976 Python courses in Leighton Buzzard delivered Online

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Career Path The certification and skills you get from this Microsoft Power BI Masterclass Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. Course Curriculum Section 01: Introduction Welcome! 00:01:00 What is Power BI? 00:03:00 Download & Installing Power BI Desktop 00:04:00 Getting to know the interface 00:03:00 Mini Project: Transform Data 00:07:00 Mini Project: Visualize Data 00:05:00 Mini Project: Creating a Data Model 00:07:00 Course Outline: What will you learn in this course? 00:05:00 How to learn best with this course? 00:03:00 Section 02: Preparing our Project Creating our initial project file 00:04:00 Working with the attached project files 00:04:00 Section 03: Data Transformation - The Query Editor Exploring the Query Editor 00:06:00 Connecting to our data source 00:07:00 Editing rows 00:08:00 Changing data types 00:08:00 Replacing values 00:03:00 Close & Apply 00:03:00 Connecting to a csv file 00:03:00 Connecting to a web page 00:05:00 Extracting characters 00:06:00 Splitting & merging columns 00:09:00 Creating conditional columns 00:06:00 Creating columns from examples 00:09:00 Merging Queries 00:17:00 Pivoting & Unpivoting 00:06:00 Appending Queries 00:08:00 Practice & Solution: Population table 00:15:00 The Fact-Dimension-Model 00:09:00 Practice: Load the dimension table 00:04:00 Organizing our queries in groups 00:03:00 Entering data manually 00:05:00 Creating an index column 00:03:00 Workflow & more transformations 00:05:00 Module summary 00:05:00 Exercise 1 - Instruction 00:02:00 Exercise Solution 00:11:00 Section 04: Data Transformation - Advanced Advanced Editor - Best practices 00:09:00 Performance: References vs. Duplicating 00:10:00 Performance: Enable / Disable Load & Report Refresh 00:05:00 Group by 00:05:00 Mathematical Operations 00:05:00 Run R Script 00:15:00 Using Parameters to dynamically transform data 00:06:00 M formula language: Basics 00:07:00 M formula language: Values, Lists & Tables 00:14:00 M formula language: Functions 00:13:00 M formula language: More functions & steps 00:05:00 Exercise 2 - Instructions 00:01:00 Exercise 2 - solution 00:05:00 Section 05: Creating a Data Model Understanding the relationship 00:05:00 Create & edit relationships 00:06:00 One-to-many & one-to-one relationship 00:06:00 Many-to-many (m:n) relationship 00:08:00 Cross filter direction 00:06:00 Activate & deactivate relationships 00:06:00 Model summary 00:03:00 Exercise 3 Create Model 00:02:00 Exercise 3 Solution 00:02:00 Section 06: Data Visualization Our first visual 00:08:00 The format tab 00:12:00 Understanding tables 00:10:00 Conditional formatting 00:09:00 The Pie Chart 00:06:00 All about the filter visual 00:13:00 The filter pane for developers 00:09:00 Cross filtering & edit interactions 00:04:00 Syncing slicers across pages 00:07:00 Creating drill downs 00:08:00 Creating drill throughs 00:07:00 The tree map visual 00:07:00 The decomposition tree 00:05:00 Understanding the matrix visual 00:05:00 Editing pages 00:07:00 Buttons & Actions 00:09:00 Bookmarks to customize your report 00:10:00 Analytics and Forecasts with line charts 00:10:00 Working with custom visuals 00:07:00 Get data using R Script & R Script visual 00:08:00 Asking questions - Q&A visual 00:04:00 Wrap up - data visualization 00:08:00 Section 07: Power BI & Python Python in Power BI - Plan of attack 00:03:00 Setting up Python for Power BI 00:03:00 Transforming data using Python 00:11:00 Creating visualizations using Python 00:08:00 Violin plots, pair plots & ridge plots using Python 00:15:00 Machine learning (BayesTextAnalyzer) using Python 00:00:00 Performance & Troubleshooting 00:03:00 Section 08: Storytelling with Data Introduction 00:01:00 Show Empathy & Identify the Requirement 00:03:00 Finding the Most Suitable KPI's 00:02:00 Choose an Effective Visualization 00:04:00 Make Use of Natural Reading Pattern 00:03:00 Tell a Story Using Visual Cues 00:05:00 Avoid Chaos & Group Information 00:02:00 Warp Up - Storytelling with Data 00:02:00 Section 09: DAX - The Essentials Introduction 00:03:00 The project data 00:04:00 Measures vs. Calculated Columns 00:15:00 Automatically creating a date table in DAX 00:08:00 CALENDAR 00:05:00 Creating a complete date table with features 00:04:00 Creating key measure table 00:03:00 Aggregation functions 00:06:00 The different versions of COUNT 00:14:00 SUMX - Row based calculations 00:09:00 Section 10: DAX - The CALCULATE function CALCULATE - The basics 00:11:00 Changing the context with FILTER 00:07:00 ALL 00:08:00 ALL SELECTED 00:03:00 ALL EXCEPT 00:07:00 Section 11: Power BI Service - Power BI Cloud How to go on now? 00:03:00 Power BI Pro vs Premium & Signing up 00:04:00 Exploring the interface 00:04:00 Discovering your workspace 00:03:00 Connecting Power BI Desktop & Cloud 00:04:00 Understanding datasets & reports 00:03:00 Working on reports 00:04:00 Updating reports from Power BI Desktop 00:04:00 Creating and working with workspaces 00:07:00 Installing & using a data gateway 00:13:00 Get Quick Insights 00:03:00 Creating dashboards 00:04:00 Sharing our results through Apps 00:10:00 Power BI Mobile App 00:05:00 Creating the layout for the Mobile App 00:04:00 Wrap up - Power BI Cloud 00:07:00 Section 12: Row-Level Security Introduction 00:03:00 Creating a Row-Level Security 00:05:00 Row-Level Security in the Cloud 00:04:00 Row-Level Security & Data Model 00:05:00 Dynamic Row-Level Security 00:07:00 Dynamic Many-to-Many RLS 00:04:00 Hierarchical Row-Level Security 00:13:00 Section 13: More data sources JSON & REST API 00:10:00 Setting up a local MySQL database 00:14:00 Connecting to a MySQL database in Power BI 00:05:00 Connecting to a SQL database (PostgreSQL) 00:05:00 Section 14: Next steps to improve & stay up to date Congratulations & next steps 00:06:00 The End 00:01:00 Resources Resources - Microsoft Power BI Masterclass 00:00:00

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Ever wondered how machines recognise faces, detect traffic signs, or even tag photos with uncanny accuracy? This course dives straight into the heart of Convolutional Neural Networks (CNNs) – the very engine behind image recognition and deep learning breakthroughs. With a clear focus on project-based learning, you’ll explore how CNNs work, how they’re built, and how they’re trained to see and interpret the world digitally. The content flows logically and stays rooted in clarity, making even the most complex architectures feel almost polite. This is not just a sequence of slides and jargon. It’s a well-structured digital journey tailored for learners who want to confidently grasp how deep learning models behave and evolve. Whether you're brushing up on your neural network knowledge or aiming to reinforce your AI expertise, the course serves up algorithms, code walkthroughs and layered insights with a tone that’s informative, direct, and occasionally dry-witted. If you fancy turning raw data into pixel-level predictions using nothing but code, logic, and neural layers — you’re exactly where you need to be. Learning Outcomes: Gain a solid understanding of convolutional neural networks and their applications in deep learning. Learn how to install the necessary packages and set up a dataset structure for deep learning projects. Discover how to create your own convolutional neural network model and layers using Python. Understand how to preprocess and augment data for advanced image recognition tasks. Learn how to evaluate the accuracy of your models and understand the different models available for deep learning projects. The Deep Learning Projects - Convolutional Neural Network course is designed to provide you with the skills and knowledge you need to build your own advanced deep learning projects. Using Python, you'll learn how to install the necessary packages, set up a dataset structure, and create your own convolutional neural network model and layers. You'll also learn how to preprocess and augment data to enhance the accuracy of your models and evaluate the performance of your models using data generators. Deep Learning Projects - Convolutional Neural Network Course Curriculum Section 01: Introduction Section 02: Installations Section 03: Getting Started Section 04: Accuracy How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Data analysts who want to expand their skills in deep learning and convolutional neural networks. Programmers who want to learn how to build advanced models for image recognition. Entrepreneurs who want to develop their own deep learning-based applications for image recognition. Students who want to enhance their skills in deep learning and prepare for a career in the field. Anyone who wants to explore the world of convolutional neural networks and deep learning projects. Career path Data Analyst: £24,000 - £45,000 Machine Learning Engineer: £28,000 - £65,000 Computer Vision Engineer: £30,000 - £70,000 Technical Lead: £40,000 - £90,000 Chief Technology Officer: £90,000 - £250,000 Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. Course Curriculum Introduction Introduction And What You Will get When You Join 00:03:00 Who We Are. 00:05:00 Qucik Intro To Raspberry Pi World 00:06:00 10 Uses for Raspberry Pi Board 00:03:00 Very Important Note: Review Process 00:00:00 Different Versions of Raspberry Pi 00:03:00 Raspberry Pi Components 00:03:00 OS Versions Available for Raspberry Pi 00:07:00 Arduino Vs Raspberry Pi Vs BeagleBone 00:07:00 Course Material Works With Any High Tech Board! 00:02:00 Running The Raspberry Pi Board What Do You Need To RunThe Raspberry Pi 00:06:00 Getting OS and Running Raspberry Pi 00:09:00 Setting Up Raspberry Pi for the First Time 00:04:00 Simulating Raspberry Pi 00:05:00 Raspberry Pi Hardware and Starter Kit That Can be Used With it Simple Starter Kit 00:07:00 Raspberry Pi Modeb B Unboxing 00:01:00 A tour inside Raspberry Pi Operating System A Tour Inside Raspberry Pi Operating System 00:04:00 Raspberry Pi Command Line 00:06:00 Updating Software Packages 00:06:00 Control Raspberry Pi Board From Another Device Control Raspberry Pi From Another Device Using SSH 00:04:00 Control Raspberry Pi From Another Device Using VNC Server 00:08:00 Assign A fix IP Address for Raspberry Pi Board 00:04:00 Connecting Raspberry Pi to another computer using Network Cable 00:04:00 Control General Purpose Input Output Programming GPIO With Python 00:04:00 Python and Your First Code 00:08:00 Installing the Control Library 00:03:00 Practical Examples Example 1 Blinking Led 00:07:00 Example Two Reading Input Values From A Switch 00:03:00 Example Three Running the Led Using LDR 00:03:00 Example Four PIR Motion Detector 00:04:00 Additional Components and Notes BCM Numbering Instead of Numbering by Order 00:02:00 Adafruit Protection Box and Connection Cable 00:02:00 The End 00:02:00 Obtain Your Certificate Order Your Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

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Step into the intricate world of artificial neural networks with a course that sheds light on one of the most dynamic branches of deep learning. Designed for those keen to explore the structure, function and logic behind intelligent systems, this course blends academic insight with a clear, structured approach to the evolving digital frontier. From perceptrons to multilayer networks, it offers a layered understanding of how machines mimic the human brain’s decision-making process — minus the caffeine and existential crises. Whether you're sharpening your knowledge or stepping into the field with curiosity, this course provides a sharp focus on the core principles that power technologies like image recognition, voice synthesis and predictive modelling. Delivered in an accessible format, it caters to learners who want depth without the fluff, logic without the waffle, and progress without the guesswork. Neural networks may be artificial — but your understanding of them won’t be. Learning Outcomes: Gain a solid understanding of artificial neural networks and their applications in deep learning. Learn how to install the necessary packages and preprocess data for neural network training. Discover how to encode data and build your own artificial neural network using Python. Understand the steps involved in making predictions using your neural network model. Learn how to deal with imbalanced data in your neural network training. The Project on Deep Learning - Artificial Neural Network course is designed to provide you with the skills and knowledge you need to build your own neural network and perform complex tasks using deep learning. You'll learn how to install the necessary packages, preprocess data, and encode data for neural network training. You'll also gain a deeper understanding of artificial neural networks and learn how to build your own model using Python. By the end of the course, you'll be able to make predictions using your neural network model and understand how to deal with imbalanced data in your training. Project on Deep Learning - Artificial Neural Network Course Curriculum Section 01: Introduction Section 02: ANN Installation Section 03: Data Preprocessing Section 04: Data Encoding Section 05: Steps to Build ANN Section 06: Predictions and Imbalance-Learn How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Data analysts who want to expand their skills in deep learning and artificial neural networks. Programmers who want to learn how to build their own neural network models for advanced tasks. Entrepreneurs who want to develop their own deep learning-based applications. Students who want to enhance their skills in deep learning and prepare for a career in the field. Anyone who wants to explore the world of artificial neural networks and deep learning projects. Career path Data Analyst: £24,000 - £45,000 Machine Learning Engineer: £28,000 - £65,000 Deep Learning Engineer: £30,000 - £75,000 Technical Lead: £40,000 - £90,000 Chief Technology Officer: £90,000 - £250,000 Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

Ever wondered how to speak confidently about buildings, materials, and construction terms—in Portuguese? This course is your blueprint to building language skills tailored to the world of structural design and architecture. From concrete columns to roofing terms, you'll learn how to talk structures with precision and clarity—all in Portuguese. Whether you're a construction enthusiast, a professional working with Portuguese-speaking clients, or simply keen to expand your vocabulary, this course is structured to help you build fluency without ever picking up a hammer. Expect engaging modules that introduce you to the foundations of structural language—from everyday construction phrases to technical expressions. It's not about laying bricks; it's about laying down words that matter. Delivered entirely online, this course offers you the flexibility to learn from wherever you are, while gaining knowledge that’s both specific and linguistically sharp. If structure speaks to you, let it speak Portuguese too. Learning Outcomes: Gain a solid understanding of artificial neural networks and their applications in deep learning. Learn how to install the necessary packages and preprocess data for neural network training. Discover how to encode data and build your own artificial neural network using Python. Understand the steps involved in making predictions using your neural network model. Learn how to deal with imbalanced data in your neural network training. The Project on Deep Learning - Artificial Neural Network course is designed to provide you with the skills and knowledge you need to build your own neural network and perform complex tasks using deep learning. You'll learn how to install the necessary packages, preprocess data, and encode data for neural network training. You'll also gain a deeper understanding of artificial neural networks and learn how to build your own model using Python. By the end of the course, you'll be able to make predictions using your neural network model and understand how to deal with imbalanced data in your training. Build Structures in Portuguese Course Curriculum Introduction Section 01: Chapter 1 Section 02: Chapter 2 Section 03: Chapter 3 Section 04: Chapter 4 Section 05: Chapter 5 Section 06: Chapter 6 Section 07: Chapter 7 Section 08: Chapter 8 Section 09: Chapter 9 Section 10: Chapter 10 How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Data analysts who want to expand their skills in deep learning and artificial neural networks. Programmers who want to learn how to build their own neural network models for advanced tasks. Entrepreneurs who want to develop their own deep learning-based applications. Students who want to enhance their skills in deep learning and prepare for a career in the field. Anyone who wants to explore the world of artificial neural networks and deep learning projects. Career path Data Analyst: £24,000 - £45,000 Machine Learning Engineer: £28,000 - £65,000 Deep Learning Engineer: £30,000 - £75,000 Technical Lead: £40,000 - £90,000 Chief Technology Officer: £90,000 - £250,000 Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

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