This course teaches you how to perform various data science tasks using Java. JavaServer Pages (JSP) is a technology for developing Webpages that supports dynamic content. This helps developers insert java code in HTML pages by making use of special JSP tags, most of which start with % and end with % . A JavaServer Pages component is a type of Java servlet that is designed to fulfill the role of a user interface for a Java web application. Web developers write JSPs as text files that combine HTML or XHTML code, XML elements, and embedded JSP actions and commands.

PySpark is an interface for Apache Spark in Python. PySpark is often used for large-scale data processing and machine learning. We just released a PySpark crash course on the freeCodeCamp.org Krish is a lead data scientist and he runs a popular YouTube channel. Apache Spark is written in the Scala programming language. To support Python with Spark, the Apache Spark community released a tool called PySpark. PySpark allows people to work with Resilient Distributed Datasets (RDDs) in Python through a library called Py4j. PiSpark is an interface for Apache Spark in Python is often used for large scale data processing and machine learning. Krish knack teaches this course. So we are going to start Apache Spark series. And specifically, if I talk about Spark, we will be focusing on how we can use spark with Python. So we are going to discuss about the library called pi Spark, we will try to understand everything why spark is actually required. And probably will also try to cover a lot of ...

This video will help you leverage the power of TensorFlow to perform advanced image processing. TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. This video will help you leverage the power of TensorFlow to perform advanced image processing. This course is a continuation of the Intro to Computer Vision course, building on top of the skills learned in that course. In this course, you'll dive deeper as we cover more advanced computer vision concepts.

For building any product, whether it includes ML or not, the first step is to identify the problem you're trying to solve. ML is a great tool for solving some problems, but there are many where it's best to start simpler. In this post, let's consider working for a company building a hypothetical product for automatically transcribing university lectures. We're going to build an automatic speech recognition (ASR) system which is tuned to work well for lectures -- this is something that definitely needs machine learning at its core. The product team have decided to start small and focus initially on just Physics lectures as a proof of concept.

Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab. This is one of the most exciting courses I've done and it really shows how fast and how far deep learning has come over the years. When I first started my deep learning series, I didn't ever consider that I'd make two courses on convolutional neural networks. I think what you'll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.

NLP is literally the processing of textual data to gain insights from it. In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff.

The topics outlined below are taught using practical Python projects!

People who want to learn about machine learning and deep learning will work on five real-world projects. 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!

Gain a competitive edge in the world of Computer Vision through this course by learning how to do Smart Data Extraction from Pdf and Images. Gain a competitive edge in the world of Computer Vision through this course by learning how to do Smart Data Extraction from Pdf and Images. The technology landscape of world has brought in cognitive skills at the forefront where major emphasis is on intelligent data extraction. This becomes more complex due to the huge variety of input documents such as pdf document with structured data, scanned pdf document and word document. This course aims to solve this challenging problem by helping you to understand these various formats and then empower you to do smart data extraction using Python, Pandas, OCR, Tesseract, PyTesseract, OpenCV, Spacy and NER concepts.

Understand how to overcome RPA Implementation Challenges · Have a good grasp on how to Prepare and Launch a RPA project · Identify how to select the right process ... Robotics Process Automation(RPA) is changing the way we work and has become an integral element for any company who is thinking about digital transformation. This market is growing at a rapid pace and professionals need to evolve to become a part of this intelligent digital workforce. Do you want to be in the smart crowd and take the RPA Path? Then, this course is for you as it aims to answer the Why, What and How of RPA Implementation by adopting a step-by-step approach. We will teach you the essentials of RPA, explain how to overcome RPA Challenges like Process Analysis, Process Identification, Building an RPA team for RPA Managers, Business Case Issues and ROI Analysis through use cases.