NLP with SpaCy Learning Path

A skill track on Data Camp that focuses on natural language processing (NLP). It covers techniques for text classification, sentiment analysis, and other NLP tasks, providing a solid foundation in this specialized area of AI.

Welcome to "Machine Learning: Modern Computer Vision & Generative AI," a cutting-edge course that explores the exciting realms of computer vision and generative artificial intelligence using the KerasCV library in Python. This course is designed for aspiring machine learning practitioners who wish to explore the fusion of image analysis and generative modeling in a streamlined and efficient manner.Course Highlights:KerasCV Library: We start by harnessing the power of the KerasCV library, which seamlessly integrates with popular deep learning backends like TensorFlow, PyTorch, and JAX. KerasCV simplifies the process of writing deep learning code, making it accessible and user-friendly.Image Classification: Gain proficiency in image classification techniques. Learn how to leverage pre-trained models with just one line of code, and discover the art of fine-tuning these models to suit your specific datasets and applications.Object Detection: Dive into the fascinating world of object detection. Master the art of using pre-trained models for object detection tasks with minimal effort. Moreover, explore the process of fine-tuning these models and learn how to create custom object detection datasets using the Label Img GUI program.Generative AI with Stable Diffusion: Unleash the creative potential of generative artificial intelligence with Stable Diffusion, a powerful text-to-image model developed by Stability AI. Explore its capabilities in generating images from textual prompts and understand the advantages of KerasCV's implementation, such as XLA compilation and mixed precision support, which push the boundaries of generation speed and quality.Course Objectives:Develop a strong foundation in modern computer vision techniques, including image classification and object detection.Acquire hands-on experience in using pre-t

Are you an HR professional or aspiring HR manager eager to master ChatGPT and AI for Human Resource Management activities?Are you looking to gain a future-ready skill that can help you automate workflows, improve employee engagement, and drive HR innovation?If yes, this course is tailored just for you!Why Take This Course?In today’s digital era, AI is rapidly transforming how Human Resources functions. From recruitment to onboarding, performance management to HR analytics — AI is at the heart of Next Gen HR practices.This course, “Hands-on Generative AI for HR Professionals | ChatGPT | AI,” offers a comprehensive, beginner-friendly, and hands-on learning experience to help you unlock the true potential of ChatGPT and AI tools in your HR role.Latest Curriculum updated as of September 2025.What You Will Learn – Course Curriculum Overview:Section 1: Introduction to NLP and ChatGPT Architecture Introduction to Natural Language Processing (NLP)Practical NLP activity and understanding ChatGPT architecture Section 2: Getting Started with ChatGPT for HR Professionals Basics of ChatGPT Real-world use cases you’ve never heard before Custom GP Ts for HR ChatGPT integrations & AP Is Introduction to Prompt Engineering Section 3: Chat Bot & API Integration for Automating HR Activities Step-by-step setup of ChatGPT chatbots for HR functions Automating tasks using chatbot and website integrations Section 4: Access to The Next Gen HR Reporter – Monthly Newsletter Download HRM newsletters from March to July 2025Stay updated on global HR tech trends and use cases Section 5: Uses

The demand for Big Data Hadoop Developers, Architects, Data Scientists, Machine Learning Engineers is increasing day by day and one of the main reason is that companies are more keen these days to get more accurate predictions & forecasting result using data. They want to make sense of data and wants to provide 360 view of customers thereby providing better customer experience. This course is designed in such a way that you will get an understanding of best of both worlds i.e. both Hadoop as well as Data Science. You will not only be able to perform Hadoop related operations to gather data from the source directly but also they can perform Data Science specific tasks and build model on the data collected. Also, you will be able to do transformations using Hadoop Ecosystem tools. So in a nutshell, this course will help the students to learn both Hadoop and Data Science Natural Language Processing in one course. Companies like Google, Amazon, Facebook, Ebay, Linked In, Twitter, and Yahoo! are using Hadoop on a larger scale these days and more and more companies have already started adopting these digital technologies. If we talk about Text Analytics, there are several applications of Text Analytics (given below) and hence companies prefer to have both of these skillset in the professionals. One of the application of text classification is a faster emergency response system can be developed by classifying panic conversation on social media.Another application is automating the classification of users into cohorts so that marketers can monitor and classify users based on how they are talking about products, services or brands online.Content or product tagging using categories as a way to improve browsing experience or to identify related content on the website. Platforms such as news agencies, directories, E-commerce, blogs, content curators, and likes can use automated technologies to classify and tag content a

Who Is This Course For?Are you curious about Artificial Intelligence but overwhelmed by buzzwords like NLP, Machine Learning, Deep Learning, and Generative AI? Do you want to understand AI without getting lost in technical jargon? If so, this course is perfect for you! No prior experience required! Whether you're a complete beginner, a student exploring AI for the first time, a professional looking to future-proof your career, or simply someone fascinated by how AI is shaping the world—you’ll walk away with clear, practical knowledge that puts you ahead of the curve.Why This Course?AI is no longer the future—it’s the present! Companies worldwide are integrating AI into their businesses, and those who understand it have a massive advantage in any industry. This course will give you a strong foundation in AI concepts without requiring coding or advanced mathematics. You’ll not only understand what AI is but also how it works and where it's headed, making you confident in conversations, career choices, and future learning.   What makes this course special?Clarity over complexity – AI explained in simple, beginner-friendly terms.Practical relevance – Understand AI's real-world applications and how it's shaping industries.Guided learning roadmap – Follow Andrew Ng’s structured approach to mastering AI after this course.Don’t miss out on the AI revolution! Start learning today and gain the knowledge that will set you apart in the world of tomorrow. Enroll now!

Sevgili arkadaşlar merhaba,NLP’nin temel kavramlarını ve modern dil modellerinin gelişimini keşfetmeye hazır mısınız? Bu kursta, NLP’nin başlangıcından Büyük Dil Modelleri’nin (LL Ms) yükselişine kadar olan süreci adım adım ele alacağız.Kurs, temel bilgilerden karmaşık modellere doğru bir öğrenme deneyimi sunacak şekilde tasarlandı. Amacımız, NLP’nin gelişimini sade ve anlaşılır bir şekilde aktararak, teorik altyapıyı en iyi şekilde kavramanızı sağlamak. NLP’nin evrimindeki her adımı sırasıyla inceleyecek ve bu alandaki kavramları derinlemesine öğreneceksiniz. Eğer bu alanda yeniyseniz veya mevcut bilginizi derinleştirmek istiyorsanız, bu kurs sizin için ideal!Eğitimin içerisinde Doğal Dil İşlemenin evrim basamaklarını aşağıdaki sıra ile öğreneceğiz:KURAL TABANLI NLP (RULE-BASED NLP)İSTATİSTİKSEL NLP (STATISTICAL NLP)FREKANS TABANLI METİN VEKTÖRİZASYONU (FREQUENCY-BASED TEXT VECTORIZATION)BAĞLAMDAN BAĞIMSIZ STATİK TEMSİLLER (CONTEXT-INDEPENDENT STATIC EMBEDDINGS)BAĞLAMLAŞTIRILMIŞ TEMSİLLER (CONTEXTUALIZED EMBEDDINGS)BU KURS SİZE NE KAZANDIRACAK?NLP'nin temel kavramlarından LLM'lere uzanan yolculuğu anlama fırsatı.Büyük Dil Modellerinin (LL Ms) temel mantığı, mimarisi ve uygulama alanları hakkında detaylı bilgi.NLP projelerinde kullanabileceğiniz pratik ve teorik bilgi.Güncel NLP araçları ve modelleriyle çalışma becerisi.Kurs; Makine Öğrenimi, Derin Öğrenme, Doğal Dil İşleme ve Büyük Dil Modelleri (LL Ms) alanlarında uzmanlaşan Musa Bayır tarafından hazırlanmıştır. Eğer NLP ve Büyük Dil Modelleri (LL Ms) hakkında kapsamlı teorik bilgi edinmek ve bu alandaki bilginizi derinleştirmek istiyorsanız hemen kayıt olun.

Practice questions to prepare for Generative AI LL Ms Associate (NCA-GENL)!This certification is designed to validate foundational knowledge and practical skills in working with large language models (LL Ms) and generative AI. This certification is ideal for professionals aiming to develop expertise in deploying and managing LLM-based solutions. Key focus areas include understanding transformer-based architectures, prompt engineering techniques for guiding model responses, and leveraging modern pretrained models to solve a range of natural language processing (NLP) tasks, such as text generation, token classification, and sentiment analysis. The certification covers best practices for working with human-labeled data and strategies for optimizing models for specific applications. This certification is ideal for those looking to strengthen their understanding of generative AI and advanced technologies within the rapidly evolving AI landscape.About the course Prepare yourself for success in the Generative AI LL Ms certification with this comprehensive mock exam course. This course is specifically designed to help you master the key concepts and skills needed to excel in the rapidly growing field of Generative AI, focusing on Large Language Models (LL Ms).This course features six carefully crafted mock exams that closely mirror the format, difficulty, and scope of the actual certification exam. Each mock exam contains a diverse set of questions that test your knowledge on various topics, including the fundamentals of Generative AI, architecture and deployment of LL Ms, model training and fine-tuning, ethical considerations, and specific tools and platforms for AI development.What sets this course apart is the detailed explanations provided for each question. After completing each exam, you will not only see which answers you got right or wrong but also receive in-depth explanations that clarify why certain answers are correct. This approach

Data Scientist is amongst the trendiest jobs, Glassdoor ranked it as the 1 Best Job in America in 2018 for the third year in a row, and it still holds its 1 Best Job position. Python is now the top programming language used in Data Science, with Python and R at 2nd place. Data Science is a field where data is analyzed with an aim to generate meaningful information. Today, successful data professionals understand that they require much-advanced skills for analyzing large amounts of data.  Rather than relying on traditional techniques for data analysis, data mining and programming skills, as well as various tools and algorithms, are used. While there are many languages that can perform this job, Python has become the most preferred among Data Scientists.Today, the popularity of Python for Data Science is at its peak. Researchers and developers are using it for all sorts of functionality, from cleaning data and Training models to developing advanced AI and Machine Learning software. As per Statista, Python is Linked In's most wanted Data Science skill in the United States.Data Science with R, Python and Spark Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R, Python and Spark. Data Science Trainingencompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introductionto Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR.Curriculum Introduction to Data Science Learning Objectives - Get an introduction to Data Science in this module and see how Data Sciencehelps to analyze large and unstructured data with different tools.Topics:What is Data Science? What does Data Science involve?Era of Data Science Business Intelligence vs Data Science Life cycle

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today Let’s parse that. The course is down-to-earth : it makes everything as simple as possible - but not simpler The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff. You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is. The course is very visual : most of the techniques are explained with the help of animations to help you understand better. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall. What's Covered: Machine Learning: Supervised/Unsu