Search Results for 'sentiment' (60 products)

Do you want to learn how to perform sentiment analysis? The answer should almost always be yes if you are working in any business domain. Every company on the face of the earth wants to know what its customers feel about its products and services — and sentiment analysis is the easiest and most accurate way of finding out the answer to this question. By learning to perform sentiment analysis, you will make yourself invaluable to any company, especially those who are interested in quality assurance of their products and those working with business intelligence; and this is almost all sensible companies, large and small, nowadays. In this course, we make it easy to perform sentiment analysis. In the very first video, we introduce a sentiment analysis engine of fewer than 60 lines that can perform industry-grade sentiment analysis. We then spend the rest of the course explaining these very powerful 60 lines so that you have a thorough understanding of the code. After you are done with this course, you will immediately be able to plug this system into your existing pipelines to perform sentiment analysis of any text you can throw at it. That is one of the reasons you should use Python for sentiment analysis and not some other data science language such as R. If you work with R for sentiment analysis, you still have to put in a lot of effort to take this skill to the market. If you write your sentiment analysis engine in Python, incorporating your code into your final business product is dead easy. The second important tip for sentiment analysis is that the latest success stories do not try to do it by hand. Instead, you train a machine to do it for you. That is why we use deep sentiment analysis in this course: you will train a deep-learning model to do sentiment analysis for you. That way, you put in very little effort and get industry-standard sentiment analysis — and you can improve your engine later by simply utilizing a better model as soon as it becomes available with little effort. All the code files are placed at https://github.com/PacktPublishing/Sentiment-Analysis-through-Deep-Learning-with-Keras-and-Python

This course begins with a Python crash course and then guides you on setting up Microsoft Windows-based PCs, Linux desktops, and Macs. After the setup, we delve into machine learning, AI, and data mining techniques, which include deep learning and neural networks with TensorFlow and Keras; generative models with variational autoencoders and generative adversarial networks; data visualization in Python with Matplotlib and Seaborn; transfer learning, sentiment analysis, image recognition, and classification; regression analysis, K-Means Clustering, Principal Component Analysis, training/testing and cross-validation, Bayesian methods, decision trees, and random forests. Additionally, we will cover multiple regression, multilevel models, support vector machines, reinforcement learning, collaborative filtering, K-Nearest Neighbors, the bias/variance tradeoff, ensemble learning, term frequency/inverse document frequency, experimental design, and A/B testing, feature engineering, hyperparameter tuning, and much more! There's a dedicated section on machine learning with Apache Spark to scale up these techniques to "big data" analyzed on a computing cluster. The course will cover the Transformer architecture, delve into the role of self-attention in AI, explore GPT applications, and practice fine-tuning Transformers for tasks such as movie review analysis. Furthermore, we will look at integrating the OpenAI API for ChatGPT, creating with DALL-E, understanding embeddings, and leveraging audio-to-text to enhance AI with real-world data and moderation.

This course begins with a warm welcome and an overview of the curriculum, setting the stage for an exciting journey into the world of Natural Language Processing (NLP). The initial section ensures you are well-prepared, guiding you on how to access the necessary code and providing tips for succeeding in the course. We delve deep into the realm of Transformers, starting from the basics of Recurrent Neural Networks (RNNs) and advancing to the attention mechanisms that power modern NLP models. The course covers a wide range of practical applications, including sentiment analysis, text generation, embeddings, semantic search, and named entity recognition. Each concept is paired with hands-on Python tutorials, enabling you to implement these techniques in real-world scenarios. By the end of this course, you will have a solid understanding of various NLP tasks and how to approach them using Hugging Face's powerful library. Whether you are analyzing sentiment, summarizing text, or translating languages, this course equips you with the skills to tackle these challenges efficiently. The comprehensive coverage ensures you gain both theoretical knowledge and practical experience, making you proficient in applying Transformers to solve complex NLP problems.

Embark on a journey through the world of text analytics with our expertly crafted course. Starting with an introduction and outline, you'll discover the foundational concepts and receive a special offer to get you started. The initial sections guide you through the setup process, ensuring you have all the resources and tips needed for success. Delve into the core of text analytics with dedicated sections on spam detection, sentiment analysis, text summarization, topic modeling, and latent semantic analysis. Each section begins with a problem description, followed by intuitive explanations of algorithms like Naive Bayes, logistic regression, and Latent Dirichlet Allocation. You'll engage with practical exercises designed to reinforce your understanding, and apply these techniques using Python in a hands-on manner. The course culminates with advanced topics and comprehensive summaries, ensuring you grasp both the theoretical and practical aspects of text analytics. By the end, you'll have a robust understanding of various NLP techniques and the confidence to apply them in real-world scenarios. This course is an essential resource for technical professionals looking to excel in the rapidly evolving field of natural language processing.