442 Algorithm courses

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 anyone who is new to software development and wants, or needs, to gain an understanding of the fundamentals of coding and basics of C++ and object-oriented programming concepts. This course is for Non-Developers, or anyone who wants to have a basic understanding of and learn how to code C++ applications and syntax Overview Companies are constantly challenged to keep their applications, development projects, products, services (and programmers!) up to speed with the latest industry tools, skills, technologies and practices to stay ahead in the ever-shifting markets that make up today's fiercely competitive business landscape. The need for application, web and mobile developers and coders is seemingly endless as technologies regularly change and grow to meet the modern needs of demanding industries and clients. C++ and Programming Basics for Non-Programmers is a five-day, basic-level training course geared for IT candidates who have little or no prior experience in computer programming. Throughout this gentle introduction to programming and C++, students will learn to create applications and libraries using C++ using best practices and sound OO development techniques for writing object-oriented programs in C++. Special emphasis is placed on object-oriented concepts and best practices throughout the training. Fundamentals of the Program Development Cycle Computer Architecture The Notion of Algorithms Source Code vs. Machine Code Compile-Time vs. Run-Time Software Program Architecture Standalone Client/Server Distributed Web-Enabled IDE (Interactive Development Environment) Concepts Looping Constructs Counter-Controlled Repetition Sentinel-Controlled Repetition Nested Control Constructs break and continue Statements Structured Programming Best Practices Writing Methods (Functions) Static vs. Dynamic Allocation Declaring Methods Declaring Methods with Multiple Parameters Method-Call Stack Scope of Declarations Argument Promotion and Casting Designing Methods for Reusability Method Overloading Arrays Purpose of Arrays Declaring and Instantiating Arrays Passing Arrays to Methods Multidimensional Arrays Variable-Length Argument Lists Using Command-Line Arguments Using Environment Variables Deeper Into Classes and Objects Controlling Access to Class Members Referencing the Current Object Using this Overloading Constructors Default and No-Argument Constructors Composition of Classes Garbage Collection and Destructors The finalize Method Static Class Members Defining Classes Using Inheritance Application Development Fundamentals Structure of a C++ Program Memory Concepts Fundamental Data Type Declarations Fundamental I/O Concepts Fundamental Operators Arithmetic Operators Logical Operators Precedence and Associativity Building and Deploying a C++ Program Superclasses and Subclasses Advantages of Using Inheritance protected Class Members Constructors in Subclasses Increasing Convenience by Using Polymorphism Purpose of Polymorphic Behavior The Concept of a Signature Abstract Classes and Methods final Methods and Classes Purpose of Interfaces Using and Creating Interfaces Common Interfaces of the C++ API Files and Streams Concept of a Stream Class File Sequential Access Object Serialization to/from Sequential Access Files Fundamental Searching and Sorting Introduction to Searching Algorithms Linear Search Binary Search Introduction to Sorting Algorithms Selection Sort Insertion Sort Merge Sort Fundamental Data Structures Dynamic Memory Allocation Linked Lists Stacks Queues Trees Exception Handling Types of Exceptions Exception Handling Overview Introduction to Classes and Objects Classes, Objects and Methods Object Instances Declaring and Instantiating a C++ Object Declaring Methods set and get Methods Initiating Objects with Constructors Primitive Types vs. Reference Types Flow Control Conditional Constructs Exception Class Hierarchy Extending Exception Classes When to Throw or Assert Exceptions Formatted Output printf Syntax Conversion Characters Specifying Field Width and Precision Using Flags to Alter Appearance Printing Literals and Escape Sequences Formatting Output with Class Formatter Strings, Characters and Regular Expressions Fundamentals of Characters and Strings String Class String Operations StringBuilder Class Character Class StringTokenizer Class Regular Expressions Regular Expression Syntax Pattern Class Matcher Class Fundamental GUI Programming Concepts Overview of Swing Components Displaying Text and Graphics in a Window Event Handling with Nested Classes GUI Event Types and Listener Interfaces Mouse Event Handling Layout Managers Additional course details: Nexus Humans C Plus Plus and Programming Basics for Non-Programmers (TTCP2000) 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 C Plus Plus and Programming Basics for Non-Programmers (TTCP2000) 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 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 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 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Duration 2 Days 12 CPD hours This course is intended for This course is for web developers who are well-versed in JavaScript and want to take their TypeScript knowledge to the next level. No prior TypeScript knowledge is required. Overview By completing this course, you will be able to build interesting TypeScript projects. This course will take your current TypeScript knowledge to the next level. Here are the important topics that this course will cover: - Learn the fundamentals of TypeScript - Build a weather forecast widget with Angular - Create building a note-taking client/server application using Angular, Node.js, and MongoDB - Create a real-time chat application with React In this course, you will begin by learning the fundamentals of TypeScript and then move toward advanced concepts. Then, learn how TypeScript relates to JavaScript that you may have written before. and understand the benefits of TypeScript and how it can help you avoid software defects. You will also learn to use type guards, check null and undefined, create tagged union types, and compare the performance of algorithms. Additionally, you will use TypeScript to build a weather forecast widget with Angular and then progress by building a note-taking client/server application using Angular, Node.js, and MongoDB, and finally create a real-time chat application with React. TypeScript Fundamentals Introduction to TypeScript Type Checking Advanced Type Analysis Features Comparing Performance of Algorithms Building a Weather Forecast Widget with Angular 4 Using Modules Creating the First Component Using the Component in Other Components Using the Main Component Note-Taking Client/Server App Setting up the Server Integrating with MongoDB Creating the Client Side with Angular Real-Time Chat Setting up the Project Writing the Server Creating the Chat Room Additional course details: Nexus Humans Building Web Applications With TypeScript, Angular, and React 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 Building Web Applications With TypeScript, Angular, and React 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 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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 The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Duration 5 Days 30 CPD hours This course is intended for Experienced system administrators and network administrators Overview By the end of the course, you should be able to meet the following objectives: Describe the NSX Advanced Load Balancer architecture Describe the NSX Advanced Load Balancer components and main functions Explain the NSX Advanced Load Balancer key features and benefits Deploy and configure the NSX Advanced Load Balancer infrastructure within private or public clouds using Write and No Access Cloud Connectors Explain, deploy, and configure Service Engines Explain and configure local load balancing constructors such as virtual services, pools, health monitors, and related components Understand and modify application behavior through profiles, policies, and DataScripts Configure advanced services such as global server load balancing Describe how to use NSX Advanced Load Balancer REST API interfaces and related automation capabilities Describe and configure NSX Advanced Load Balancer application and infrastructure monitoring Gather relevant information and perform basic troubleshooting of applications that use built-in NSX Advanced Load Balancer tooling This five-day, fast-paced course provides comprehensive training to install, configure, and manage a VMware NSX© Advanced Load Balancer? (Avi Networks) solution. This course covers key NSX Advanced Load Balancer (Avi Networks) features and functionality offered in the NSX Advanced Load Balancer 20.x release. Features include the overall infrastructure, virtual services and application components, global server load balancing, various cloud connectors, application troubleshooting, and solution monitoring. Hands-on labs provide access to a software-defined data center environment to reinforce the skills and concepts presented in the course. Course Introduction Introduction and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and their respective functions Virtual Services Configuration Concepts Explain Virtual Service components Explain Virtual Service types Explain and configure basic Virtual Service components such as Application Profiles, Network Profiles Profiles and Policies Explain and deep dive on Advanced Virtual Service creation Explain and deep dive on Application Profiles and Types such as L4, DNS, Syslog, and HTTP Explain and configure advanced application HTTP Profile options Deep dive on Network Profiles and Types Explain and configure SSL Profiles and Certificates Explain and Configure HTTP and DNS policies Pools Configuration Concepts Explain and deep dive on Pools configuration options Describe available Load Balancing algorithms Explain multiple Health Monitor types Explain multiple Persistence Profiles Explain and configure Pool Groups Modifying Application Behavior Design and apply application solutions leveraging application profiles Design and apply application solutions leveraging Network and HTTP Policies and DataScripts Explain DataScript fundamentals Explain and leverage NSX Advanced Load Balancer analytics to understand application behavior Describe and configure Client SSL Certificate Validation Describe and configure Virtual Service DDoS, Rate Limiting, and Throttling capabilities Modify Network Profiles properties such as TCP connection properties Design and apply application solutions leveraging Persistence Profiles NSX Advanced Load Balancer Infrastructure Architecture Deep dive on the management, control, data, and consumption planes and functions Describe Control Plane Clustering and High Availability Describe Controller Process Sharding Describe Controller Sizing Describe Service Engine CPU and NIC Architecture Explain Tenants Deep dive and configure properties of Service Engine Groups Explain Service Engine Group High Availability Modes Describe and configure Active/Standby High Availability Mode Describe and configure Elastic HA High Availability Mode (Active/Active, N+M) Explain Service Engine Failure Detection and Self-Healing Describe Service Engine as a Router Deep dive on Virtual Service scale out options, such as Layer 2 (Native), Layer 3 (BGP), and DNS-based Introduction to Cloud Connector Introduce Cloud Connectors Review Cloud Connector integration modes Introduce Cloud Connector types Install, Configure and Manage NSX Advanced Load Balancer in No-Access Cloud Explain No Access Cloud concepts Configure No Access Cloud integration Explain and Configure Linux Server Cloud Describe the Advanced Configuration options available in Bare-Metal (Linux Server Cloud) Install, Configure and Manage NSX Advanced Load Balancer in VMware Environment: Cloud Configuration Introduce VMware integration options Explain and configure VMware No Access Cloud Connector Explain and configure VMware Write Access Cloud Connector Describe VMware Write with NSX-V Access Cloud Connector Describe VMware NSX-T integration AWS Cloud Configuration Describe NSX Advanced Load Balancer Public Cloud integrations Explain and demonstrate AWS Public Cloud Integration DNS Foundations Review, discuss, and explain DNS fundamentals Describe NSX Advanced Load Balancer DNS and IPAM providers Global Server Load Balancing Introduce Global Server Load Balancing Concepts and Benefits Explain and configure NSX Advanced Load Balancer infrastructure Explain and configure DNS Virtual Service components Explain and configure GSLB Service Engine Group Describe and configure GSLB Sites Explain and configure basic GSLB Services to include pools and health monitors Describe GSLB Service Load Balancing algorithms Explain and configure Data and Control Plane-based Health Monitors Describe GSLB Health Monitor Proxy NSX Advanced Load Balancer: Troubleshooting Introduce Infrastructure and Application Troubleshooting Concepts Describe Control Plane and Data Plane-based Troubleshooting Explain Application Analytics and Logs Describe client logs analysis Explain Headers troubleshooting and Packet Capture mechanism Leverage CLI for detailed data plane troubleshooting Explain Service Engine Logs Explain Health Monitors troubleshooting Explain BGP session troubleshooting Describe Control Plane Troubleshooting, Clustering, and Cloud Connector issues Events and Alerts Describe NSX Advanced Load Balancer Events Describe and configure NSX Advanced Load Balancer Alerts Describe NSX Advanced Load Balancer monitoring capabilities, leveraging SNMP, Syslog, and Email Introduction to NSX Advanced Load Balancer Rest API Introduce NSX Advanced Load Balancer REST API interface Describe REST API Object Schema Explain and interact with REST API interface, leveraging browser and command line utility Explain Swagger-based API documentation Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware NSX Advanced Load Balancer: Install, Configure, Manage [V20.x] 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 VMware NSX Advanced Load Balancer: Install, Configure, Manage [V20.x] 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.