There’s tonnes of data being generated every minute of our life. Thereafter, the data is provided to a machine learning model so that it is calculated and analyzed thousands of times to make progressive decisions and recommendations. With no human assistance, machine models learn, identify patterns, and make accurate and efficient decisions removing any possibility of human error. Abundance data availability, affordable data storage and processing marks the expansion of this prospective technology. This is also the reason that motivates companies to build compelling machine learning models that are able to analyze bigger and complex data in less time with improved results. 

Countless industries like healthcare, government, marketing and sales, e-commerce, social media, transportation, logistics, manufacturing, etcetera are embracing the technology as the best way to walk the path of expansion and development. They understand the value that it can bring to business of all sizes.

75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10% (Source: Forbes)

Making informed decisions 

This is a very crucial aspect for business organizations and they rely on good information available at the right time for that. Business organizations take advantage of ML technology and put other intelligent technologies to use to extract large data sets to integrate them into everyday processes and develop actionable predictions. This futuristic technology in the 20th century offers improved business models and services.

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AI-driven personal assistants reduce man’s efforts

These computer systems perform tasks based on human commands. Machine learning here uses natural language processing (NLP)  to program a system to process and assess the human language. The speech or text recognition is looked into that allows humans to communicate with computers in their own language and the computers to understand the command and act on it and perform likewise.

Machine learning improves logistics 

ML improves logistics (a business that runs on data) by enhancing the processes like buying raw materials, manufacturing, selling the end product, shipment, etc. It allows the service providers to study large frameworks of data making its management system intelligent and improved. From optimizing equipment costs, and increasing transparency across the supply chain, accounting for unusual activity, this technology is proving productive. 

Maintaining manufacturing for companies 

Predictive analysis and ML apps let businesses save and make money. It stores data, analyses it, predicts outcomes, generates insights, and brings solutions for the business. There are specific algorithms that can also analyse the working of a machine and when it requires maintenance and this avoids unexpected machinery problems and shutdowns.

According to a report, the global machine learning market was valued at around USD 1.58 billion in 2017 and is expected to reach approximately USD 20.83 billion in 2024, growing at a CAGR of 44.06% between 2017 and 2024.

Source: Globe News Wire

Analysing customer’s data

Now think of it this way, let’s say a company has 1 petabyte of data which is trust me a lot and calculatively, it is humanly impossible for one person to process all of this data without any errors. So, by using a machine learning software, the company will be able to analyze this large volume of unstructured data in less time and without errors. Now you tell me, won’t this give the company a competitive edge over the others?   

Business competition is overwhelming and for any business to do better in the market and earn profits, it is important for them to understand their customers and their ever-changing needs. There is large amounts of data to make the process a tad bit easier. And, when it comes to processing that data, machine learning comes next to help with the process. 

Customer’s data like purchasing patterns, preferences, demographics, their reviews, complaints, and more are analyzed to make predictions and machine learning will explain all the data that can be used to build business strategies and increase business by providing better customer experience. This can be achieved by building individual customer profiles, improving user interface and user experience and optimizing search results on search engines.

57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support.

Source: Forbes

Cybersecurity

Unidentified threats that are capable of causing harm to any business should be recognized and taken care of and having said that, machine learning in business is capable of doing so and within no time.

“If not now then when?” is the question here. It’s high time we discontinue manual data entry. There are errors and duplication of data likely to be found with a manual approach. Also, let’s not forget about the increased time consumption the process demands. Machine learning and predictive algorithms encourage to make the data entry process less arduous and also reduces the chances of glitches.

Financial Analysis

Investors, financers, traders, stock marketers use ML algorithms to analyse the market and make desired profits. Algorithm trading to make better trade decisions, fraud detection and prevention, portfolio management, loan underwriting and credit scoring are all taken care of with the use of developed machine learning systems. Oodles of data are accessed, studied, different parameters are adjusted to improve the overall experience by providing accurate insights and calculative predictions, streamlined processes, reduced risks, and better-optimized portfolios.

You may also read about the Development Sets in Machine Learning

Machine learning has become an integral part of all businesses and that is because entrepreneurs understand its value and how it can work in great ways for them, if used to its full potential. The flourishing internet, the booming online presence along with the outsized number of connected devices, will make businesses reliable on algorithms to bring solutions to their expansive problems. This discipline of artificial intelligence will increase performance and enhance security with a substantial booth in ROI and revenue. The key is to know how to take advantage of ML to solve business problems effectively.

Be that as it may, Logic Simplified, an artificial intelligence development company in Dehradun, India has aspiring and focused machine learning programmers that are innovative and share their experience with their admirable work. The real-world cases of machine learning are heard by many if not all and we as a team walk slowly but with heavy steps to bring to the world the unheard and unseen with our unparalleled work in the field of artificial intelligence and machine learning. To know more, drop in your queries at enquiry@logicsimplified.com

Surveys of machine learning developers and data scientists have shown that the data collection and preparation steps can take up to 80% of a machine learning project's time. 

Source: SearchEnterpriseAI

A machine learning model creation step involves training the model and then testing it. It starts with an idea, according to which the raw data is collected for the model and then data processing for AI and ML algorithms takes place which converts the data into a form that can be used by the model to learn. Once the model is built to solve a specific problem, it is tested until the model gives satisfying results.

Building a model can be time consuming and asks for the right approach and methods. For that, it is essential to understand the requirements that are expected of the model and the problem that is trying to be solved.

First things first, the data collected is prepared for use by employing algorithms and appropriate techniques. It is distributed into three categories - structured, unstructured, and semi-structured data. The model is thereafter trained by using the good quality and prepared data. This exhaustive process involves selecting the right technique, hyperparameters, algorithm, whereupon configuring and tuning hyperparameters, identifying the appropriate features for best results, finally testing and evaluation of different versions of models for optimum performance and whether it meets the objective. The evaluation is done applying the validation technique and using a validation dataset which determines the model’s capability of performing once ready. Operationalizing the model is what comes next which involves measuring and monitoring its performance. Then finally what is left is to make the model adjustable so that it works best in all circumstances and iterations are also made to attain the desired results.

The technique

The Hold-out cross validation technique is a cross validation technique used to  build a computational machine learning model and it divides the algorithm into three variant subsets on which the training, tuning, model selection, and testing is carried out. Those three sets are Training set, Validation set, and Testing set. As the name suggests, the machine learning algorithm is trained using a training dataset, the trained model is validated using the validation or development dataset, and the testing dataset tests the trained and validated model. 

Based on the algorithm and the type of data the model consumes, Machine Learning has two basic types of important learning methods - Unsupervised and Supervised. Supervised learning follows predictive data analysis while on the other hand unsupervised learning works on finding data patterns.

The dataset that does carry weight

The development set is a significant dataset in the process of developing a ML model and it forms the basis of the whole model evaluation procedure. A machine learning algorithm has two parameters -  model parameters that define individual models and hyperparameters define high-level structural settings for algorithms. The development set is used to select the parameters, tune them and then use them to choose the best model of a training algorithm. Nevertheless, it also helps in avoiding or minimizing overfitting and simultaneously controls the learning rate. 

It is the quantity and quality of the dataset that determines the picking of the best performance model and it’s precision. Development sets develop machine learning solutions and help one find the best model of all the different models. It allows one to choose the number of layers (Depth), neurons per layer (width), activation function (ReLU, ELU, etc.), optimizer (SGD, Adam, etc.), learning rate, batch size, and more in the algorithm.

60-20-20 rule of thumb

The size of the dev set is 20% of the whole and that sums up to a large amount of data which is used for training and teaching the model more diverse features. The three sets that are the training set, development set and test set split the algorithm into the ratio of 60 : 20 : 20.

Errors

While the model is computationally trained, there are chances of error arising, just like in any other process. And here, the error value on the training set is called Bias while the difference between the error value on dev set and training set is called Variance. And, error is analysed by identifying  Bias and Variance. 

To choose the best model that aligns with the needs of the objective, it is necessary that there are reduced possible errors in the process. The different sort of errors that arise in the path (bias-variance trade-off) are the training error and development error. Focusing on the latter, it is measured by analysing the divergence from the value predicted. 

Different data is spent to train and test your model by feeding it to the algorithm. If you use the same data to train and test the model, in that case the model could be overfit and then the model could perform well on the training data subset but poorly on the test data and vice versa.

The development error should be the lowest so that the model comes out good. Taking that into account, the errors are analysed time and again until they are reduced to minimal. This paradigm is used to pick the best model (algorithm) that can later be used to find accuracy on the test set. It is the development set that is used to choose and tune the AI model. 

A model’s performance should have low bias and variance. And, Cross validation is a common technique that is used to balance the Bias and Variance of a model. It contributes to achieving a stable estimate of the model performance. If the dataset is not split appropriately, it can lead to extremely high Variance of the model performance. Cross-validation techniques can also be used when evaluating and mutually comparing more models, various training algorithms, or when seeking for optimal model parameters. The model must work in training as well as validation and should not be overfitted. And, a validation set or development set can be taken as a part of the training set that helps find the accuracy and efficiency of the algorithm.

Logic Simplified, a machine learning and artificial intelligence development company in India has developers with qualities that are looked for, like precision, accuracy,  someone who understands the ML ecosystem well and has the capability to build machine learning models that meet the interests of the industries and create diverse possibilities and opportunities for people. Get in touch with us and enquire all you want to avail the best services in town. Share your thoughts, enquiries and suggestions by writing to us at enquiry@logicsimplfied.com, and we will get back to you shortly to provide you with high-end Artificial Intelligence and Machine Learning solutions.