In any machine learning project, 90% of work is about data extraction, cleaning, preprocessing. This is a very challenging part of the machine learning projects. This skill is must have for any machine learning engineer.
Solve these problems to become very efficient at solving data preprocessing, cleaning, transforming, or extracting using Pandas, Python, and Numpy.
Welcome to the project on Yolov4 with OpenCV for Object Detection. In this project, we will learn how to use a YOLOv4 network pretrained on the MSCOCO dataset for object detection.
Object detection has applications in various fields, from home automation to self-driving computers. YOLOv4 is one of the recent state-of-art object detection models. This project provides an overview of how to use a YOLOv4 pretrained model.
In this topic, we will learn MongoDB and various concepts like - CRUD operations, query optimization, data modeling, aggregations, MapReduce, indexing, replication, sharding, administration and security
Kids who have been playing Roblox games would love to build Roblox games using Roblox Studio. The coding is done using Lua while making games such as Roblox Jailbreak.
Before starting this I would strongly recommend watching a couple of videos from Roblox official youtube channel: https://www.youtube.com/watch?v=0LiaEDui2vE
This playlist is going to be full of simple questions to make kids learn fundamentals of coding.
This is a Hands-On assessment to help you learn how to create a deep neural network using TensorFlow in Python. We are going to use the MNIST example to demonstrate.
There are many Big Data Solution stacks.
The first and most powerful stack is Apache Hadoop and Spark together. While Hadoop provides storage for structured and unstructured data, Spark provides the computational capability on top of Hadoop.
The second way could be to use Cassandra or MongoDB. The third could be to use Google Compute Engine or Microsoft Azure. In such cases, you would have to upload your data to Google or Microsoft which may not be acceptable to your organization sometimes.
In this post, we will understand the basics of:
This is a bite-sized course for programmers who want to learn about the tech behind AI powered search engine similar to Google are built on - Neural Search. You don't need to know about Machine Learning or Neural Networks to learn. As long as you understand few basic concepts of logic and programming, this is for you.
Concepts covered
You'll need
Welcome to the chapter on Race Conditions and Deadlocks. Here, We can understand what Race Conditions and Deadlocks are, and also practice some MCQs.
This topic will help you learn about Regular Expressions. Commonly used by string-searching algorithms for "find" or "find and replace" operations on strings, or for input validation, regular expression is a sequence of characters to easily define a search pattern.
Welcome to this project on Building a RAG Chatbot from Your Website Data using OpenAI and Langchain. In this project, we will build a RAG based end-to-end chatbot for our organization or personal use.
Skills Covered:
Welcome to this project on Deploy Image Classification Pre-trained Keras model using Flask. In this project, we will have a comprehensive understanding of how to deploy a deep learning model as a web application using the Flask framework.
Developing a machine learning or deep learning model is very important to solve problems using AI. On the other hand, it is equally important to have a knowledge of how to deploy those amazing problem-solving models into such an interface that enables the users to make use of these solutions. Even many apps we use today, like YouTube, Amazon/Flipkart shopping, FaceApps …