Goto

Collaborating Authors

Results


Python for Machine Learning with Numpy, Pandas & Matplotlib

#artificialintelligence

Some programming experience Be comfortable with coding in Python Windows/Linux/MAC machine Desire to learn data science Nothing else! It's just you, your computer and your ambition to get started today Nothing else! It's just you, your computer and your ambition to get started today Are you ready to start your path to becoming a Data Scientist or ML Engineer? This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!


Artificial intelligence to influence top tech trends in major way in next five years

#artificialintelligence

Artificial intelligence will be the common theme in the top 10 technology trends in the next few years, and these are expected to quicken breakthroughs across key economic sectors and society, the Alibaba Damo Academy says. The global research arm of Chinese technology major Alibaba Group says innovation will be extended from the physical world to a mixed reality, as more innovation finds its way to industrial applications and digital technology drives a green and sustainable future. "Digital technologies are growing faster than ever," Jeff Zhang, president of Alibaba Cloud Intelligence and head of Alibaba Damo, said in a report released on Monday. "The advancements in digitisation, 'internetisation' and intelligence are redefining a digital world that is characterised by the prevalence of mixed reality. "Digital technology plays an important role in powering a green and sustainable future, whether it is applied in industries such as green data centres and energy-efficient manufacturing, or in day-to-day activities like paperless office."


Data Science vs AI Explained for the Business - Tech Business Guide

#artificialintelligence

As an established business owner, entrepreneur or professional, you have heard many new terms for technologies and domains of study, such as data science, AI and machine learning. You might ask how data science vs ai compares and what their role in your business and life is. What is Artificial Intelligence (AI)? Which one is better for my business? In this post, we analyze data science vs AI, describing their fields of study, how they connect and what part each plays in helping you achieve the benefits of new tech.


Amazon.com: Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies: 9780262044691: Kelleher, John D., Mac Namee, Brian, D'Arcy, Aoife: Books

#artificialintelligence

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.


[100%OFF] Python For Data Science And Machine Learning

#artificialintelligence

This course offers a deep and wide range of skills set from Programming to statistics and machine learning algorithms. The skills you will attain from this course could make you an expert Data Analyst, Quality Analyst and Business Analyst and Statistical Analyst roles. Machine learning algorithms such as Regression, Clustering, Classification and prominent libraries such as Pandas, Matplotlib, SciKit -learn is covered from this course. The main goal of the course is to provide a deeper understanding and hands-on learning experience on the Data Science domain with the help of Python programming language along with real-time Data Science projects to provide an overall knowledge on Data Science domain. This course covers all the topics from Mathematics to Programming to Visualization techniques that are needed for a Data Scientist role.


How to Turn your Data Science idea into a Funded Project

#artificialintelligence

All the Data Scientists hide at least on Data Science idea in their heart. However, due to time constraints or lack of money, they do not transform their ideas into a project. In this article, I propose a strategy to turn your idea into a Data Science project. The first step towards funding your project involves writing a first draft your project. The summary is an overview of the project.


R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O and MXNet: Prakash, Dr. PKS, Rao, Achyutuni Sri Krishna: 9781787121089: Amazon.com: Books

#artificialintelligence

Dr. PKS Prakash is a Data Scientist and an author. He has spent last 12 years in developing many data science solution to solve problems from leading companies in healthcare, manufacturing, pharmaceutical and e-commerce domain. He is working as Data Science Manager at ZS Associates. ZS is one of the world's largest business services firms helping clients with commercial success, by creating data-driven strategies using advanced analytics that they can implement within their sales and marketing operations to make them more competitive, and by helping them deliver impact where it matters.


Deep demand forecasting with Amazon SageMaker

#artificialintelligence

Every business needs the ability to predict the future accurately in order to make better decisions and give the company a competitive advantage. With historical data, businesses can understand trends, make predictions of what might happen and when, and incorporate that information into their future plans, from product demand to inventory planning and staffing. If a forecast is too high, companies may over-invest in products and staff, which results in wasted investment. If the forecast is too low, companies may under-invest, which leads to a shortfall in raw materials and inventory, creating a poor customer experience. Time series forecasting is a technique that predicts future time series data based on historical data.


Crack the Data Science Interview Case study! - Analytics Vidhya

#artificialintelligence

This article was published as a part of the Data Science Blogathon. When asked about a business case challenge at an interview for a Machine learning engineer, Data scientist, or other comparable position, it is typical to become nervous. Top firms like FAANG like to integrate business case problems in their screening process these days. This approach is followed by a few other leading companies, like Uber and Twitter. Most case studies are open-minded and technical.


Exploring the ML Tooling Landscape (Part 3 of 3)

#artificialintelligence

The previous blog post in this series considered the current state of the ML tooling ecosystem and how this was reflected in ML adoption in industry. The main takeaway was the widespread use of propriety tooling amongst companies in this field, with a correspondingly diverse and splintered ML tooling market. The post ended by looking at some emerging near-term trends, highlighting the predominance of data observability and related tools, as well as the emergence of MLOps startups. This blog post will pick up from this previous thread to discuss some of the key trends in ML tooling that are likely to dominate in the near future -- or at least ones I want to talk about! As indicated in the previous blog post, I want to focus on MLOps, AutoML, and data-centric AI.