Then this course is for you!! This course has been practically and carefully designed by industry experts to offer the best way of learning Data Science and Machine Learning the practical way with hands-on projects throughout the course. This course will help you learn complex Data Science concepts and machine learning algorithms the practical way for easier understanding. We will walk you through step-by-step on each topic explaining each line of code for your understanding. There is going to be a lot of fun, exciting, and robust projects to better understand each concept under each topic.

Machine learning specialized libraries and frameworks are available in a large number of Python distributions, making the development process easier and decreasing development time. Python's straightforward syntax and readability enable it to be used for fast testing of complicated algorithms while also making it accessible to those who are not programmers.

Highlights: In Machine Learning, a backpropagation algorithm is used to compute the loss for a particular model. The most common starting point is to use the techniques of single-variable calculus and understand how backpropagation works. However, the real challenge is when the inputs are not scalars but of matrices or tensors. In this post [1], we will learn how to deal with inputs like vectors, matrices, and tensors of higher ranks. We will understand how backpropagation with vectors and tensors is performed in computational graphs using single-variable as well as multi-variable derivatives.

This specialization program is especially dedicated to statistics. In this program, you will learn basic and intermediate concepts of statistical analysis using the Python programming language. In this program, you will learn the following topics- where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. Along with that, you will work on a variety of assignments that will help you to check your knowledge and ability. This specialization program is a 3-course series. Let's see the details of the courses-

Welcome to the comprehensive course on Master Artificial Intelligence Step-by-Step Guide for 2021. R Tutor is a team of software applications training professionals who explain complex information in the simplest form with relevant examples. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. Artificial Intelligence can provide humans a great relief from doing various repetitive tasks.

Text classification is the task of assigning a sentence or document an appropriate category. The categories depend on the selected dataset and can cover arbitrary subjects. Therefore, text classifiers can be used to organize, structure, and categorize any kind of text. Common approaches use supervised learning to classify texts. Especially BERT-based language models achieved very good text classification results in recent years.

This is step by step course on how to create predictive model using machine learning. It covers Numpy, Pandas, Matplotlib, Scikit learn and Django and at the end predictive model is deployed on Django. Most of things machine learning beginner do not know is how they can deploy a created model. How to put created model into application? Training model and getting 80%, 85% or 90% accuracy does not matter. As Artificial Intelligence Engineer you should be able to put created model into application.

Aside from the real fact that Data Science is one of the highest-paid, hottest and most popular fields today, it's also somehow worth noting that it will certainly remain kind of innovative and also challenging for another decade or more as well. Data science is unquestionably one of the most in-demand professions right now. Data Science job openings abound in the global market, with enticing compensation packages from reputable employers. Companies are hiring data scientists across the board (many of which have data science departments). For ambitious data scientists all across the world, prestigious educational institutes are offering exclusive curriculum, including online diploma courses.

Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You'll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You'll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Improved techniques and new methods around parsing and processing text are discussed as well. There is also a chapter dedicated to semantic analysis where you'll see how to build your own named entity recognition (NER) system from scratch.