This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.
This comprehensive course will help you learn the basics to advanced mechanisms of chatbot development using deep learning with Python. This course is a complete package for beginners to learn chatbot fundamentals with deep learning and its applications and build it from scratch using deep learning (RNNs) with Python.
Enhance your coding skill set by learning Java multithreading and parallel programming. Figure out common problems such as debugging codes, splitting a problem into multiple independent pieces, and measuring the runtime and performance of your code. Save time and avoid going into the same pitfalls while learning multithreading.
This course for absolute beginners provides you with the opportunity to systematically learn core statistical and probability concepts, descriptive statistics, hypothesis testing, regression analysis, analysis of variance (ANOVA), and advanced regression/ML methods such as logistics regressions, polynomial regressions, decision trees, and more.
This extensive course for beginners provides the basics of chatbots with machine learning, deep learning, AWS, and its applications, building it from scratch with hands-on practice for chatbot development. This course will help you learn basic to advanced mechanisms of developing chatbots using machine learning, deep learning, and AWS with Python.
This course will help you prepare for the AI-900 Exam: Microsoft Azure AI Fundamentals. We will cover the complete exam syllabus as updated in April 2021 with sample questions.
In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNNs). You will learn how to apply CNNs to several practical image recognition datasets and learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning.
In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.
In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.
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