This article is based on an in-depth study of the data science efforts in three large, private-sector Indian banks with collective assets exceeding $200 million. The study included onsite observations; semistructured interviews with 57 executives, managers, and data scientists; and the examination of archival records. The five obstacles and the solutions for overcoming them emerged from an inductive analytical process based on the qualitative data. More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1

Being a data scientist by profession and a part-time crypto trader by passion, I have been very interested in creating a Deep Learning model that could help me predict Bitcoin price. This article is based on the experimentation I did to create such a model. Long short term memory, or more popularly known as LSTM's, is a type of Recurrent Neural network that helps the model learn long-term sequences in the data set. Since my focus here is more on their usage, if you are interested in knowing more details about what LSTM's are and how they work, you can check out this great article that goes in-depth to explain all that. I imported the data onto my local drive and read it as a CSV using pandas. For this model I created fields to track the hour of the day and the weekday.

Artificial Intelligence, or AI, makes us look better in selfies, obediently tells us the weather when we ask Alexa for it, and rolls out self-drive cars. It is the technology that enables machines to learn from experience and perform human-like tasks. As a whole, AI contains many subfields, including natural language processing, computer vision, and deep learning. Most of the time, the specific technology at work is machine learning, which focuses on the development of algorithms that analyzes data and makes predictions, and relies heavily on human supervision. SMU Assistant Professor of Information Systems, Sun Qianru, likens training a small-scale AI model to teaching a young kid to recognize objects in his surroundings.

Simple linear regression is a statistical approach that allows us to study and summarize the relationship between two continuous quantitative variables. Simple linear regression is used in machine learning models, mathematics, statistical modeling, forecasting epidemics, and other quantitative fields. Out of the two variables, one variable is called the dependent variable, and the other variable is called the independent variable. Our goal is to predict the dependent variable's value based on the value of the independent variable. A simple linear regression aims to find the best relationship between X (independent variable) and Y (dependent variable).

Deep learning is that form of AI which excels in incorporating the human brain that ultimately aids in better decision-making capabilities. There are numerous applications that rely on deep learning. One such application that garnered attention from everyone across is its incorporation in AI chips. Jeff Dean, an American computer scientist and also Google's brain director had mentioned how Google would be using artificial intelligence to advance its internal development of custom chips about a year ago. This would ultimately pave the way for accelerating its software.

Human resources professionals and job seekers alike may soon be able to better understand a company's unique organizational culture thanks to a new machine-learning approach. Developed by Georgia Tech researchers, the approach is the first of its kind to computationally model organizational culture using publicly available anonymized data sources – including Glassdoor user reviews. These models are illustrated using heat maps that reveal positive and negative sentiment for a company and its business units across 41 dimensions of organizational culture. The heat maps give a "cloud-contributed" sense of what a particular workplace culture is like and can provide actionable insights to HR teams, unit managers, and job seekers, according to the researchers. "Right now, to get a measure of organizational culture, companies rely on internal surveys, which are difficult to scale. It's also unlikely that they are getting true responses given factors like organizational bias or employee concerns about anonymity," said Vedant Das Swain, a second-year Ph.D. student studying human-computer interaction at Georgia Tech.

Supervised learning is one of the most common methods of machine learning, and is particularly useful for financial predictions, fraud detection and risk assessment, among other things.

The perpetual penetration of new-age technology is demanding a need for DevOps intelligence in the entire software development lifecycle. From development to delivery, product companies have transitioned their approach. Traditional waterfall has been replaced by agile, DevOps is superseded by DevSecOps. However, it is worth noting that the roles served by Agile and DevOps are complementary. By combining the collective efforts of Agile and DevOps to incorporate CI/CD, product companies are ensuring regular software updates throughout the year rather than having just one major release.

Video AI is a growing market with lots of innovation. The Video AI market encompasses Video surveillance, Automatic/self-drive vehicles, content moderation in video, automatic video editing. Convolution Neural Networks is the backbone of Video AI in many applications and the challenge lies in training the data as well as abstracting the outcomes for better outpost. The field is still emerging and the technology is still evolving in many of these areas. As an example, even though Youtube would like to have a general understanding of the video so they know when to insert relevant ads, the technology to do that is just emerging.

All You Need Is Covered!! What you'll learn Do you want to know the best ways to clean data and derive useful insights from it? Do you want to save time and easily perform Exploratory Data Analysis(EDA)? Then this course is for you!! According to Forbes: "60% of the Data Scientist's or Data Analyst's time is spent in cleaning and organising the data..." In this course, you will not just get to know the industry level strategies but also I will practically demonstrate them for better understanding. This course aims to help beginners, as well as an intermediate data analyst, students, business analyst, data science, and machine learning enthusiasts, master the foundations of confidently working with data in the real world.