The past year has seen a massive increase in the scope of data related roles. Most aspiring data professionals tend to put a lot of focus on model building, and there is less emphasis placed on other elements of the data science lifecycle. Due to this, many data scientists are unable to work in an environment outside of a Jupyter Notebook. They are unable to get their models into the hands of an end-user, and rely on external teams to do this from them. In smaller companies that don't have a data pipeline in place, these models never see the light of day.

In this article you will learn the detail architure of'AlexNet'. It was introduced in research paper ImageNet Classification with Deep Convolutional Neural Networks by Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton in year 2010. Before introducing AlexNet the labelled image dataset was relatively small like CIFAR and NORB consisting of tens of thousands of images. However, The recent availability of large datasets like ImageNet consist 15 million labelled high-resolution images belonging to roughly 22,000 categories pushed the demand of capable deep learning algorithm. In 2010 they started training a large deep convolutional neural network to classify the 1.2 million high resolution images from the ImageNet LSVRC into 1000 different classes. Before 2010 it was easy to train model on thousand images and detect the object in the image with a low resolution, but when applied to high-resolution it was very difficult to train.

This course will be your guide to learning how to use the power of theory, math and python to create linear regression and logistic regression, two of most popular and useful machine learning models from scratch. This course is designed for folks with some programming experience or experienced developers looking to make the jump to data science and machine learning, I'll teach you how to dive deep into the math behind the linear models in an easy and understandable way. Once, you have understood the inner workings of the linear models and uncovered the black box, you are ready to code everything from the ground up without using any fancy ready made machine learning libraries and yes you will be taught that too! The course is beneficial for understanding the machine learning concepts deeply rather than just using some library to get results, it will guide you in the right direction for learning many other machine learning and deep learning algorithms, as this course covers all the basics required, you will be well on your way to becoming an expert Data Scientist! Since this course goes deep into the math and has coding from scratch, a basic to intermediate knowledge of coding is a must, also good idea of derivatives(calculus), linear algebra(matrix multiplication) and basic probability is required to get the full out of this course.

Image Processing & classification is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. We start the course by learning Scikit Image for image processing which is the essential skill required and then we will do the necessary preprocessing techniques & feature extraction to an image like HOG. After that we will start building the project. In this course you will learn how to label the images, image data preprocessing and analysis using scikit image and python. Then we will train machine learning here we will see Stochastic Gradient Descenct Classifier for image classification and followed by model evaluation proces and pipeline the machine learning model.

This Course simplifies the advanced Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTM), Gated Recurrent Units(GRU), etc. TensorFlow, Keras, Google Colab, Real World Projects and Case Studies on topics like Regression and Classification have been described in great detail. Advanced Case studies like Self Driving Cars will be discussed in great detail. Currently the course has few case studies.The objective is to include at least 20 real world projects soon. Case studies on topics like Object detection will also be included. TensorFlow and Keras basics and advanced concepts have been discussed in great detail.

Optical Character Recognition (OCR) with less than 10 Lines of Code using Python · Want to read more stories like this? It costs only 4,16$ per month. Within the area of Computer Vision is the sub-area of Optical Character Recognition (OCR), which aims to transform images into texts. OCR can be described as converting images containing typed, handwritten or printed text into characters that a machine can understand. It is possible to convert scanned or photographed documents into texts that can be edited in any tool, such as the Microsoft Word.

Machine learning is an intimidating subject. Knowing where to develop mastery around such a massive subject that encompasses so many fields, research topics, and applications can be the hardest part of the journey. Anyone with a background in programming will attest to the value of a good textbook, especially when it comes to a subject as technical as machine learning. Get a quote for an end-to-end data solution to your specific requirements. Whether you're a complete novice or a distinguished mastermind in this field, we at iMerit have compiled the best field guides, icebreakers, and referential machine learning textbooks that will suit both newcomers and veterans alike who are looking to improve their understanding of machine learning.

In this article, I've compiled a list of the best Python Data Science courses available online. I built the ranking by following a well-defined methodology that you can find below. This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks. The AI deployment is moving at a warp speed on the back of giant technological strides and growing investment in the sector. "As AI systems become increasingly more capable, it becomes critical to measure and understand the ways in which they can perpetuate harm," said Helen Ngo, an AI Index-affiliated researcher and co-author of Stanford University's AI index report 2022. With artificial intelligence becoming pervasive across all walks of life, it is critical to understand and create awareness about this technology's ethical challenges and potential risks.

ML Ops topped LinkedIn's Emerging Jobs ranking, with a recorded growth of 9.8 times in five years. Most individuals looking to enter the data industry possess machine learning skills. However, most data scientists are unable to put the models they build into production. As a result, companies are now starting to see a gap between models and production. Most machine learning models built in these companies are not usable, as they do not reach the end-user's hands.