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This accredited qualification has been designed for delivery to all learners working or preparing to work in a customer service role or where using the telephone is a part of their role. This qualification covers the principles of customer service, including how to meet customer expectations, the importance of appropriate behaviour and communication techniques, as well as ways to deal with problem customers. How long will it take me to achieve this qualification? This qualification is classroom-based and usually achieved by taking a one-day course. However, it can also be achieved through a variety of other methods including blended and distance learning, as long as the recommended learning hours are completed. How is the qualification assessed? Through a 1-hour 30-question multiple-choice examination. Learners must achieve a score of at least 20 out of 30 in order to pass.

Developing a solid foundation in Greek grammar will help you create your own sentences correctly and will also make it easier to improve your communication skills in both spoken and written Greek. So this course has been designed to help you steadily advance with the Greek language. Here, on the Greek Online School Learning Management System (LMS) you will find all the grammar phenomena that you need to know for the A2 Level (basic knowledge) in Greek, the language that influenced all European languages.

The Music Technology, Production and Recording course can lead to a degree and career in many different professions. In one year, you could be well on your way to a new job or university study such as: • sound engineer • producer • media composer • sound recordist • music performer • recording artist • acoustic engineer • mixing or mastering engineer • sound designer The qualification gives you all the academic knowledge and creative skills you need for higher education study. It also provides the opportunity to develop study skills that help you to succeed at HE or in the industry sector. The course runs for 3 days per week (9.15am - 2.45pm) What will I study?  The course explores various areas of the industry and in particular those that are career based and can be studied at a higher level. Key subject areas are: • Acoustics in Music Production and Recording  • Music Composition and Theory • MIDI and Audio Sequencing • Sound and Music for Visual Media • Music Technology in Performance • Studio Production and Recording • Sampling and Synthesis You will learn to use industry-standard software and hardware equipment in the classroom, recording studio and live room environments that emulate real-world scenarios and working practices. 

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Hands-on Predicitive Analytics with Python (TTPS4879) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This course is intended for individuals who need to manage instances of Salesforce Sales Cloud©. Target students have Sales Cloud user skills and are often existing Sales, Administrative, or Operations support employees, IT employees who are given the additional responsibilities of Salesforce.com© system administration, or external consultants who have been engaged to provide administrative support for an organization.This course can also be an important component of preparing for the Salesforce Certified Administrator exam for students who are seeking the Salesforce Certified Administrator certification. Overview In this course, students will perform the basic skills required of a typical Salesforce system administrator.Students will:- Describe basic concepts related to Salesforce administration.- Set up an organization.- Manage user accounts.- Implement security controls.- Configure the Salesforce Classic user interface.- Support the Lightning Experience user interface.- Customize pages.- Manage Opportunities.- Implement additional Opportunity features.- Implement data validation and workflows.- Manage Leads.- Manage Accounts.- Manage Contacts.- Manage Campaigns.- Manage Cases.- Manage custom objects.- Manage data.- Configure views, reports, and dashboards.- Integrate and extend Salesforce. In this course, students will identify information about the five native business processes every company can manage using Salesforce, regardless of the License Edition. Students will also gain insight into each of the functional groups of users (Inside Sales, Outside Sales, Marketing, Customer Support, and Management), and they will establish patterns of critical thinking that can help them to ensure that they are indeed taking the right approach and providing the necessary support for each request they receive. Introduction to Salesforce Administration The Salesforce Data Model SMART Administration Principles Setting Up an Organization Manage the Company Profile Configure Organizational Settings Managing User Accounts Create User Accounts Manage Users Implementing Security Controls Salesforce Security Essentials Configure Profiles Establish Organization-Wide Sharing Defaults Configure Roles Create Sharing Rules Perform a Health Check Configuring the Salesforce Classic User Interface Configure User Interface Settings Customize the Home Page in Salesforce Classic Configure Search in Salesforce Classic Supporting the Lightning Experience User Interface Implement Lightning Experience Customize Lightning Experience Home Pages Customizing Pages Create Page Layouts in Salesforce Classic Customize Record Pages in Lightning Experience Introduction to Opportunity Management Opportunity Management Essentials Design and Implement Opportunity Fields Design and Implement Opportunity Stages Design and Implement Opportunity Contact Roles Design and Create Opportunity Record Types Implementing Additional Opportunity Features Implement and Maintain Opportunity Products and Price Books Implement the Similar Opportunities Function Implement Opportunity Teams Create a Big Deal Alert Implementing Data Validation and Workflows Create and Test Validation Rules Create and Test Workflows Managing Leads Lead Management Essentials Design and Implement Lead Fields Design and Implement Custom Lead Sources Design and Implement Web-to-Lead Forms Design and Implement Lead Assignment Rules Managing Accounts Design an Account Management Model Implement an Account Management Model Managing Contacts Design a Contact Management Strategy Implement a Contact Management Strategy Managing Campaigns Prepare for Campaign Management Administer a Campaign Management Strategy Managing Cases Case Management Essentials Design and Implement Case Fields Design and Implement Case Origins Automate Case Management Providing Apps and Custom Objects Supply Apps in Salesforce Classic Supply Apps in Lightning Experience Managing Data Data Management Essentials Import and Update Data Back Up and Restore Data Configuring Views, Reports, and Dashboards Create Views Create and Manage Reports Create and Manage Dashboards Integrating and Extending Salesforce Integrate Salesforce and Outlook Implement Salesforce1 Implement SalesforceA Additional course details: Nexus Humans Salesforce.com - Sales Cloud Administration Essentials training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Salesforce.com - Sales Cloud Administration Essentials course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Our rather awesome bass course return with a seven session run falling on alternate Saturdays.  You'll learn basic technique from how to move your fingers, play your first arpeggios, accompany a band, play your first classic bass riffs and of course, execute the perfect bass face.  You don't even need a bass guitar to join. A regular six string guitar will do, and, once we've got you addicted to the groove, you can go out and buy your self a Rickenbacker.

Duration 5 Days 30 CPD hours This course is intended for This course is for administrators and users who are responsible for planning, administrating and configuring an IBM FileNet P8 Platform system Overview Introduction to IBM FileNet P8 Platform - IBM FileNet P8 Platform features - IBM FileNet P8 Platform integration options - IBM FileNet P8 Platform components - IBM FileNet P8 Platform solutions Architecture and domain structures - IBM FileNet P8 Platform Architecture - Explore the core IBM FileNet P8 Platform applications - Locate P8 domain structures - Use IBM Content Navigator Manage logging - View and archive system logs - Configure trace logging Configure auditing - Create audit definitions - Prune audit entries Manage storage areas - Create a file storage area - Create a storage policy - Create an advanced storage area Build an object store - Create JDBC data sources for an object store - Create an object store - Add the repository to an IBM Content Navigator desktop Create property templates and classes - Create a choice list - Create property templates - Create document and folder classes Modify classes and properties - Change the property template name - Modify a choice list - Work with metadata dependencies Use events to trigger actions - Create a subscription with an event action - Update the event action with new code module Configure security for IBM FileNet P8 assets - Configure initial object store security - Use the Security Script wizard - Configure default instance security - Configure security inheritance - Implement designer group access - Configure role-based access Use bulk operations - Use bulk actions to modify security for multiple documents - Use bulk operations to cancel checkout of documents Configure content-based retrieval searches - Register IBM Content Search Services - Configure index partitioning - Configure content-based indexes - Create content-based searches Work with sweeps - Configure a bulk move content job - Create a disposal policy - Create a content migration policy Move IBM FileNet P8 Platform applications between environments - Configure a destination environment - Create a source environment - Export the FileNet P8 application assets - Convert and analyze the FileNet P8 assets - Import the application assets - Run a change impact analysis in command line Introduction to IBM FileNet P8 content services containers - IBM FileNet P8 content services containers - Benefits of containers - Considerations when choosing containers Organize content across the enterprise - Plan for Multitenancy - Isolate content in an IBM FileNet P8 platform system This course teaches you the configuration and administration of an IBM FileNet P8 Platform 5.5.x system. It introduces you to the key concepts of IBM FileNet P8 Platform architecture and organizing the content across the enterprise. You will learn how to build content repositories, configure metadata, create storage areas, manage security, logging, and auditing, run bulk processing, use the sweep framework, extend the functionality with Events and Subscription, migrate and deploy FileNet P8 assets between environments, and configure content-based retrieval searches Introduction to IBM FileNet P8 Platform IBM FileNet P8 Platform features IBM FileNet P8 Platform integration options IBM FileNet P8 Platform components IBM FileNet P8 Platform solutions Architecture and domain structures IBM FileNet P8 Platform Architecture Explore the core IBM FileNet P8 Platform applications Locate P8 domain structures Use IBM Content Navigator Manage logging View and archive system logs Configure trace logging Configure auditing Create audit definitions Prune audit entries Manage storage areas Create a file storage area Create a storage policy Create an advanced storage area Build an object store Create JDBC data sources for an object store Create an object store Add the repository to an IBM Content Navigator desktop Create property templates and classes Create a choice list Create property templates Create document and folder classes Modify classes and properties Change the property template name Modify a choice list Work with metadata dependencies Use events to trigger actions Create a subscription with an event action Update the event action with new code module Configure security for IBM FileNet P8 assets Use the Security Script wizard Configure default instance security Configure security inheritance Implement designer group access Configure rolebased access +O35Use bulk operations Use bulk actions to modify security for multiple documents Use bulk operations to cancel checkout of documents Configure content-based retrieval searches Register IBM Content Search Services Configure index partitioning Configure contentbased indexes Create contentbased searches Work with sweeps Configure a bulk move content job Create a disposal policy Create a content migration policy Move IBM FileNet P8 Platform applications between environments Configure a destination environment Create a source environment Export the FileNet P8 application assets Convert and analyze the FileNet P8 assets Import the application assets Run a change impact analysis in command line Introduction to IBM FileNet P8 content services container IBM FileNet P8 content services containers Benefits of containers Considerations when choosing container Organize content across the enterprise Plan for Multitenancy Isolate content in an IBM FileNet P8 platform system

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 3 Days 18 CPD hours This course is intended for This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.