170 Courses

Duration 2 Days 12 CPD hours This course is intended for This is an Intermediate PowerBI course geared for experienced users who wish to leverage the tool's more advanced capabilities Overview This course is about 50% hands-on lab and 50% lecture, designed to train attendees in essential PowerBI data handling functions and reporting skills, coupling the most current, effective techniques with the soundest practices. Attendees of this course will gain practical examples from the experienced instructor who has deployed and configured Power BI reporting in a wide variety of businesses. Working in a hands-on learning environment led by our expert facilitator, students will learn how to: Create Advanced Power BI Reports Advanced understanding of the data schemas and extracting data Perform advanced transformations of data or any data schema Utilize time-phased data in the creation of complex analyses Create new measures using DAX Filter data using row-level security Create and deploy content packs Use Power BI to integrate with line-of-business applications Next Level Power BI for Experienced Users is a two day, course that provides attendees already experienced with Microsoft Power BI basics with a hands-on exploration of intermediate and beyond level features. This course is geared for attendees ready to learn the advanced techniques that you, your business analysts, and your stakeholders need to create complex information from projects, program, and portfolio reporting to utilizing time-phased data and, potentially, data from your enterprise?s other line-of-business tools. Get Project Online Data Select and mine relevant tables with ODATA Advanced ODATA data mining Importing other data formats Advanced Editing of data queries Advanced Data Transformations Managing table relationships Creating & using data hierarchies Creating custom columns and measures and metrics for filtering and reporting Creating Power BI Reports Using advanced visualizations Configuring drill-down Modifying visual interactions Importing and creating custom visuals Configure Power BI Security Creating Dashboard and row-level security Utilizing Filtering using row-level security Publishing Reports and Dashboards Building Mobile Reporting Creating and deploying content packs Configuring natural language query

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

Duration 2 Days 12 CPD hours This course is intended for This in an Introductory and beyond level course is geared for experienced Java developers seeking to be proficient in Apache Kafka. Attendees should be experienced developers who are comfortable with Java, and have reasonable experience working with databases. Overview Working in a hands-on learning environment, students will explore Overview of Streaming technologies Kafka concepts and architecture Programming using Kafka API Kafka Streams Monitoring Kafka Tuning / Troubleshooting Kafka Apache Kafka is a real-time data pipeline processor. It high-scalability, fault tolerance, execution speed, and fluid integrations are some of the key hallmarks that make it an integral part of many Enterprise Data architectures. In this lab intensive two day course, students will learn how to use Kafka to build streaming solutions. Introduction to Streaming Systems Fast data Streaming architecture Lambda architecture Message queues Streaming processors Introduction to Kafka Architecture Comparing Kafka with other queue systems (JMS / MQ) Kaka concepts : Messages, Topics, Partitions, Brokers, Producers, commit logs Kafka & Zookeeper Producing messages Consuming messages (Consumers, Consumer Groups) Message retention Scaling Kafka Programming With Kafka Configuration parameters Producer API (Sending messages to Kafka) Consumer API (consuming messages from Kafka) Commits , Offsets, Seeking Schema with Avro Kafka Streams Streams overview and architecture Streams use cases and comparison with other platforms Learning Kafka Streaming concepts (KStream, KTable, KStore) KStreaming operations (transformations, filters, joins, aggregations) Administering Kafka Hardware / Software requirements Deploying Kafka Configuration of brokers / topics / partitions / producers / consumers Security: How secure Kafka cluster, and secure client communications (SASL, Kerberos) Monitoring : monitoring tools Capacity Planning : estimating usage and demand Trouble shooting : failure scenarios and recovery Monitoring and Instrumenting Kafka Monitoring Kafka Instrumenting with Metrics library Instrument Kafka applications and monitor their performance

Duration 4 Days 24 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 4 Days 24 CPD hours This course is intended for This is an introductory-level C++ programming course designed for developers with experience programming in C or other languages. Practical hands-on prior programming experience and knowledge is required. Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in basic coding with C++, coupling the most current, effective techniques with the soundest industry practices. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn: Writing procedural programs using C++ Using private, public and protected keywords to control access to class members Defining a class in C++ Writing constructors and destructors Writing classes with const and static class members Overloading operators Implementing polymorphic methods in programs Writing programs using file I/O and string streams Using manipulators and stream flags to format output Using the keyword template to write generic functions and classes Writing programs that use generic classes and functions Writing programs that use algorithms and containers of the Standard Library Apply object-oriented design techniques to real-world programming problems Using algorithms and containers of the Standard Library to manipulate string data Understand how C++ protects the programmer from implementation changes in other modules of an application Using try() blocks to trap exceptions Using catch() blocks to handle exceptions Defining exceptions and using throw to trigger them Introduction to C++ Programming / C++ Essentials is a skills-focused, hands-on C++ training course geared for experienced programmers who need to learn C++ coupled with sounds coding skills and best practices for OO development. Students will leave this course armed with the required skills to put foundation-level C++ programming skills right to work in a practical environment. The central concepts of C++ syntax and style are taught in the context of using object-oriented methods to achieve reusability, adaptability and reliability. Emphasis is placed on the features of C++ that support abstract data types, inheritance, and polymorphism. Students will learn to apply the process of data abstraction and class design. Practical aspects of C++ programming including efficiency, performance, testing, and reliability considerations are stressed throughout. Comprehensive hands on exercises are integrated throughout to reinforce learning and develop real competency Moving from C to C++ (Optional) New Compiler Directives Stream Console I/O Explicit Operators Standard Libraries Data Control Capabilities Handling Data New Declaration Features Initialization and Assignment Enumerated Types The bool Type Constant Storage Pointers to Constant Storage Constant Pointers References Constant Reference Arguments Volatile Data Global Data Functions Function Prototypes and Type Checking Default Function Data Types Function Overloading Problems with Function Overloading Name Resolution Promotions and Conversions Call by Value Reference Declarations Call-by-Reference and Reference Types References in Function Return Constant Argument Types Conversion of Parameters Using Default Initializers Providing Default Arguments Inline Functions Operator Overloading Advantages and Pitfalls of Overloading Member Operator Syntax and Examples Class Assignment Operators Class Equality Operators Non-Member Operator Overloading Member and Non-Member Operator Functions Operator Precedence This Pointer Overloading the Assignment Operator Overloading Caveats Creating and Using Objects Creating Automatic Objects Creating Dynamic Objects Calling Object Methods Constructors Initializing Member consts Initializer List Syntax Allocating Resources in Constructor Destructors Block and Function Scope File and Global Scope Class Scope Scope Resolution Operator :: Using Objects as Arguments Objects as Function Return Values Constant Methods Containment Relationships Dynamic Memory Management Advantages of Dynamic Memory Allocation Static, Automatic, and Heap Memory Free Store Allocation with new and delete Handling Memory Allocation Errors Controlling Object Creation Object Copying and Copy Constructor Automatic Copy Constructor Conversion Constructor Streaming I/O Streams and the iostream Library Built-in Stream Objects Stream Manipulators Stream Methods Input/Output Operators Character Input String Streams Formatted I/O File Stream I/O Overloading Stream Operators Persistent Objects Introduction to Object Concepts The Object Programming Paradigm Object-Orientated Programming Definitions Information Hiding and Encapsulation Separating Interface and Implementation Classes and Instances of Objects Overloaded Objects and Polymorphism Declaring and Defining Classes Components of a Class Class Structure Class Declaration Syntax Member Data Built-in Operations Constructors and Initialization Initialization vs. Assignment Class Type Members Member Functions and Member Accessibility Inline Member Functions Friend Functions Static Members Modifying Access with a Friend Class Templates Purpose of Template Classes Constants in Templates Templates and Inheritance Container Classes Use of Libraries Strings in C++ Character Strings The String Class Operators on Strings Member Functions of the String Class Inheritance Inheritance and Reuse Composition vs. Inheritance Inheritance: Centralized Code Inheritance: Maintenance and Revision Public, Private and Protected Members Redefining Behavior in Derived Classes Designing Extensible Software Systems Syntax for Public Inheritance Use of Common Pointers Constructors and Initialization Inherited Copy Constructors Destructors and Inheritance Public, Protected, Private Inheritance Exceptions Types of Exceptions Trapping and Handling Exceptions Triggering Exceptions Handling Memory Allocation Errors C++ Program Structure Organizing C++ Source Files Integrating C and C++ Projects Using C in C++ Reliability Considerations in C++ Projects Function Prototypes Strong Type Checking Constant Types C++ Access Control Techniques Polymorphism in C++ Definition of Polymorphism Calling Overridden Methods Upcasting Accessing Overridden Methods Virtual Methods and Dynamic Binding Virtual Destructors Abstract Base Classes and Pure Virtual Methods Multiple Inheritance Derivation from Multiple Base Classes Base Class Ambiguities Virtual Inheritance Virtual Base Classes Virtual Base Class Information The Standard Template Library STL Containers Parameters Used in Container Classes The Vector Class STL Algorithms Use of Libraries

Duration 3 Days 18 CPD hours This course is intended for This intermediate-level hands-on course is geared for experienced Administrators, Analysts, Architects, Data Scientists, Database Administrators and Implementers Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our Oracle Certified expert facilitator, students will learn how to: Administer ODI resources and setup security with ODI Apply ODI Topology concepts for data integration Describe ODI Model concepts Describe architecture of Oracle Data Integrator Design ODI Mappings, Procedures, Packages, and Load Plans to perform ELT data transformations Explore, audit data, and enforce data quality with ODI Implement Changed Data Capture with ODI Oracle Data Integrator is a comprehensive data integration platform that covers all data integration requirements from high-volume, high-performance batch loads, to event-driven integration processes and SOA-enabled data services. Oracle Data Integrator's Extract, Load, Transform (E-LT) architecture leverages disparate RDBMS engines to process and transform the data - the approach that optimizes performance, scalability and lowers overall solution costs. Throughout this course participants will explore how to centralize data across databases, performing integration, designing ODI Mappings, and setting up ODI security. In addition, Oracle Data Integrator can interact with the various tools of the Hadoop ecosystem, allowing administrators and data scientists to farm out map-reduce operations from established relational databases to Hadoop. They can also read back into the relational world the results of complex Big Data analysis for further processing. Working in a hands-on learning environment led by our Oracle Certified expert facilitator, students will learn how to: Administer ODI resources and setup security with ODI Apply ODI Topology concepts for data integration Describe ODI Model concepts Describe architecture of Oracle Data Integrator Design ODI Mappings, Procedures, Packages, and Load Plans to perform ELT data transformations Explore, audit data, and enforce data quality with ODI Implement Changed Data Capture with ODI Introduction to Integration and Administration Oracle Data Integrator: Introduction Oracle Data Integrator Repositories Administering ODI Repositories Create and connect to the Master Repository Export and import the Master Repository Create, connect, and set a password to the Work Repository ODI Topology Concepts ODI Topology: Overview Data Servers and Physical Schemas Defining Topology Agents in Topology Planning a Topology Describing the Physical and Logical Architecture Topology Navigator Creating Physical Architecture Creating Logical Architecture Setting Up a New ODI Project ODI Projects Using Folders Understanding Knowledge Modules Exporting and Importing Objects Using Markers Oracle Data Integrator Model Concepts Understanding the Relational Model Understanding Reverse-Engineering Creating Models Organizing ODI Models and Creating ODI Datastores Organizing Models Creating Datastores Constraints in ODI Creating Keys and References Creating Conditions Exploring Your Data Constructing Business Rules ODI Mapping Concepts ODI Mappings Expressions, Join, Filter, Lookup, Sets, and Others Behind the Rules Staging Area and Execution Location Understanding Knowledge Modules Mappings: Overview Designing Mappings Multiple Sources and Joins Filtering Data Overview of the Flow in ODI Mapping Selecting a Staging Area Configuring Expressions Execution Location Selecting a Knowledge Module Mappings: Monitoring and Troubleshooting Monitoring Mappings Working with Errors Designing Mappings: Advanced Topics 1 Working with Business Rules Using Variables Datasets and Sets Using Sequences Designing Mappings: Advanced Topics 2 Partitioning Configuring Reusable Mappings Using User Functions Substitution Methods Modifying Knowledge Modules Using ODI Procedures Procedures: Overview Creating a Blank Procedure Adding Commands Adding Options Running a Procedure Using ODI Packages Packages: Overview Executing a Package Review of Package Steps Model, Submodel, and Datastore Steps Variable Steps Controlling the Execution Path Step-by-Step Debugger Starting a Debug Session New Functions Menu Bar Icons Managing ODI Scenarios Scenarios Managing Scenarios Preparing for Deployment Using Load Plans What are load plans? Load plan editor Load plan step sequence Defining restart behavior Enforcing Data Quality with ODI Data Quality Business Rules for Data Quality Enforcing Data Quality with ODI Working with Changed Data Capture CDC with ODI CDC implementations with ODI CDC implementation techniques Journalizing Results of CDC Advanced ODI Administration Setting Up ODI Security Managing ODI Reports ODI Integration with Java

Duration 5 Days 30 CPD hours This course is intended for This hands-on course is geared for experienced DBAs new to Oracle 19c, who can work in Linux and have basic experience with SQL scripting. Overview This course combines expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our Oracle Certified expert facilitator, students will learn how to: How to use tools to access an Oracle Database Generate database creation scripts by using DBCA How to create a container database (CDB) by using the CREATE DATABASE command Learn about Startup and shut down Oracle databases Initialization parameter files and initialization parameters Tools that are used to administer Oracle Net Services How to use the Oracle Connection Manager Control utility to administer Oracle Connection Manager How to create a new PDB from the PDB seed. Logical and physical storage structures in an Oracle database Usage of Oracle Database features that save space Explanation of DML and undo data generation Learn about general architecture of Oracle Data Pump and SQL*Loader How to use SQL*Loader to load data from a non-Oracle database (or user files) Bonus Content / Time Permitting: Explanation of maintenance windows Bonus Content / Time Permitting: Employ basic monitoring procedures and manage performance Oracle 19C Database Administration I (Oracle DBA I) is a comprehensive, hands-on course provides concrete information on the design of an Oracle Database instance and database, allowing you to manage your database investment. In this class, you will learn how to create database storage structures that align with your requirements and business models. You will also learn how to create users and administer databases as well as harden the databases to meet your business requirements. This is the foundational course for learning about Oracle Database and it does not assume prior knowledge or Oracle technologies, however you should have incoming experience working with SQL, and be comfortable with Linux and working from the command line. This training is NOT Official Oracle University training. This is independent, adjustable content that aligns with current topics, skills and tools that participants need to excel in these areas. Introduction to Oracle Database Oracle Multitenant Container Database Oracle Database Instance Configurations Database Sharding Oracle Database Server Accessing an Oracle Database Oracle Database Tools Database Tool Choices SQL Developer Enterprise Manager Database Express Creating an Oracle Database by Using DBCA Planning the Database Choosing a Database Template Configuration Assistant Creating an Oracle Database by Using a SQL Command Creating a Container Database Enable Pluggable Database Starting Up and Shutting Down a Database Instance Starting the Oracle Database Instance Shutting Down an Oracle Database Instance Opening and Closing PDBs Managing Database Instances Investigating Initialization Parameter Files Viewing Initialization Parameters by Using SQL*Plus Modifying Initialization Parameters by Using SQL*Plus Viewing Diagnostic Information Oracle Net Services Overview Oracle Net Services Components Oracle Net Listener Configuring Naming Methods Configuring the Oracle Network to Access a Database Creating a Net Service Name for a PDB Configuring and Administering the Listener Exploring the Default Listener Creating a Second Listener Connecting to a Database Service Using the New Listener Configuring a Shared Server Architecture Configuring Shared Server Mode Configuring Clients to Use a Shared Server Configuring Oracle Connection Manager for Multiplexing and Access Control Oracle Connection Manager Processes Filtering Rules Session Multiplexing Creating PDBs Creating a New PDB from the PDB Seed Cloning Remote PDBs in Hot Mode Relocating PDBs Managing PDBs Renaming a PDB Setting Parameter Values for PDBs Database Storage Overview Logical and Physical Database Structures Tablespaces and Data Files Types of Segments Monitoring Tablespace Space Usage Creating and Managing Tablespaces Viewing Tablespace Information Creating a Tablespace Managing Temporary and Permanent Tablespaces Improving Space Usage Managing Space in Tablespaces Using Compression Enabling the Resumable Space Allocation Feature Creating and Managing User Accounts Creating Common and Local Users Creating a Local User for an Application Exploring OS and Password File Authentication Configuring Privilege and Role Authorization Granting a Local Role (DBA) to PDBADMIN Using SQL*Developer to Create Local Roles Configuring User Resource Limits Using SQL*Developer to Create a Local Profile & Users Configuring a Default Role for a User Implementing Oracle Database Auditing Enabling Unified Auditing Creating Audit Users Creating an Audit Policy Introduction to Loading and Transporting Data General Architecture Oracle Data Pump SQL Loader Loading Data into a PDB from an External File Moving Data from One PDB to Another PDB Transporting a Tablespace Using External Tables to Load and Transport Data Querying External Tables Unloading External Tables Automated Maintenance Tasks Overview Automated Maintenance Tasks Maintenance Windows Predefined Maintenance Windows Bonus: Managing Tasks and Windows Enabling and Disabling Automated Maintenance Tasks Modifying the Duration of a Maintenance Window Bonus: Database Monitoring and Performance Tuning Overview Performance Planning Considerations Automatic Workload Repository (AWR) Advisory Framework Bonus: Monitoring Database Performance & Processes Server-Generated Alerts Setting Metric Thresholds Performance Monitoring Examining the Database Background Processes Bonus: Tuning Database Memory Viewing Memory Configurations Bonus: Analyzing SQL and Optimizing Access Paths Using the Optimizer Statistics Advisor

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.

Duration 5 Days 30 CPD hours This course is intended for This introductory-level Python course is geared for experienced web developers new to Python who want to use Python and Django for full stack web development projects. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Develop full-stack web sites based on content stored in an RDMS Use python data types appropriately Define data models Understand the architecture of a Django-based web site Create Django templates for easy-to-modify views Map views to URLs Take advantage of the built-in Admin interface Provide HTML form processing Geared for experienced web developers new to Python, Introduction to Full Stack Web Development with Python and Django is a five-day hands-on course that teaches students how to develop Web applications using the Django framework. Students will explore the basics of creating basic applications using the MVC (model-view-controller) design pattern, as well as more advanced topics such as administration, session management, authentication, and automated testing. This comprehensive, practical course provides an in-depth exploration of working with the programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world. The Python Environment Starting Python Using the interpreter Running a Python script Getting help Editors and IDEs Getting Started Using variables Built in functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control Conditional expressions Relational and Boolean operators while loops Lists and Tuples About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Working with Files File overview The with statement Opening a file Reading/writing files Dictionaries and Sets About dictionaries Creating and using dictionaries About sets Creating and using sets Functions Returning values Function parameters Variable Scope Sorting with functions Errors and Exception Handling Exception overview Using try/catch/else/finally Handling multiple exceptions Ignoring exceptions Modules and Packages Creating Modules The import statement Module search path Creating packages Classes About OO programming Defining classes Constructors Properties Instance methods and data Class/static methods and data Inheritance Django Architecture Django overview Sites and apps Shared configuration Minimal Django layout Built in flexibility Configuring a Project Executing manage.py Starting the project Generating app files App configuration Database setup The development server Using cookiecutter Creating models Defining models Related objects SQL Migration Simplel model access Login for Nothing and Admin for Free Setting up the admin user Using the admin interface Views What is a view HttpResponse URL route configuration Shortcut: get_object_or_404() Class-based views Templates About templates Variable lookups The url tag Shortcut: render() Querying Models QuerySets Field lookups Chaining filters Slicing QuerySets Related fields Q objects Advanced Templates Use Comments Inheritance Filters Escaping HTML Custom filters Forms Forms overview GET and POST The Form class Processing the form Widgets Validation Forms in templates Automated Testing Why create tests? When to create tests Using Django's test framework Using the test client Running tests Checking code coverage

Private equity refers to investments made in private companies, where investors provide capital in exchange for equity ownership. Private equity refers to investments made in private companies, where investors provide capital in exchange for equity ownership. It’s a form of alternative investment that can help companies accelerate growth, expand operations, or make strategic acquisitions. For UK companies, private equity can be particularly attractive because it offers access to significant capital, strategic guidance, and industry expertise that can fuel their growth ambitions. Private equity CFOs are strategic leaders who strive to raise their company’s profile, engage with new talent, and attract the attention of private equity investors by creating an engaging investor story. CFOs with PE experience are growth-oriented, adopting a forward-looking approach, instead of looking through the rear-view mirror as financial often do. Private equity CFOs are multi-dimensional with a growing list of responsibilities. Many are set to become tomorrow’s CEOs, laying the groundwork by engaging with internal and external stakeholders. Organisations seeking private equity investment are increasingly recruiting CFOs during the early stages of their life cycle. CFOs with private equity house experience will drive value and nurture rich working relationships by boosting the company’s financial credibility with potential investors and traditional financial institutions. Companies seeking private equity funding in highly regulated industries, such as financial services and health care, will want to recruit a CFO who is an expert in that field. CFOs with industry-specific regulatory knowledge will understand the nuances and challenges that their company must contend with. To learn more visit our website at https://www.fdcapital.co.uk/cfo-recruitment/ Tags Online Events Things To Do Online Online Seminars Online Business Seminars #raising #uk #privateequity