1699 Basic courses in Liverpool delivered Live Online

Our courses have been designed by experienced teachers to make getting TEFL qualified as convenient as possible. These online courses are entirely self-paced, meaning you can fit study around your life in a way that works best for you. Throughout the course, you will have the support of your own personal tutor who is with you from beginning to end to mark your work, provide helpful feedback, and answer all your questions. 120-hour Premier Online TEFL Course: 50-hour Online TEFL Course Learn about classroom management, teaching styles and advanced TEFL methodology. Includes units on business English, young learners, and teaching English one-to-one. Study with a personal tutor and meet fellow students on our course forums. 30-hour Online Grammar Course Do not know your irregular verbs from auxiliary verbs? This course will improve your basic grammar and give you the grounding you will need to communicate complicated language rules to a student audience. 20-hour Online Video Course Seeing an English teacher in action is a potent learning tool and really brings the theory to life. Feel free to steal the ideas in these video courses to use in your first lessons. We do not mind! Two 10-hour specialist mini-modules. Two mini-modules designed for specific TEFL jobs, which will help your CV stand out. One 10-hour unit is about teaching online, and the other 10-hour unit will help you teach large classes. Included in the 120-hour Premier Online TEFL Course: Lifetime access to the TEFL Job Centre The TEFL Virtual Educators course doesn’t just train you to teach; it helps you find and be offered great TEFL jobs around the world. From exclusive access to our job vacancies to a lifetime of free TEFL career advice, we’re here to help you kick-start your new career. Internationally recognised TEFL certificate – hard copy & PDF A widely-recognised certificate posted straight to your door which shows your achievements on our course, something you can show to TEFL employers to secure you a job. Personal online TEFL tutor Work through your course with the help of an experienced and qualified TEFL tutor. Throughout the course, you’ll have the same tutor, so you have one point of contact for advice and assistance. Online TEFL Class breakdown Lesson Planning Teaching English Vocabulary Teaching Pronunciation Understanding English Grammar Teaching Receptive Skills: Listening and Reading Teaching Productive Skills: Speaking and Writing Teaching English Grammar Principles of Teaching English as a Foreign Language Materials and Aids for Teaching English/Classroom Management Strategies Using Resources Effectively when Teaching English as a Foreign Language Case Study, CV and Cover Letter, Job Sites and Resources You will explore the latest theories in topics such as second language acquisition and social discourse and have the chance to apply these to your own classroom practice and curriculum development. You will also learn to conduct your own research, compile and analyse qualitative and quantitative data and develop your critical-evaluative skills. These skills will be used on your own research project at the end of the course, as well as in your future career. Core modules: Second Language Acquisition You will focus on the major themes that have emerged from literature on second language learning over the last three decades. You will examine some of the research on the second-language acquisition process, look critically at reports of second-language research, and examine some of the theories which endeavour to interpret research evidence. You will be encouraged to use your own language learning and teaching experience to assess the relative merits of such materials. Discourse in Society You will examine the relationship between language and society and the construction of discourse in various domains. You will explore sociological and sociolinguistic models and theories, such as speech communities, communities of practice and ethnolinguistic vitality, with a particular focus on social variation and stratification across various linguistic levels (phonology, lexicon, syntax). You will explore discourse's notion (or notions) in both its linguistic and wider meaning and its construction in and through society and language use. You will study methods for collecting and transcribing data and discover various linguistic and discourse analysis approaches. These methods and approaches will then be put into context and used in the analysis of the relevant social spheres and domains, such as educational or institutional discourse. Research Methods in Applied Linguistics This module will provide you with an introduction to research methods in preparation for the MA dissertation. It will familiarise you with the basic processes of conducting research, including general methodological approaches as well as research ethics. You will analyse and discuss both qualitative and quantitative data in order to develop your critical-evaluative skills. Materials and Course Design You will explore the factors involved in the design of language courses and teaching materials, reflecting on one possible course design process. You will start with an analysis of the context in which the course will take place, the needs of the learners, and current theories of language and language learning. You will move on to consider how course content can be selected and ordered in a principled way, how assessment relates to course design, and how and when courses should be evaluated. Finally, you will consider the evaluation, adaptation, and creation of course materials. Classroom Theory and Practice You will examine current research on modern classroom operations, exploring key concepts and issues through relevant professional and academic literature. A more practical element will be realised through filmed observation of teachers in practice. You will also be encouraged to reflect on your teaching and learning experience and analyse and discuss your beliefs and attitudes towards learning and teaching. Major Project This module will support you in the preparation and submission of a master’s dissertation, allowing you to explore in-depth a particular topic that reflects your academic interest. Assessment You will show your understanding of theoretical issues and their practical application through a combination of portfolios, classroom observation tasks and critical essays. After completing your final module, you will also research and prepare a 15,000-word dissertation.

Duration 1 Days 6 CPD hours This course is intended for To gain the most from attending this course you should possess the following incoming skills: Basic knowledge of programming concepts and syntax in Python. Familiarity with common data formats such as CSV, JSON, and XML. Experience using command-line interfaces and basic text editing tools. Understanding of basic machine learning concepts and algorithms. Overview Working in an interactive learning environment, led by our engaging expert, you will: Gain a solid understanding of prompt engineering concepts and their applications in software development and AI-driven solutions. Master the techniques for preprocessing and cleaning text data to ensure high-quality inputs for AI models like GPT-4. Develop expertise in GPT-4 tokenization, input formatting, and controlling model behavior for various tasks and requirements. Acquire the ability to design, optimize, and test prompts effectively, catering to diverse business applications and use cases. Learn advanced prompt engineering techniques, such as conditional text generation and multi-turn conversations, to create more sophisticated AI solutions. Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex. Understand the ethical implications and best practices in responsible AI deployment, ensuring fair and unbiased AI applications in software development. Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts. Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment. Introduction to Prompt Engineering Overview of prompt engineering and its importance in AI applications Major applications of prompt engineering in business Common challenges faced in prompt engineering Overview of GPT-4 and its role in prompt engineering Key terminology and concepts in prompt engineering Getting Things Ready: Text Preprocessing and Data Cleansing Importance of data preprocessing in prompt engineering Techniques for text cleaning and normalization Tokenization and n-grams Stop word removal and stemming Regular expressions and pattern matching GPT-4 Tokenization and Input Formatting GPT-4 tokenization and its role in prompt engineering Understanding and formatting GPT-4 inputs Context windows and token limits Controlling response length and quality Techniques for handling out-of-vocabulary tokens Prompt Design and Optimization Master the skills to design, optimize, and test prompts for various business tasks. Designing effective prompts for different tasks Techniques for prompt optimization GPT-4 system and user parameters for controlling behavior Importance of prompt testing and iteration Best practices for prompt engineering in business applications Advanced Techniques and Tools in Prompt Engineering Learn advanced techniques and tools for prompt engineering and their integration in business applications. Conditional text generation with GPT-4 Techniques for handling multi-turn conversations Overview of tools for prompt engineering: OpenAI API, OpenAI Codex, and OpenAI Playground Integration of GPT-4 with other software platforms and tools Monitoring and maintaining prompt performance Code Generation and Testing with Prompt Engineering Develop the skills to generate, integrate, and test AI-generated code effectively, enhancing productivity and creativity in software development projects. Introduction to code generation with AI models like GPT-4 Designing prompts for code generation across programming languages Techniques for specifying requirements and constraints in prompts Generating and interpreting code snippets using AI-driven solutions Integrating generated code into existing projects and codebases Best practices for testing and validating AI-generated code Ethics and Responsible AI Understand the ethical implications of prompt engineering and the importance of responsible AI deployment in business. Ethical considerations in prompt engineering Bias in AI systems and its impact on prompt engineering Techniques to minimize bias and ensure fairness Best practices for responsible AI deployment in business applications Monitoring and addressing ethical concerns in prompt engineering

Duration 1 Days 6 CPD hours This course is intended for To ensure success, students should have completed Excel Essentials and Excel Functions Including Pivot Tables and Lookups or have the equivalent knowledge and experience. Overview Upon successful completion of this course, students will be able to enhance productivity and efficiency by streamlining workflow, collaborate with others, and audit and analyse data. This course is designed for students desiring to gain skills necessary to create macros, collaborate with others, audit and analyse data, incorporate multiple data sources, and import data. Working with Multiple Worksheets and Workbooks Working with Named Ranges Link Cells Across Worksheets and Workbooks Use 3D References to Calculate Across Worksheets Consolidate Data Use Formula Auditing and Error Checking Reveal Formulas Trace Cell Precedents and Dependents Locate Errors in Formulas Watch and Evaluate Formulas Reviewing and Protecting Workbooks Control Data Entry via Data Validation Protect Workbook Access Protect Worksheets and Cell Content Add and Edit Comments Prepare a Workbook for Distribution Modify Excel's Default Settings Using Macros to Automate Workbook Functionality Create Macros via Recording Run Macros via Buttons and Shortcuts Assign Macros to the Quick Access Toolbar and Ribbon Assign Macros to Objects View Macro Code Forecasting and Analysis Data Use Conditional Formatting to Highlight, Sort and Filter Key Data Advanced Conditional Formatting using Formulas Create Sparklines to Visualise Data Add Trendlines to Charts to Visualise and Forecast Trends Use Data Tables and Scenarios to Project Potential Outcomes Use Goal Seek to Calculate Outcomes Forecast Data Trends Using Solver

Duration 5 Days 30 CPD hours This course is intended for Linux Professional Institute Certification (LPIC-1) 102 training is suitable for individuals with roles of: System administrator Network administrator Technician DevOps Overview Upon successful completion of this course, students will be able to: customize shell environments to meet users' needs customize existing scripts or write simple new Bash scripts install and configure X11 add, remove, suspend and change user accounts use cron and systemd timers to run jobs at regular intervals and to use at to run jobs at a specific time localize a system in a different language than English properly maintain the system time and synchronize the clock via NTP manage print queues and user print jobs using CUPS and the LPD compatibility interface manage the persistent network configuration of a Linux host configure DNS on a client host review system configuration to ensure host security in accordance with local security policies know how to set up a basic level of host security use public key techniques to secure data and communication. This course prepares students to take the 102 exam of the LPI level 1 certification. Shells and Shell Scripting Customize and use the shell environment Customize or write simple scripts User Interfaces and Desktops Install and configure X11 Graphical Desktops Accessibility Administrative Tasks Manage user and group accounts and related system files Automate system administration tasks by scheduling jobs Localisation and internationalisation Essential System Services Maintain system time System logging Mail Transfer Agent (MTA) basics Manage printers and printing Networking Fundamentals Fundamentals of internet protocols Persistent network configuration Basic network troubleshooting Configure client side DNS Security Perform security administration tasks Setup host security Securing data with encryption Additional course details: Nexus Humans Linux Professional Institute Certification (LPIC-1) 102 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 Linux Professional Institute Certification (LPIC-1) 102 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 Anyone who plans to work with Kubernetes at any level or tier of involvement Any company or individual who wants to advance their knowledge of the cloud environment Application Developers Operations Developers IT Directors/Managers Overview All topics required by the CKAD exam, including: Deploy applications to a Kubernetes cluster Pods, ReplicaSets, Deployments, DaemonSets Self-healing and observable applications Multi-container Pod Design Application configuration via Configmaps, Secrets Administrate cluster use for your team A systematic understanding of Kubernetes architecture Troubleshooting and debugging tools Kubernetes networking and services Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stability while maximizing resource utilization for applications and services. By the conclusion of this hands-on training, you will go back to work with all necessary commands and practical skills to empower your team to succeed, as well as gain knowledge of important concepts like Kubernetes architecture and container orchestration. We prioritize covering all objectives and concepts necessary for passing the Certified Kubernetes Application Developer (CKAD) exam. You will command and configure a high availability Kubernetes environment (and later, build your own!) capable of demonstrating all ?K8s'' features discussed and demonstrated in this course. Your week of intensive, hands-on training will conclude with a mock CKAD exam that matches the real thing. Kubernetes Architecture Components Understand API deprecations Containers Define, build and modify container images Pods Master Services Node Services K8s Services YAML Essentials Creating a K8s Cluster kubectl Commands Kubernetes Resources Kubernetes Namespace Kubernetes Contexts Pods What is a Pod? Create, List, Delete Pods How to Access Running Pods Kubernetes Resources Managing Cloud Resource Consumption Multi-Container Pod Design Security Contexts Init Containers Understand multi-container Pod design patterns (e.g. sidecar, init and others) Pod Wellness Tracking Networking Packet Forwarding ClusterIP and NodePort Services Provide and troubleshoot access to applications via services Ingress Controllers Use Ingress rules to expose applications NetworkPolicy resource Demonstrate basic understanding of NetworkPolicies Network Plugins Defining the Service Mesh Service mesh configuration examples ReplicaSets Services ReplicaSet Function Deploying ReplicaSets Deployments Deployment Object Updating/Rolling Back Deployments Understand Deployments and how to perform rolling updates Deployment Strategies Use Kubernetes primitives to implement common deployment strategies (e.g. blue/green or canary) Scaling ReplicaSets Autoscaling Labels and Annotations Labels Annotations Node Taints and Tolerations Jobs The K8s Job and CronJob Understand Jobs and CronJobs Immediate vs. scheduled internal use Application Configuration Understanding and defining resource requirements, limits and quotas Config Maps Create & consume Secrets Patching Custom Resource Definition Discover and use resources that extend Kubernetes (CRD) Managing ConfigMaps and Secrets as Volumes Storage Static and dynamic persistent volumes via StorageClass K8s volume configuration Utilize persistent and ephemeral volumes Adding persistent storage to containers via persistent volume claims Introduction to Helm Helm Introduction Charts Use the Helm package manager to deploy existing packages Application Security Understand authentication, authorization and admission control Understand ServiceAccounts Understand SecurityContexts Application Observability and Maintenance Use provided tools to monitor Kubernetes applications How to Troubleshoot Kubernetes Basic and Advanced Logging Techniques Utilize container logs Accessing containers with Port-Forward Debugging in Kubernetes Hands on Labs: Define, build and modify container images Deploy Kubernetes using Ansible Isolating Resources with Kubernetes Namespaces Cluster Access with Kubernetes Context Listing Resources with kubectl get Examining Resources with kubectl describe Create and Configure Basic Pods Debugging via kubectl port-forward Imperative vs. Declarative Resource Creation Performing Commands inside a Pod Understanding Labels and Selectors Insert an Annotation Create and Configure a ReplicaSet Writing a Deployment Manifest Perform rolling updates and rollbacks with Deployments Horizontal Scaling with kubectl scale Implement probes and health checks Understanding and defining resource requirements, limits and quotas Understand Jobs and CronJobs Best Practices for Container Customization Persistent Configuration with ConfigMaps Create and Consume Secrets Understand the Init container multi-container Pod design pattern Using PersistentVolumeClaims for Storage Dynamically Provision PersistentVolumes with NFS Deploy a NetworkPolicy Provide and troubleshoot access to applications via services Use Ingress rules to expose applications Understand the Sidecar multi-container Pod design pattern Setting up a single tier service mesh Tainted Nodes and Tolerations Use the Helm package manager to deploy existing packages A Completed Project Install Jenkins Using Helm and Run a Demo Job Custom Resource Definitions (CRDs) Patching Understanding Security Contexts for Cluster Access Control Utilize container logs Advanced Logging Techniques Troubleshooting Calicoctl Deploy a Kubernetes Cluster using Kubeadm Monitoring Applications in Kubernetes Resource-Based Autoscaling Create ServiceAccounts for use with the Kubernetes Dashboard Saving Your Progress With GitHub CKAD Practice Drill Alta Kubernetes Course Specific Updates Sourcing Secrets from HashiCorp Vault Example CKAD Test Questions

Welcome to the Women's Voice Medicine Journey. This is a female-designed, new, step-by-step guide, to teach you how you can truly activate and integrate some of the most essential Voicework tools to Free Your Voice and unleash your Creative Feminine Power.

Duration 4 Days 24 CPD hours This course is intended for #NAME? Overview At the completion of this course, students already familiar with the RHCT/RHCSA administration skills will have exposure to all competencies tested by the RHCSA and RHCE exams. If you are an experienced Linux© system administrator and hold a Red Hat Certified System Administrator (RHCSA©) credential or possess equivalent skills and want to broaden your ability to administer Linux systems at an enterprise level, this is the perfect course.The course will empower you to deploy and manage network servers running caching domain name service (DNS), MariaDB, Apache HTTPD, Postfix SMTP null clients, network file sharing with network file system (NFS) and server message block (SMB), iSCSI initiators and targets, advanced networking and firewall configurations, and to use bash shell scripting to help automate, configure, and troubleshoot your system. Through lectures and hands-on labs, you will be exposed to all competencies covered by the Red Hat Certified Engineer (RHCE) exam (EX300), supplementing what you have already learned in earning your RHCSA credential.This course is based on Red Hat© Enterprise Linux 7. Getting Started with the Classroom Environment Given a virtualized environment, begin to administrate multiple systems using prerequisite skills Enhance User Security Configure system to use Kerberos to verify credentials and grant privileges via sudo Bash Scripting and Tools Automate system administration tasks utilizing Bash scripts and text-based tools File Security with GnuPG Secure files with GnuPG. Software Management Use yum plugins to manage packages and understand the design of packages to build a simple package Network Monitoring Profile running services then capture and analyze network traffic Route Network Traffic Configure system to route traffic and customize network parameters with sysctl Secure Network Traffic Secure network traffic through SSH port forwarding and iptables filtering/network address translation (NAT) NTP Server Configuration Configure an NTP server Filesystems and Logs Manage local file system integrity, monitor system over time, and system logging Centralized and Secure Storage Access centralized storage (iSCSI) and encrypt filesystems SSL-encapsulated Web Services Understand SSL certificates and deploy an SSL encapsulated web service Web Server Additional Configuration Configure web server with virtual hosts, dynamic content, and authenticated directories Basic SMTP Configuration Configure an SMTP server for basic operation (null client, receiving mail, smarthost relay) Caching-Only DNS Server Understand DNS resource records and configure a caching-only name server File Sharing with NFS Configure file sharing between hosts with NFS File Sharing with CIFS Configure file and print sharing between hosts with CIFS File Sharing with FTP Configure file sharing with anonymous FTP Troubleshooting Boot Process Understand the boot process and recover unbootable systems with rescue mode

Duration 5 Days 30 CPD hours This course is intended for Practiced Linux system administrators who currently manage Linux servers at the enterprise level - Skilled Linux system administrators who who want or are required to demonstrate competencies by earning the RHCSA This course is designed for experienced Linux System Administrators who want to harden their technical skill sets and become accredited with the RHCSA certification. Accessing the command line Log in to a Linux system and run simple commands using the shell. Managing Files from the command line Work with files from the bash shell prompt. Managing local Linux users and groups Manage Linux users and groups and administer local password policies. Controlling access to files with Linux file system permissions Set access permissions on files and interpret the security effects of different permission settings. Managing SELinux security Use SELinux to manage access to files and interpret and troubleshoot SELinux security effects. Monitoring and managing Linux processes Monitor and control processes running on the system. Installing and updating software packages Download, install, update, and manage software packages from Red Hat and yum package repositories. Controlling services and daemons Control and monitor network services and system daemons using systemd. Managing Red Hat Enterprise Linux networking Configure basic IPv4 networking on Red Hat Enterprise Linux systems. Analyzing and storing logs Locate and interpret relevant system log files for troubleshooting purposes. Managing storage and file systems Create and use disk partitions, logical volumes, file systems, and swap spaces. Scheduling system tasks Schedule recurring system tasks using cron and systemd timer units. Mounting network file systems Mount network file system (NFS) exports and server message block (SMB) shares from network file servers. Limiting network communication with firewalld Configure a basic local firewall. Additional course details: Nexus Humans Enterprise Linux System Admin Fast Track with Exam Prep 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 Enterprise Linux System Admin Fast Track with Exam Prep 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 2 Days 12 CPD hours This course is intended for This course is intended for software testers, architects, engineers, or other related roles, who wish to apply AI to software testing practices within their enterprise. While there are no specific pre-requisites for this course, it would be helpful is the attendee has familiarity with basic scripting (Python preferred) and be comfortable with working from the command line (for courses that add the optional hands-on labs). Attendees without basic scripting skills can follow along with the hands-on labs or demos. Overview This course introduces AI and related technologies from a practical applied software testing perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore: Exploring AI Introduction to Machine Learning Introduction to Deep Learning Introduction to Data Science Artificial Intelligence (AI) in Software Testing Implementing AI in Test Automation Innovative AI Test Automation Tools for the Future Implementing AI in Software Testing / AI in Test Automation is an introductory-level course for attendees new to AI, Machine Learning or Deep Learning who wish to automate software testing tasks leveraging AI. The course explores the essentials of AI, ML and DL and how the integrate into IT business operations and initiatives. Then the course moves to specifics about the skills, techniques and tools used to apply AI to common software testing requirements. Exploring AI AI-Initiatives The Priority: Excellence AI- Intelligence Types The Machine Learning Types The Quality Learning Initiative The Inception in Academics AI - Importance & Applications The Re-visit Learning Re-visited via AI Teaching in the world of AI Exploring AI for Self-Development AI In Academics Beyond Academics Introduction to Machine Learning What is Machine Learning? Why Machine Learning? Examples - Algorithms behind Machine Learning Introduction to Deep Learning What is Deep Learning? Why Deep Learning? Example - Deep Learning Vs Machine Learning Introduction to Data Science What is Data Science? Why Data Science? Examples - Use Cases of Data Science Artificial Intelligence (AI) in Software Testing What is AI in Software Testing? The Role of AI Testing Why do we Need AI in Software Testing? Pros and Cons of AI in Software Testing Applications of AI in Software Testing Is it time for Testers or QA Teams to worry about AI? Automated Testing with Artificial Intelligence Implementing AI in Test Automation Training the AI Bots Challenges with AI-powered Applications Examples - Real World use cases using Artificial Intelligence Demo - Facial Emotion Detection Using Artificial Intelligence Demo - Text Analysis API Using Artificial Intelligence Demo - EYE SPY Mobile App Using Artificial Intelligence Innovative AI Test Automation Tools for the Future Tools used for Implementing AI in Automation Testing What is NEXT? AI Test Automation Demo using Testim

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client