution

You would ideally want to choose a model that stands at the sweet spot between overfitting and underfitting. To achieve this goal, you can track the performance of a machine learning algorithm over time as it’s working with a set of training data. You can plot both the skill on the training data and the skill on a test dataset that you’ve held back from the training process. As the algorithm learns over time, the level of error for the model on the training data would decrease and so would the error on the test dataset. Training the model for too long would cause a continual decrease in the performance on the training dataset due to overfitting. At the same time, due to the model’s decreasing ability for generalization, the error for the test set would start to increase again. The sweet spot is the point just before the error on the test dataset begins to rise where the model shows good skill on both the training dataset as well as the unseen test dataset.

To limit overfitting in a machine learning algorithm, two additional techniques that you can use are:

• Using a resampling method to estimate the accuracy of the model
• Holding back a validation dataset

So, during your machine learning training, keep an eye on generalization when estimating your model accuracy on unseen data.

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1- Machine learning in Java

There’s a misconception that without learning Python or R, you can’t succeed in machine learning. However, the truth is that if you’ve got a Java development background, you can do without learning these popular programming languages. You should remember that Java gives support for development in any field you want, and data science is no different. By using third-party open source libraries, you can leverage your expertise as a Java developer to implement a data science algorithm and get things done. Though there’s no denying that Python or R come with their own set of advantages, you won’t need to learn them specifically to execute machine learning- or data science-related algorithms.

2- Leading machine learning libraries for Java

If you’re looking for some of the best machine learning libraries for Java, you’ll find Weka to be the most popular choice. Weka is suitable for data mining tasks, where algorithms can either be called from your own Java code or applied directly to a dataset. Weka contains tools for functions like clustering, classification, regression, association rules, and visualization.

Apache Mahout is another machine learning library for Java, which is designed to be enterprise-ready. This scalable and flexible ML framework comes with in-built algorithms to help you create your own algorithm implementations. Mahout’s distributed linear algebra framework allows statisticians, mathematicians, analytics professionals, and data scientists to implement their own algorithms.

ADAMS (Advanced Data mining And Machine learning System) is a flexible workflow engine that uses a tree-like structure to manage how data flows in the workflow. This means there exist no explicit connections that are essential. Using ADAMS, you can quickly build and maintain real-world workflows that are generally complex in nature.

Some other machine learning libraries for Java are ELKI (Environment for Developing KDD-Applications Supported by Index Structures), Deeplearning4j, JavaML, MALLET (MAchine Learning for LanguagE Toolkit), JSAT (Java Statistical Analysis Tool), and RapidMiner, to name a few.

If you’re a Java programmer or are adept in Java, the fastest route to a career in machine learning is enrolling in a machine learning bootcamp. Taught by industry experts and having ample hands-on training, such a bootcamp will help you fast-track your machine learning career dreams.

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1- What’re neural networks?

In simple words, neural networks can be considered mathematical models loosely modeled on the human brain. Neural networks engage in two distinguished phases. First, comes the learning phase where a model is trained to perform certain tasks. These could be how to perform language translations or how to describe images to the blind. And second comes the application stage where the trained model is utilized. You can think of Spotify sending you a weekly-playlist created by analyzing your music taste. Neural networks come with some fundamental building blocks that include neurons, input, outputs, weights, and biases. Here, each neuron comes with one or multiple inputs together with a single output.

You can use this output as an input to one or multiple neurons or as the entire network’s output. The most intelligent thing about neural networks is the self-learning during the training period of the models. Here, a neural network is given a dataset of inputs (could be text, speech, or images – but everything has to be translated to numbers) and a true answer accompanying every observation set. Now the model learns to find out the true answer based on the inputs it has been presented with. Throughout the learning process, the model would estimate second-hand-values continuously and compare those to the true values. If there’s a large difference, the model parameters get automatically updated to push those estimates closer to true second-hand-values. This process gets repeated until the average difference between true and assigned values becomes adequately small.

2- What’s deep learning?

You can think of deep learning as the absolute cutting edge of AI (artificial intelligence). Here, the machine trains itself to process, as well as, learn from data. With deep learning, you don’t need to teach machines to process and learn from data, which is the working method of machine learning.

Parting Thoughts

The difference between deep learning and neural networks remains in the model’s depth where the former phrase is used to mention complex neural networks. A deep learning system is simply a self-teaching one that keeps on learning by filtering information via multiple hidden layers, much like the way the human brain works. It’s being assumed by some people that deep learning will automate a significant number of tasks and might replace many human workers in the future. But it’s also important to understand that implementation of deep learning might replace someone who works on repetitive, manual tasks but it just can’t replace the engineer or the scientist developing and maintaining a deep learning application.

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1- An overview of data mining applications

If you’ve made up your mind to learn data mining, here are some applications of it, knowing which would help you choose your career path:

• Market Basket Analysis: This modeling technique is based upon a theory that buying a specific set of articles makes you more inclined to buy another group of articles. A retailer can use this technique to recognize a buyer’s purchase behavior, which can then be used to tweak the offerings or change the store’s layout to encourage buying. The use of differential analysis comparison of results between customers in different demographic groups or even between stores at different locations can help retailers make the necessary changes to boost sales.
• Fraud Detection: Unlike time-consuming and complex traditional methods of fraud detection, data mining can help find meaningful patterns in data. This information can then be used to collect sample records, classify them as fraudulent or non-fraudulent, and build a model using this data where the algorithm is designed to spot whether the record is fraudulent or not.
• Future Healthcare: Using data mining approaches such as machine learning, multi-dimensional databases, statistics, data visualization, and soft computing, the number of patients in each category can be predicted. This can help develop processes that ensure each patient gets appropriate care at the right time and the right place. Apart from helping to improve healthcare and reduce costs, data mining can also assist healthcare insurers in identifying fraud and abuse.
• Education: EDM or Educational Data Mining helps predict the future learning behavior of students, and even lets you study the effects of educational support as well as how advanced scientific knowledge can help in learning. Thus, data mining can help educational and training institutes focus on what they should teach and the ways to effectively teach apart from predicting the results of the students.

Apart from the above, many other industries like banking, transportation, manufacturing, etc. can also gain from data mining and data science.

2- How to learn data mining?

If you have your eyes set on the field of data science and want to master data mining, you can either get enrolled in a full-time course or find some bootcamps to join where you’ll learn all that you need to, albeit much faster than a traditional course. In case you plan to use bootcamps, remember that you’ll need a good statistical and machine learning foundation to understand what’s being taught and apply this knowledge to get useful information by cutting the noise of Big Data.

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Major impacts of artificial intelligence

While we’re probably heading toward a future where machines might become intellectually equal or superior to humans, let’s take a look at how artificial intelligence is transforming the present world now.

• Highly personalized production: Advancements in artificial intelligence together with software intelligence are empowering businesses to offer products and services that are extremely relevant to individual customers. According to studies, brands that are willing to personalize products are able to develop greater trust with their consumers. Studies also revealed that a huge percentage of customers are willing to have trusted brands use their personal data to receive targeted and tailored products, offers, and recommendations.
• No more repetitive tasks: Repetitive tasks aren’t only monotonous but consume a huge amount of time and energy of the human workforce as well. With the help of artificial intelligence, many of these tasks are being carried out by machines as they can think faster than humans and are capable of multi-tasking. Additionally, as their time and speed are calculation based parameters, those can be adjusted, if required.
• Almost flawless medical diagnosis: Artificial intelligence has enabled the healthcare industry to effectively analyze possible advert scenarios by advancing the process of finding solutions or preventing. It has also made healthcare accessible for those who’ve had unequal access until today, because now it has become possible to build a personalized way for inclusion for each one.
• Personalized education: It’s a fact that it’s impossible for a single educator to work with each student at once. With the emergence of a personal AI assistant or tutor, today it has become possible for students to get one-on-one, additional help in areas of required development. While some educators may fear the idea of artificial intelligence replacing them in the future, the reality is, human intelligence and AI working in tandem will only drive us forward.

Parting Thoughts

Virtually, there’s nothing that artificial intelligence won’t be able to support but it requires human expertise to oversee, assign its responsibilities, and identify its limitations. When the best parts of AI would be coupled with the best parts of humanity, it’ll take us to a different level together than either one could do individually. So, it can be safe to say that it’s probably the best time to learn artificial intelligence.  A comprehensive AI training would not only help you get an edge in today’s competitive job market but will make you future-proof as well.

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First of all, data science is often described as a multidisciplinary field which uses scientific processes, methods, systems, and algorithms to derive insights and knowledge from data. The emergence of big data has promoted the development of new algorithms, systems, and computing paradigms. Data science as a field essentially uses the most powerful hardware, most powerful programming system, and algorithms to obtain the solution of problems.

When it comes to identifying the most powerful idea in data science, we can say it depends on patterns and the way you want to use them. And which one of the patterns is useful to you, depends on the goals you’re trying to accomplish.

Though the most fundamental definition of data science is it’s a field that involves capturing, storing, organizing, and analyzing huge amounts of data, it all boils down to identifying patterns and drawing conclusions that can help either to identify the solution to a present business problem or to predict future scopes. And when it comes to identifying patterns, probably the best idea is to split a dataset. Then having the analysts focus on one part, come up with their insights derived from that part, and finally using the other part of the dataset to check their conclusions.

It’s important to understand that in recent years, the field of data science has eventually become less about the data and more about different types of tools and technologies that are being used to interact with it. High-end solutions like artificial intelligence, machine learning together with robust and advanced analytics tools now make it possible not only to process and comprehend huge amounts of data but at unprecedented speeds.

If reading till now and learning about the most important idea of data science make you interested in the field, let’s have a quick discussion on the things that are critical to start your journey. Some of the obvious subjects include programming, mathematics, descriptive statistics, linear algebra, and machine learning. There’re lots of online courses offered by reputable institutes that can help you gain a robust understanding of all these subjects. Then there’re data science master’s programs, along with certificate courses, which can help you gain more advanced skills in the field.

Keeping the key goal of data science and the present situation of the tech landscape, it won’t be difficult to say that the future of data science should become more expansive than ever – as the field touches almost every enterprise-level process. And we can expect to see this progress becoming more expedited with the help of automation, machine learning, and more advanced and efficient solutions.

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Here’re the major ways through which the finance industry is leveraging the power of artificial intelligence and machine learning.

1- Better risk management

Probably the biggest impact of artificial intelligence and machine learning on the finance industry can be found when it comes to risk management. While traditional software applications can predict creditworthiness based on the static information obtained from financial reports and loan applications, implementation of machine learning technologies can help financial institutions to go much further. Algorithms identify the signs of probable future issues and analyze a client’s history of risk cases to help the authorities make an informed decision. They’re also able to identify present market trends together with relevant news items which can affect the ability of a client to pay.

2- Improved data security

Data security has always been at the top of the list of concerns for any financial institution. And if you consider the number of data breaches occurred during recent years, there’re reasons to be concerned. Traditional security tools aren’t capable of identifying modern sophisticated cyberattacks. To mitigate security risks, financial institutions implement advanced technologies like machine learning. Security solutions powered by machine learning are come with unique abilities to secure the financial data. The combined power of big data capabilities and intelligent pattern analysis gives machine learning security technology a robust advantage over traditional tools.

3- Enhanced customer experience

Like all other industries, the financial industry is also focusing on developing the top line by implementing advanced methods to offer custom services and better experience to customers. Many financial institutions have already introduced chatbots powered by artificial intelligence abilities that can analyze the voice of a customer and converse accordingly. With the help of machine learning and big data, these chatbots understand how to respond to the questions of customers’ – from transaction-specific questions to onboarding concerns. Additionally, technologies backed by artificial intelligence and machine learning are capable of making product recommendations and handling customer requests.

Parting Thoughts

In the finance industry, the disruption triggered by artificial intelligence and machine learning is increasing exponentially and toward greater economic impact than ever, both on the customers and the industry. By addressing all the major operational aspects and adding advanced features, these technologies are not only revolutionizing the entire industry but also improving the financial health millions of customers involved in the process. And from a business perspective, these technologies are driving a more fundamental and deeper shift in the finance industry.

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1- Targeting the audience

These days, one of the biggest problems experienced by businesses is that they fail to capture the attention of common people. The problem lies in the fact that advertisements often don’t connect with the audience. If you too are experiencing this issue, implementation of machine learning can help you sail through. You can use computer speech and vision to obtain valuable insights about your audience and use that information to create more targeted ads that result in more engagements which mean more profit.

2- Personalized customer service

Quality of customer service can make or break a business. With the help of machine learning tools and technologies, it’s now possible to combine years of data pertaining to customer services and merge it with NLP technology. The natural language processing algorithms make interactions with customers more personalized by leveraging that data. Each and every customer receives the most useful answers to their queries, which greatly increases the quality quotient of customer service. Additionally, the technology reduces the need for heavy investment that results in reduced customer servicing costs.

3- Personalize product recommendations

If you’re into e-commerce environment, then you probably know that the customers like to have personalized product recommendations delivered to them. For them, it improves their overall shopping experience and for you, it brings a new opportunity to sell more products. By leveraging the power of predictive analysis and machine learning, you can look beyond what the consumers searching for and try to connect those dots on what they most likely want. Matching customers to specific products or services will increase the chances of more conversions and thus, more profit.

4- Dynamic pricing

Change of pricing based on the level of demand or a need can bring a good opportunity to increase your revenue stream. For instance, Uber uses machine learning to create dynamic prices. It uses the technology to optimize the ride-sharing aspect and to minimize wait time. It can temporarily change pricing in an area to obtain a higher revenue stream and can lower rates where the demand is much lower. Machine learning can utilize available data to predict areas where demand may occur, which you can leverage to attract more customers, increasing your bottom line.

Final Thoughts

These days, businesses are capturing data from a huge number of sources and with the help of machine learning tools and technologies, they’re becoming able to develop a better brand exposure to obtain successful outcomes. Machine learning has already started impacting almost every part of the business domain. So, it’d be wise to integrate this technology with your existing technologies to improve profit.

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While the concept of big data has been around for a significant number of years, everything has started to change with the emergence of big data analytics. This process allows businesses to perform analytical procedures efficiently and quickly, giving them a competitive advantage over competitors. Here’re some of the most prominent real-world examples of how big data analytics is being used.

1- Healthcare industry

The entire healthcare industry is getting transformed with the help of big data analytics. The ability to provide hyper-personalized patient treatment, improve the quality of life of the patients, as well as, discover medical breakthroughs – all have been impacted by big data analytics. In this industry, big data analytics isn’t performed with the focus of finding new product opportunities or increasing profits. Instead, it’s all about applying and analyzing big data to offer a better patient-centric approach. For instance, healthcare providers are analyzing historical big data to analyze and identify certain risk factors in patients, which is extremely useful for early detection of diseases, enabling both the patients and doctors to take action sooner.

2- Retail industry

Probably the maximum implementation of big data analytics can be observed in the retail industry. As the industry has gone digital, the customers have also started to expect a better and seamless experience. With the help of big data analytics, retail companies have become in a position to understand their customers more and thus, to provide a variety of personalized services. From creating product recommendations based on a customer’s past searches to demand forecasting to performing crisis control – everything is being taken care of through big data analytics.

3- Media and entertainment industry

The media and entertainment industry is one of the biggest users of big data analytics. As the number of users of different digital gadgets is increasing rapidly, media and entertainment companies are leveraging the power of big data analytics to a great extent. Some of the biggest benefits that are being experienced by the industry include on-demand or optimized scheduling of media streams, getting actionable insights from customer reviews, predicting the actual interests of audiences, successful targeting of the advertisements, and many more.

Final Thoughts

For any business, big data analytics is a crucial investment that can help to optimize the real-life situations where common people are involved to a great extent. Implementation of big data analytics not only helps businesses to achieve competitive advantage but also drives customer retention and reduces the cost of operation. And as technological advancements steadily continue to emerge, big data analytics will become even more important to businesses across industries.

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You can consider an artificial neural network as a computational model which is based on the human brain’s neural structure. Neural networks are capable of learning to perform tasks such as prediction, decision-making, classification, visualization, just to name a few.

An artificial neural network contains processing elements or artificial neurons and is organized in different interconnected layers namely input, hidden, and output. In deep learning, different types of neural networks are used. Since the emergence of big data, the field of deep learning has been gaining steady popularity as the performance of neural networks has improved by working with more amounts of data than ever before.

A lot of neural networks are there, each with its unique strengths. Different principles are used by different types of neural networks to determine their own rules. Let’s have a look at the most common ones.

• Convolutional neural network or CNN: A convolutional neural network comes with one or multiple convolutional layers, which can either be pooled or completely interconnected and utilizes a variation of multilayer perceptrons. Before the result is passed on to the next layer, a convolutional operation on the input is used by the convolutional layer. This operation lets the network to be deeper but with much fewer parameters. Convolutional neural networks demonstrate excellent results in speech and image applications.
• Recurrent neural network or RNN: A recurrent neural network is capable of remembering the past the decisions are influenced by what it has learned in the past. In simple words, each node of a recurrent neural network acts as a memory cell while performing computations and carrying out operations. LSTM or Long Short-Term Memory is a specific RNN architecture which was designed to model temporal sequences, as well as, their long-range dependencies more accurately compared to traditional RNNs. The capability of recurrent neural networks suggests that they can make better predictions by learning the temporal context of input sequences. Sequence prediction problems may come in different forms and can be best described by the types of outputs and inputs supported. Some instances of sequence prediction problems may include One-to-Many, Many-to-One, and Many-to-Many. LSTMs, in particular, have received a huge success when working with deep learning. It includes both sequences of spoken language and sequences of text. In general, recurrent neural networks are used for text data, speech data, regression prediction problems, classification prediction problems, and generative models.

Final Takeaway

As you may have understood from the above, a recurrent neural network is the best suited for temporal data in working with deep learning. Neural networks are designed to truly learn and improve more with more usage and more data. And that’s why it’s sometimes said that different kinds of neural networks will be the next-generation AI’s fundamental framework.

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