Machine Learning

TensorFlow Quantum: beauty and the beast

Reading Time: 4 minutes So, we are finally here, after a long wait, we are going to be in an era of quantum computing. TFQ, the beauty of TensorFlow and beast nature of quantum computing. Quantum computing is becoming a technology to observe more closely in 2020. We have seen some recent announcements from Honeywell, Google and others, it’s worth looking forward to new pieces of hardware coming this year. Now, Google has Continue Reading

MachineX: Sentiment analysis with NLTK and Machine Learning

Reading Time: 9 minutes In this blog, we are going to see how we can NLP library NLTK for sentiment analysis. Sentiment Analysis is a common NLP task nowadays. Every data scientist or a person working on data science needs to perform. Introduction to NLP Natural Language processing Natural Language Processing (NLP) is a subfield of artificial intelligence that helps computers understand human language. NLP enables machines to derive Continue Reading

MachineX: Anticipate Customer behavior for Retailing

Reading Time: 4 minutes In this blog, we are going to see the power of Customer behavior Anticipation and how it can derive the success of the retail sector. Nowadays, Machine learning is playing an important in the success of different sectors. we can talk about Healthcare, Finance, Manufacturing, Agriculture, now even in Education. Retail is one of the sectors, which is getting huge benefits from machine learning and Continue Reading

MachineX: Demystifying Market Basket analysis

Reading Time: 7 minutes In this blog, we are going to see how we can Anticipate customer behavior with Market Basket analysis By using Association rules. Introduction to Market Basket analysis Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it Continue Reading

MachineX: The Power of Recommendation Engines

Reading Time: 4 minutes In this blog, we are going to talk about, what actually Recommendation Engines is and different types of same. You can see the full webinar, related to this blog here : Recommender Engines or Systems is one of the most mainstream utilization of data science today. They are utilized to predict the “rating” or “preference” that a user would provide for a thing. Pretty much Continue Reading

MachineX: Heart Diseases detection using Machine Learning

Reading Time: 4 minutes In this blog, we will be going to see how we can use machine learning and data science to detect or to predict potential Heart Diseases. Introduction Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmias) and heart defects you’re born with (congenital heart Continue Reading

MachineX: Evaluation Metrics for Classification Models

Reading Time: 5 minutes In our last blog post, we have looked at various evaluation metrics for the regression model. Continuing on this we will take a look at the evaluation metrics used for classification models. Classification is about predicting class labels given input data. In binary classification, there are two possible output classes whereas in Multi-class classification we have more than two possible output classes. We are going Continue Reading

Boosting medical diagnosis with Klickare

Reading Time: 4 minutes In this blog, we are going to see how KlicKare can boost up medical diagnosis by using deep learning. Medical diagnostics are a category of medical tests designed to detect infections, conditions, and diseases. These medical diagnostics fall under the category of in-vitro medical diagnostics (IVD) which be purchased by consumers or used in laboratory settings. Biological samples are isolated from the human body such Continue Reading

MachineX: Top 10 data Science use cases in Retail

Reading Time: 8 minutes In this blog, we will see some of the data science use cases in Retail industries and how it is transforming the customer experience. We are all aware of the troves of data, retail businesses generate on a daily basis. However, this repository of critical data is worthless if it cannot be translated into valuable insights into the consumer’s minds or market trends. While all Continue Reading

MachineX: Alphabets of PyTorch (Part 1)

Reading Time: 6 minutes Overview In this blog, you’ll get an introduction to deep learning using the PyTorch framework, we will see some basics of PyTorch. Introduction to PyTorch PyTorch is a Python machine learning package based on Torch, which is an open-source machine learning package based on the programming language Lua. Two main features: Tensor computation (like NumPy) with strong GPU acceleration Automatic differentiation for building and training Continue Reading

MachineX: k-Nearest Neighbors(KNN) for Regression

Reading Time: 5 minutes Introduction K Nearest Neighbor Regression (KNN) works in much the same way as KNN for classification. The difference lies in the characteristics of the dependent variable. With classification KNN the dependent variable is categorical. With regression KNN the dependent variable is continuous. Both involve the use neighboring examples to predict the class or value of other examples. In this blog we will understand the basics Continue Reading

MachineX: Genetic Algorithm

Reading Time: 2 minutes Genetic algorithm is based on the Charles Darwin famous principle of survival of the fittest, where the fittest of the individuals are given higher importance and are chosen for reproduction in order to produce children for the new generation. The process starts by selecting the fittest individuals from a population, who then produce offspring which inherit the characteristics of the parents. Since the parents already Continue Reading

MachineX: Evaluation Metrics for a Regression ML Model

Reading Time: 3 minutes In this blog post, we will quickly look at the various metrics to evaluate our regression models. But first, let us briefly discuss one of the best-known model evaluation approach we use which is Train-Test or also known as Train-Validation split. Train-Test Split: In this approach, we split the data into two parts known as Training set and Test set. The model is then trained Continue Reading