## How to Become a Successful Machine Learning Engineer

Machine learning is a rapidly growing field that offers a wide range of opportunities for those who have the right skills and qualifications. As a machine learning engineer, you will be responsible for designing and implementing machine learning models that can help organizations make data-driven decisions. If you’re interested in Read more…

## Interview Questions and Answers on TF-IDF in NLP and Machine Learning

What is TFIDF? TF-IDF, short for term frequency-inverse document frequency, is a numerical statistic that is used to reflect how important a word is to a document in a corpus of documents. It is commonly used in natural language processing and information retrieval tasks, such as document classification and search Read more…

## Top 10 Interview Questions and Answers for MLOps Engineers

MLOps, or Machine Learning Operations, is the practice of combining machine learning and operations to enable the rapid, reliable, and secure development, deployment, and management of machine learning models. MLOps aims to streamline the process of building and deploying machine learning models in a production environment, by automating and optimizing Read more…

## Feature Engineering Tutorial Series 6: Variable magnitude

Does the magnitude of the variable matter? In Linear Regression models, the scale of variables used to estimate the output matters. Linear models are of the type y = w x + b, where the regression coefficient w represents the expected change in y for a one unit change in x Read more…

## Feature Engineering Tutorial Series 5: Outliers

An outlier is a data point which is significantly different from the remaining data. “An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism.” [D. Hawkins. Identification of Outliers, Chapman and Hall , 1980.] Should Read more…

## Feature Engineering Tutorial Series 4: Linear Model Assumptions

Linear models make the following assumptions over the independent variables X, used to predict Y: There is a linear relationship between X and the outcome Y The independent variables X are normally distributed There is no or little co-linearity among the independent variables Homoscedasticity (homogeneity of variance) Examples of linear Read more…

## Feature Engineering Series Tutorial 3: Rare Labels

Labels that occur rarely Categorical variables are those whose values are selected from a group of categories, also called labels. Different labels appear in the dataset with different frequencies. Some categories appear more frequently in the dataset, whereas some other categories appear only in a few number of observations. For Read more…

## Feature Engineering Series Tutorial 2: Cardinality in Machine Learning

Cardinality refers to the number of possible values that a feature can assume. For example, the variable “US State” is one that has 50 possible values. The binary features, of course, could only assume one of two values (0 or 1). The values of a categorical variable are selected from Read more…

## Feature Engineering Series Tutorial 1: Missing Values and its Mechanisms

Missing data, or missing values, occur when no data / no value is stored for certain observations within a variable. Incomplete data is an unavoidable problem in most data sources, and may have a significant impact on the conclusions that can be derived from the data. Why is data missing? The source of missing Read more…

## Types of Data types every Data Scientist should know

One of the central concepts of data science is gaining insights from data. Statistics is an excellent tool for unlocking such insights in data. In this post, we’ll see some basic types of data(variable) which can be present in your dataset. What is a Variable? A variable is any characteristic, Read more…

## Data Visualization with Pandas

Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed. pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, in Python programming Read more…

## Resume and CV Summarization

Resume NER Training In this blog, we are going to create a model using SpaCy which will extract the main points from a resume. We are going to train the model on almost 200 resumes. After the model is ready, we will extract the text from a new resume and Read more…

## Word Embedding and NLP with TF2.0 and Keras on Twitter Sentiment Data

Word Embedding and Sentiment Analysis What is Word Embedding? Natural Language Processing(NLP) refers to computer systems designed to understand human language. Human language, like English or Hindi consists of words and sentences, and NLP attempts to extract information from these sentences. Machine learning and deep learning algorithms only take numeric Read more…

## Amazon and IMDB Review Sentiment Classification using SpaCy

Sentiment Classification using SpaCy What is NLP? Natural Language Processing (NLP) is the field of Artificial Intelligence concerned with the processing and understanding of human language. Since its inception during the 1950s, machine understanding of language has played a pivotal role in translation, topic modeling, document indexing, information retrieval, and Read more…

## Multi-Label Image Classification on Movies Poster using CNN

Multi-Label Image Classification in Python In this project, we are going to train our model on a set of labeled movie posters. The model will predict the genres of the movie based on the movie poster. We will consider a set of 25 genres. Each poster can have more than Read more…

## 2D CNN in TensorFlow 2.0 on CIFAR-10 – Object Recognition in Images

What is CNN This Notebook demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images. Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. Unlike traditional multilayer perceptron architectures, it uses two operations Read more…

## Breast Cancer Detection Using CNN

Breast Cancer Detection Using CNN in Python Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2 million new cases in 2018, making it a significant health problem in present days. The key challenge in breast cancer detection is Read more…

## Credit Card Fraud Detection using CNN

Classification using CNN It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. In this project we are going to build a model using CNN which predicts if the transaction is genuine Read more…

## Real-Time Sentiment Analysis of a Phone Call Using NLTK and TextBlob in Python

Speech to text conversion and real-time sentiment analysis In this project we are going to analyse the sentiment of the call. We are first going to convert the speech to text and the analyse the sentiment using TextBlob. TextBlob is a Python library for processing textual data. It provides a simple API Read more…

## NLP: End to End Text Processing for Beginners

Complete Text Processing for Beginners Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value can be extracted from it. In theory, we can understand Read more…