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In this course, we are going to learn about natural language processing. We will discuss various concepts such as tokenization, stemming, and lemmatization to process text. We will then discuss how to build a Bag of Words model and use it to classify text. We will see how to use machine learning to analyze the sentiment of a given sentence. We will then discuss topic modeling and implement a system to identify topics in a given document. We will start with simple problems in NLP such as Tokenization Text, Stemming, Lemmatization, Chunks, Bag of Words model. and we will build some real stuff such as : 1. Learning How to Represent the Meaning of Natural Language Text 2. Building a category predictor to predict the category of a given text document. 3. Constructing a gender identifier based on the name. 4. Building a sentiment analyzer used to determine whether a movie review is positive or negative. 5. Topic modeling using Latent Dirichlet Allocation 6. Feature Engineering 7. Dealing with corpora and WordNet 8. Dealing With your Vocabulary for any NLP and ML model TIPS (for getting through the course): - Take handwritten notes. This will drastically increase your ability to retain the information. - Ask lots of questions on the discussion board. The more the better! - Realize that most exercises will take you days or weeks to complete. - Write code yourself, don’t just sit there and look at my code. You don't know anything about NLP? let's break it down! I am always available to answer your questions and help you along your data science journey. See you in class! NOTICE that This course will be modified and I will add new content and new concepts from one time to another, so stay informed! :)

Embark on an extraordinary educational journey designed to transform engineers into world-class AI researchers. This comprehensive course is crafted to equip participants with the knowledge, skills, and strategic mindset needed to excel in the dynamic field of artificial intelligence research and make a significant impact on the global stage. AI Landscape Exploration Gain a deep understanding of the current AI landscape, exploring the latest advancements, technologies, and emerging trends. Investigate the historical context and pivotal milestones that have shaped the field. Mastering Core AI Concepts Dive into fundamental AI concepts, including machine learning, deep learning, natural language processing, and computer vision. Apply theoretical knowledge through hands-on projects to solidify understanding. Research Methodologies Master effective research methodologies specific to AI, covering experimental design, data collection, and analysis techniques. Develop skills to formulate impactful research questions and hypotheses. Hands-On Research Projects Undertake hands-on research projects under the guidance of experienced mentors. Gain practical experience in executing research studies and experiments. Navigating Advanced AI Domains Broaden your expertise by delving into diverse AI domains such as natural language processing, computer vision, reinforcement learning, and robotics. Analyze real-world applications and challenges in each domain to foster a comprehensive understanding. Strengthening Mathematical Foundations Deepen your mathematical knowledge with a focus on linear algebra, calculus, probability, and statistics. Apply advanced mathematical concepts to analyze, optimize, and innovate within machine learning algorithms. Building a Robust Research Portfolio Craft a comprehensive research portfolio showcasing your achievements, projects, and contributions. Learn the art of effective communication to present research findings to both technical and non-technical audiences. Networking and Collaboration Strategies Master networking techniques to connect with renowned researchers, industry experts, and peers. Explore collaboration opportunities and establish a strong presence within the global AI research community. Specialization and Niche Expertise Identify a specific niche within AI that aligns with your passion and strengths. Develop deep expertise in your chosen area, contributing unique insights to advance the field. Continuous Learning and Innovation Embrace a culture of continuous learning to stay updated on the latest AI research trends. Foster innovation by exploring novel ideas, experimenting with new approaches, and pushing the boundaries of existing knowledge. Recognition and Contribution to AI Community Strategically position yourself for recognition within the AI community through conference presentations, awards, and active participation. Give back to the community by mentoring aspiring researchers and contributing to educational initiatives. Key Takeaways Mastery of advanced AI concepts, methodologies, and mathematical foundations. Hands-on experience in executing impactful research projects. Strategic networking and collaboration skills within the global AI community. Expertise in niche areas of AI research. Who Should Enroll Engineers looking to transition into AI research and make substantial contributions to the field. Professionals seeking to elevate their careers by becoming globally recognized AI researchers. Enroll now in "From Engineer to World's Best AI Researcher" and take the definitive step towards making a lasting impact on the forefront of artificial intelligence research. Unleash your potential and join a community dedicated to shaping the future of AI research! Courtesy, Dr. FAK Noble Ai Researcher, Scientists, Product Developer, Innovator & Pure Consciousness Expert Founder of Noble Transformation Hub TM

Welcome learner! This is the first of five courses of the Google Cloud Data Analytics Certificate. Jump into the world of data analytics and cloud computing. Explore the data lifecycle in the cloud and gain a better understanding of the common tools used by data analysts.

Big Data engineers and professionals with NoSQL skills are in high demand in the data management industry. This specialization is designed for those who want to develop fundamental skills in Big Data, Apache Spark, and NoSQL databases. Three information-rich courses cover popular NoSQL databases like MongoDB and Apache Cassandra, the widely used Apache Hadoop ecosystem of Big Data tools, and the Apache Spark analytics engine for processing large-scale data. You will start with an overview of the different categories of NoSQL data stores (not just SQL), and then work manually with some of them, including IBM Cloudant, MonogoDB, and Cassandra. You will perform various data management tasks such as creating and replicating databases, inserting, updating, deleting, querying, indexing, aggregating, and partitioning data. Next, you will gain fundamental knowledge of Big Data technologies such as Hadoop, MapReduce, HDFS, Hive, and HBase, followed by deeper knowledge of Apache Spark, Spark Dataframes, Spark SQL, PySpark, Spark Application UI, and scaling Spark with Kubernetes. In the final course, you will learn how to work with Spark Structured Streaming Spark ML - to perform Extract, Transform, and Load (ETL) data processing and machine learning tasks. This specialization is suitable for aspiring NoSQL and Big Data professionals - whether you are or are training to become a data engineer, software developer, IT architect, data scientist, or IT manager. Applied learning project The focus of this specialization is on hands-on learning. Therefore, each course includes practical exercises to practice and apply the NoSQL and Big Data skills you learn in lectures. In the first course, you will work with several NoSQL databases - MongoDB, Apache Cassandra and IBM Cloudant, performing various tasks: creating a database, adding documents, querying data, using HTTP API, performing Create, Read, Update and Delete (CRUD) operations, limiting and sorting records, indexing, aggregation, replication, using CQL shell, keyspace operations and other table operations. In the next course, you will spin up a Hadoop cluster using Docker and run Map Reduce jobs. You will learn Spark using Jark and learn Spark using Jupyter notebooks on the Python kernel. You will develop your Spark skills using DataFrames, Spark SQL, and scale your jobs using Kubernetes. In the final course, you will use Spark for ETL processing and to train and deploy Machine Learning models with IBM Watson.

The nutritional patterns that begin in childhood influence health and well-being throughout life. Food culture has changed significantly in recent decades, especially in parts of the world where processed foods dominate our diets. This course examines modern childhood nutrition and the impact of individual family decisions. The health risks associated with childhood obesity are also discussed. Participants will learn what constitutes a healthy diet for children and adults and how to prepare simple, delicious meals to establish a lifelong connection to easy, home-cooked meals. This course will help prepare participants to become the leading health care professionals, teachers, and parents of today and tomorrow. The text and other materials in this course may contain the views of a particular instructor and do not constitute advice, endorsement, opinion, or information from Stanford University.

In this specialisation, you will master design thinking competencies in an engaging, hands-on, project-based format. We will guide you through a detailed 14-step process where you will solve a human-centred problem. You will learn and apply all the most important tools from the field of human-centred design to generate ideas and connect with clients on a human level. Then you will deepen your practice by moving through six key phases of the design thinking process, from immersion to action learning, and explore the shifts in thinking and skills that accompany them. Applied learning project Using a 14-step process, you will apply concepts and carry out work related to your chosen project, using it to practise a number of important tools for generating and testing ideas and achieving your project goals. You will also create a Personal Development Plan (PDP) that will deepen your design competencies by guiding your personal journey through the six key phases of design thinking.

Vital signs - heart rate, blood pressure, body temperature, respiratory rate and pain sensation - convey important information about the physiological state of the human body. In this six-part course, we will examine the anatomy and physiology behind vital signs so that you will have a systematic, comprehensive understanding of how the body functions. The relevant body systems, including the cardiovascular and respiratory systems, are reviewed and then how the functioning of these systems affects vital signs are explained. We will discuss normal ranges, variations in normal ranges and the mechanisms underlying changes in objective measurement of vital signs. The course also includes demonstrations of appropriate techniques for measuring vital signs in yourself and others. The course is designed for a general audience, but will be of particular interest to people working in healthcare, those considering a career in healthcare, those involved in nursing, those interested in personal health and fitness, or anyone who simply wants to understand how the body functions.

Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: ● Data Analysts, Business Analysts, Business Intelligence professionals ● Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: ● Data Analysts, Business Analysts, Business Intelligence professionals ● Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Applied learning project This specialization incorporates hands-on labs using our Qwiklabs platform. These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google Cloud Platform products, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.

This specialization is designed to help students with little or no programming experience learn to write MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but it also covers in detail the many features of the language that make MATLAB uniquely suited for engineering and scientific computing. Topics covered range from basic programming concepts in the first year, through more advanced techniques including recursion, program efficiency, object-oriented programming, and graphical user interfaces in the second year, to data and image analysis, data visualization, and machine learning in the third year. Applied learning project The specialization includes two projects. The first is the final project of the second year. It involves creating an object-oriented application with a graphical user interface that visualizes COVID-19-related data collected from around the world. The second project is part of the third year and is dedicated to image processing.

Sports analytics has become a field of study with growing popularity, fueled in part by real-world success illustrated by the best-selling book and movie Moneyball. The analysis of team and player performance data continues to revolutionize the sports industry, both on the field, court, and ice, and in the living rooms of fantasy sports and online sports bettors. Using real datasets from Major League Baseball (MLB), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier League (EPL soccer), and the Indian Premier League (IPL cricket), you will learn how to build predictive models to forecast team and player performance. You will also replicate the success of Moneyball using real statistical models, apply a linear probability model (LPM) to predict categorical outcome variables in sports, explore how teams collect and organize athlete performance data using wearable technology, and apply machine learning in the context of sports analytics. This introduction to sports analytics is designed for sports managers, coaches, physical therapists, and sports enthusiasts who want to understand the science behind sports performance and game prediction. The courses in this series will appeal to beginner Python programmers and data analysts who are looking for a fun and practical way to apply their Python, statistics, or predictive modeling skills. Applied learning project Students will apply the methods and techniques they have learned to sports data sets to produce their own results, rather than relying on the data processing performed by others. As a result, students will have the opportunity to explore their own ideas about the performance of sports teams, test them against the data, and thus become producers of sports analytics rather than consumers.

Are you working in a technical role and want to learn the fundamentals of AWS? Are you aiming for a job or career as a cloud developer, architect, or operations specialist? If so, the AWS Cloud Technical Essentials course is the perfect way to get started. This course was designed for those at the beginning of their cloud computing journey - no prior knowledge of cloud computing or AWS products and services is required! Throughout the course, students will step-by-step build highly available, scalable, and cost-effective applications. By the end of the course, you will be able to make informed decisions about when and how to use core AWS compute, storage, and database services across different use cases. You will also learn about cloud computing security by reviewing the AWS Shared Responsibility Model and exploring AWS Identity and Access Management (IAM). Additionally, you will learn how you can use AWS services to monitor and optimize your infrastructure in the cloud. AWS Cloud Technical Essentials is a foundational-level course that will increase your competence, confidence, and authority with hands-on cloud skills that will help you innovate and advance your professional future. Enroll in the AWS Cloud Technical Essentials course and start learning the technical fundamentals of AWS today! Note: This course was designed for learners with a technical background. If you are new to cloud technologies or a business person, we recommend that you complete the AWS Cloud Practitioner Essentials course (https://www.coursera.org/learn/aws-cloud-practitioner-essentials) before taking this course.

This specialization covers the fundamentals of strategic financial management, including financial accounting, investments, and corporate finance. You will learn to evaluate major strategic corporate and investment decisions, understand capital markets and institutions from a financial perspective, and develop a comprehensive framework for value-based financial management and individual financial decision making. The specialization "Financial Management" is part of University of Illinois iMBA Programs Each course in this specialization also fulfills a portion of the University of Illinois course requirements that may lead to You get credit in college. Once you complete the Financial Management specialization, you will - Have a solid foundation for developing a comprehensive system for making strategic financial decisions. - Have a thorough understanding of financial statements and the financial information they provide, and be able to critically evaluate and analyze cash flow statements. - Understand the management and valuation of securities portfolios and methods for assessing the value of firms. - Understand how to consider risk and uncertainty when making investment decisions and understand how companies make financing and investment decisions.