Machine Learning


Before diving deep into Digital Transformation with Machine Learning, let’s understand more about Machine Learning
What is Machine Learning?
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
Machine Learning Solutions
Difference between Machine Learning and Data Mining
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as “unsupervised learning” or as a preprocessing step to improve learner accuracy.

Applications of Machine Learning



  • Improve diagnoses and treatment
  • Identify trends


  • Detect patterns of fraud
  • Identification of insights in data
  • Money laundering activities


  • Improve sales forecasting
  • Interpret customer data
  • Sales communication


  • Quick decision on quality control
  • Improved supply chain accuracy
  • Reductions in planning and scheduling costs


  • Production forecasting
  • Calculated risk growing crops
  • Early identification


  • Increasing Bottom line
  • Increase in revenue generation
  • To gain the competency


  • Predictive performance for advertisement
  • Personalization customer choices
  • Predict churn and lifetime value

Use Cases

Outlier Detection

Many machine learning algorithms are sensitive to the range and distribution of attribute values in the input data. Outliers in input data can skew and mislead the training process of machine learning algorithms resulting in longer training times, less accurate models and ultimately poorer results.Even before predictive models are prepared on training data, outliers can result in misleading representations and in turn misleading interpretations of collected data. Outliers can skew the summary distribution of attribute values in descriptive statistics like mean and standard deviation and in plots such as histograms and scatterplots, compressing the body of the data. Finally, outliers can represent examples of data instances that are relevant to the problem such as anomalies in the case of fraud detection and computer security.

2) Natural Language Processing(NLP)

Natural Language Processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve speech recognition, natural language understanding, natural language generation (frequently from formal, machine-readable logical forms), connecting language and machine perception, dialog systems, or some combination thereof.

3) Recommender System

Recommender Systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things they have never experienced. The technology behind recommender systems has evolved over the past 20 years into a rich collection of tools that enable the practitioner or researcher to develop effective recommenders. Such systems are being used by companies such as Amazon, Facebook, Netflix, LinkedIn, Quora, Udemy, New York Times, etc. By taking this course, you will learn the most important of those tools, including how they work, how to use them, how to evaluate them, and their strengths and weaknesses in practice. The algorithms you will study include popularity-based systems, classification-based approach, collaborative filtering, matrix recommendation, etc.

4) Sentimental Analysis

Sentimental Analysis with the use of Natural language processing will help you identify text into productive reviews by providing positive, negative or neutral categories. These inputs can assist you to construct decisions related to the product roadmap, design positioning etc.   The sentimental analysis uses a neural network that helps the system understand the subtle value and does not just analyses the text at face value, but creates an abstract representation of what the neural network learns.   For example – A company may update an application and to further predict if the customer’s reaction is happy or unhappy with the current update; sentiment analysis aims to dictate the attitude of a speaker, writer etc with respect to some topic or the overall contextual contradiction or emotional reaction to a document, event etc. Customer review plays an important role in various industries- be it manufacturing, agriculture or education.

Machine Learning Architecture

Our Partners

Amazon Machine Learning

Amazon Machine Learning (AML) offers companies an easy, highly-scalable on-ramp for interpreting data. Under the umbrella of Amazon Web Services (AWS). Mindbowser can help you exploit the power of its visualization tools, wizards and manage your Amazon infrastructure.

Google Machine Learning

Mindbowser helps enterprise setup their frameworks to leverage the full potential of Google’s machine learning algorithms that analyze data and predict results. We can help you derive datasets from Google cloud storage or Google BigQuery.

Azure Machine Learning

Mindbowser helps you leverage the full potential of the cloud-based predictive analytics service, Azure Machine Learning to build data-driven applications to predict, forecast and change future outcomes.