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About this Training Course This 3 full-day course will provide a comprehensive understanding of the various types of transformer maintenance including breakdown maintenance, preventive maintenance, total productive maintenance, condition-based maintenance, proactive maintenance, and reliability-centered maintenance. All the expected problems in dry and oil-filled transformers will be discussed in detail. All the diagnostics, troubleshooting and maintenance required to ensure adequate operation of transformers will be covered thoroughly. This course will focus on maximizing the efficiency, reliability, and longevity of all types of transformers by providing an understanding of all commissioning requirements, repair and refurbishment methods of transformers. Training Objectives Equipment Diagnostics and Inspection: Learn in detail all the diagnostic techniques and inspections required of critical components of transformers Equipment Testing: Understand thoroughly all the routine tests, type tests, and special tests required for the various types of transformers Equipment Maintenance and Troubleshooting: Determine all the maintenance and troubleshooting activities required to minimize transformer downtime and operating cost Equipment Repair and Refurbishment: Gain a detailed understanding of the various methods used to repair and refurbish transformers Efficiency, Reliability, and Longevity: Learn the various methods used to maximize the efficiency, reliability, and longevity of transformers Equipment Sizing: Gain a detailed understanding of all the calculations and sizing techniques used for transformers Design Features: Understand all the design features that improve the efficiency and reliability of transformers Equipment Selection: Learn how to select all types of transformers by using the performance characteristics and selection criteria that you will learn in this course Equipment Commissioning: Understand all the commissioning requirements for transformers Equipment Codes and Standards: Learn all the codes and standards applicable for transformers Equipment Causes and Modes of Failure: Understand the causes and modes of failure in transformers System Design: Learn all the requirements for designing different types of transformer systems Target Audience Engineers of all disciplines Managers Technicians Maintenance personnel Other technical individuals (this course is suitable for individuals who do not have an electrical background) Course Level Basic or Foundation Training Methods Your specialist course leader relies on a highly interactive training method to enhance the learning process. This method ensures that all participants gain a complete understanding of all topics covered. The training environment is highly stimulating, challenging, and effective because the participants will learn by case studies which will allow them to apply the material taught to their own organization. Each delegate will receive a copy of the following materials written by the instructor: Excerpt of the relevant chapters from the 'ELECTRICAL EQUIPMENT HANDBOOK' published by McGraw-Hill in 2003 (600 pages) Transformer Testing, Maintenance and Commissioning Manual (covering all the tests, maintenance activities, protective systems and all commissioning procedures for all types of transformers - 350 pages) Trainer Your specialist course leader has more than 32 years of practical engineering experience with Ontario Power Generation (OPG), one of the largest electric utility in North America. He was previously involved in research on power generation equipment with Atomic Energy of Canada Limited at their Chalk River and Whiteshell Nuclear Research Laboratories. While working at OPG, he acted as a Training Manager, Engineering Supervisor, System Responsible Engineer and Design Engineer. During the period of time, he worked as a Field Engineer and Design Engineer, he was responsible for the operation, maintenance, diagnostics, and testing of gas turbines, steam turbines, generators, motors, transformers, inverters, valves, pumps, compressors, instrumentation and control systems. Further, his responsibilities included designing, engineering, diagnosing equipment problems and recommending solutions to repair deficiencies and improve system performance, supervising engineers, setting up preventive maintenance programs, writing Operating and Design Manuals, and commissioning new equipment. Later, he worked as the manager of a section dedicated to providing training for the staff at the power stations. The training provided by him covered in detail the various equipment and systems used in power stations. In addition, he has taught courses and seminars to more than four thousand working engineers and professionals around the world, specifically Europe and North America. He has been consistently ranked as 'Excellent' or 'Very Good' by the delegates who attended his seminars and lectures. He written 5 books for working engineers from which 3 have been published by McGraw-Hill, New York. Below is a list of the books authored by him; Power Generation Handbook: Gas Turbines, Steam Power Plants, Co-generation, and Combined Cycles, second edition, (800 pages), McGraw-Hill, New York, October 2011. Electrical Equipment Handbook (600 pages), McGraw-Hill, New York, March 2003. Power Plant Equipment Operation and Maintenance Guide (800 pages), McGraw-Hill, New York, January 2012. Industrial Instrumentation and Modern Control Systems (400 pages), Custom Publishing, University of Toronto, University of Toronto Custom Publishing (1999). Industrial Equipment (600 pages), Custom Publishing, University of Toronto, University of Toronto, University of Toronto Custom Publishing (1999). Furthermore, he has received the following awards: The first 'Excellence in Teaching' award offered by PowerEdge, Singapore, in December 2016 The first 'Excellence in Teaching' award offered by the Professional Development Center at University of Toronto (May, 1996). The 'Excellence in Teaching Award' in April 2007 offered by TUV Akademie (TUV Akademie is one of the largest Professional Development centre in world, it is based in Germany and the United Arab Emirates, and provides engineering training to engineers and managers across Europe and the Middle East). Awarded graduation 'With Distinction' from Dalhousie University when completed Bachelor of Engineering degree (1983). Lastly, he was awarded his Bachelor of Engineering Degree 'with distinction' from Dalhousie University, Halifax, Nova Scotia, Canada. He also received a Master of Applied Science in Engineering (M.A.Sc.) from the University of Ottawa, Canada. He is also a member of the Association of Professional Engineers in the province of Ontario, Canada. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

Learners will be introduced to the design, installation, commissioning, and maintenance of CCTV systems.

Level 4 Endorsed Diploma - International Trade Qualification Complete all 15 modules and 3 assessments, from a choice of 7 to earn a Level 4 Diploma in International Trade.

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for experienced administrators with a background in either software development or system administration. Team leaders, lead developers, and other accidental Team Foundation Server administrators are also encouraged to take this course. This course will also help a student prepare for the relevant Microsoft certification exam. Overview At course completion, attendees will have had exposure to: - TFS editions and components - Supported topologies and environments - Must-have TFS administration tools - Hardware and software requirements - Required service accounts and permissions - Installing Team Foundation Server - Configuring Team Foundation Server - Managing relevant SQL Server components - Managing relevant SharePoint components - Installing and using Team Explorer - Team project collections and team projects - Using and customizing process templates - TFS licensing and Client Access Licenses - Connecting and using Microsoft Excel & Project - Installing and using Team Explorer Everywhere - Integrating TFS and SharePoint - Using the TFS web portal - Git and TFVC version control systems - Basic and advanced version control workflows - Configuring and using code search - Controlling access to version controlled items - Command-line and third party tools - Configuring automated builds - Deploying and using Visual Studio agents - Configuring agent pools and queues - Creating and queuing automated builds - Configuring Package Management - Configuring Release Management - Creating a release definition - Creating and deploying a release - Strategies for upgrading and migrating to TFS - Integrating TFS with other systems and data - High availability and scalability options - Capacity planning and disaster recovery - Backing up, restoring, and moving TFS data - Managing the data warehouse - Using PowerShell to manage TFS - Customizing Team Foundation Server - Extending Team Foundation Server Provides students with the knowledge and skills to deploy, configure, and manage Microsoft Team Foundation Server 2019 and related software components. Introduction to Team Foundation Server Introduction to Team Foundation Server Editions, components, and configurations Visual Studio Team Services comparison TFS' support of Application Lifecycle Management TFS administrator responsibilities and tasks ?Must-have? tools of a TFS administrator Planning and Deploying TFS Planning the deployment System requirements, software, and accounts Installing and configuring TFS Installing Team Explorer Troubleshooting Configuring TFS Administrator roles and tools Managing team project collections Managing team projects Managing process templates Securing TFS, SharePoint, and SQL Server Renaming and deleting a team project Client Applications TFS Client Access Licenses (CAL) Team Explorer and the web portal Microsoft Excel and Microsoft Project SharePoint project portal Team Explorer Everywhere Command-line and 3rd party tools Version Control Overview of Git and TFVC version control systems Integration with Visual Studio Using TFVC and Git version control Basic and advanced workflows Controlling access to version control Command-line tools and utilities TFS Proxy, MSSCCI Provider, and TFS Sidekicks Building and Releasing Overview of the Visual Studio build system Build agents, agent pools, agent queues Creating and queuing a build Monitoring, and managing a build Securing the build process Running tests as part of the build Overview of Package Management Overview of Release Management Defining, creating, and deploying a release Upgrading, Migrating, and Integrating Upgrading Team Foundation Server In-place vs. migration upgrade Performing post-upgrade tasks Migrating work items Migrating items under version controlled Integrating with Team Foundation Server Custom and 3rd party solutions Advanced Administration Monitoring the health of Team Foundation Server Web-based diagnostic tools Options for scalability and high availability Disaster recovery, backup, and restore Moving Team Foundation Server Managing the data warehouse Using PowerShell to manage TFS Customizing and Extending Customizing vs. extending Customizing a process template Customizing a work item type Creating default work items Creating and using a global list Using Witadmin.exe Using work item templates Creating a custom report Using the REST API to extend Team Foundation Server Additional course details: Nexus Humans Administering Team Foundation Server 2017 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 Administering Team Foundation Server 2017 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 course is appropriate for advanced users, system administrators and web site administrators who want to use Python to support their server installations, as well as anyone else who wants to automate or simplify common tasks with the use of Python scripts. Students can apply the course skills to use Python in basic web development projects or automate or simplify common tasks with the use of Python scripts. Overview This skills-focused course is about 50% hands-on lab to lecture ratio, 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 expert instructor, you'll learn how to: Create working Python scripts following best practices Use python data types appropriately Read and write files with both text and binary data Search and replace text with regular expressions Work with with the standard library and its work-saving modules Create 'real-world', professional Python applications Know when to use collections such as lists, dictionaries, and sets Work with Pythonic features such as comprehensions and iterators Write robust code using exception handling Introduction to Python Programming Basics is a hands-on Python programming course that teaches you the key skills you?ll need to get started with programming in Python to a solid foundational level. The start of the course will lead you through writing and running basic Python scripts, and then guide you through how to use more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting. This course provides you with an excellent kick start for users new to Python and scripting, enabling you to quickly use basic Python skills on the job in a variety of ways. You?ll be able use Python in basic web development projects, or use it to automate or simplify common tasks with the use of Python scripts. The course also serves as a solid primer course / foundation for continued Python study in support for next level web development with Python, using Python in DevOps, Python for data science / machine learning or Python for systems admin or networking support. Python Quick View What is Python? Python timeline Advantages/disadvantages Installing Python Getting help The Python Environment Starting Python Using the interpreter Running a Python script Editors and IDEs Getting Started with Python Using variables Builtin functions String data Numberic data Converting types Console input/output Command line parameters Flow Control About flow control The if statement Relational and Boolean operators while loops Exiting from loops Array Types About array types Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions and generators Working with Files File overview Opening a text file Reading a text file Writing to a text file Dictionaries and Sets About dictionaries Creating dictionaries Iterating through a dictionary About sets Creating sets Working with sets Functions Defining functions Returning values Parameters and arguments Variable scope Sorting The sorted() function Custom sort keys Lambda functions Sorting in reverse Using min() and max() Errors and Exception Handling Exceptions Using try/catch/else/finally Handling multiple exceptions Ignoring exceptions Modules and Packages Creating Modules The import statement Module search path Using packages Function and module aliases Getting Started with Object Oriented Programming and Classes About object-oriented programming Defining classes Constructors Understanding self Properties Instance Methods and data Class methods and data Inheritance Additional course details: Nexus Humans Introduction to Python Programming Basics (TTPS4800) 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 Introduction to Python Programming Basics (TTPS4800) 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.

Learn how create and structure enterprise projects and programmes. Course overview Duration: 2 days (13 hours) Our P6 Project Planning and Controls Fundamentals course is an intensive two day course aimed at experienced planners and project controllers who need to use Primavera to create and manage detailed plans. It includes creating EPS levels, projects, WBS levels and detailed activity and resource planning. Experience of project planning and scheduling techniques is essential. Objectives  By the end of the course you will be able to: Create a programme structure Create projects and set project properties Create programme milestones Create a Work Breakdown Structure (WBS) Create detailed plans including activities, links and resources Progress the schedule Manage actuals Customise layouts Use the reporting tools in Primavera Content Programme Management Creating EPS elements Defining the programme structure Navigating the EPS structure Finding programmes Project Management and WBS Creating projects Setting project properties Validating projects Assigning project codes Building a work breakdown structure Creating a WBS structure Creating WBS elements Work package management Top Down budgets Allocating top down budgets Budget change Programming milestones and activity planning Creating programme milestones Setting constraints Linking milestones Scheduling Using the schedule function Detailed activity planning Creating activities Relationship types Creating relationships Adding milestones Assigning activity codes Resourcing, workloads and baselining Resource types Creating resources Resource attributes Assigning resources Switching resources Split load resource assignment Reduced hours resource assignment Checking workload Reviewing workload Dealing with resource conflicts Assignments view Baselining Creating baselines Assigning baselines Working with layouts Creating layouts Customising columns Setting filters Sorting and grouping Changing the timescale Customising the Gantt Creating activity code breakdown structures Progressing the schedules Updating task status and remaining duration Setting the data date Monitoring and reporting Exporting and importing information Primavera standard reports Creating custom reports Creating portfolios Printing Printing your schedule Printing to other packages

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

Copper cabling systems training course description A hands on training course covering installation, termination and testing of copper cables in an internal environment. The course covers all copper cabling but hands on sessions focus on unshielded Twisted Pair. What will you learn Recognise different copper cables and when to use them. Install and terminate UTP cables. Test and certify UTP cables. Copper cabling systems training course details Who will benefit: Professional cable installers. Prerequisites: None Duration 2 days Copper cabling systems training course contents Communications principles Use of cables in data networks, Use of cables in telephone networks, conductors and insulators, current, resistance, voltage and Ohms law. Copper cabling per cabling Coaxial versus twisted pair. UTP and STP. Cat 5, 5e, 6 and 7. Straight through, cross over, others. Hands on: Building a simple network. Cable termination Cable termination Preparing cables for termination, termination techniques, termination tools. Wiring standards, colour codes. Hands on: Making your own cables and using them in the simple network. Cable architectures Structure cabling system components, topologies, horizontal wiring, vertical wiring, telecomms rooms, server rooms. Hubs, switches and routers implications. Trunking. Cabling documentation, symbols and abbreviations. Hands on: punch blocks, testing continuity. Cable installation Site surveys: pathways and spaces, support structures, distance limitations. Recommended installation practices, laying and setup, retrofits. Health and safety factors, fire and building codes. Hands on: Performing a site survey, installing cable with floor boxes. Testing Basic testing, volt meters, tone set, Cat 5 testers, Cat 5e testers, Cat 6 testers. Certifying performance, Cat 5, 5e and 6 performance parameters. External factors. Common faults. Hands on: Testing an installation, troubleshooting faults. Other aspects Telephony cables, shielded cables. Hands on: installing telephone cable, testing continuity.

Supporting and engaging with different parts of the organisation and interact with internal or external customer.