366 Courses

The "YAML Fundamentals" course helps beginners with the required skills to develop YAML documents. It will also help you gain skills to develop a properly structured YAML document in both block style and flow style. The "flow style" is also known as JSON style or compact style. If you are looking forward to adding YAML to your skillset, then this course is what you need. In today's market, every IT professional is expected to know YAML.

Overview Dive into the dynamic world of computer science with our comprehensive 'Computer Science with Python Course'. Python, a versatile and widely used programming language, serves as the foundation for this course, offering learners a gateway into the intricate realm of computing. From installing Python and understanding its documentation to mastering advanced concepts like closures, classes, and data hiding, this course is meticulously designed to cater to beginners and those looking to deepen their knowledge. With a curriculum that's rich and varied, you'll be equipped with the skills to tackle real-world challenges, making you a sought-after asset in the ever-evolving tech industry. The course curriculum is structured to ensure a smooth learning curve. Starting with foundational topics such as command line usage, variables, and simple Python syntax, learners will gradually progress to more advanced subjects.  In the digital age, proficiency in a programming language like Python is invaluable. Whether you're aiming to kickstart a career in tech, enhance your current skill set, or simply satiate your curiosity, this course promises a transformative learning experience. With a blend of theoretical knowledge and its practical application, you'll be poised to make significant strides in the world of computer science. Learning Outcomes of Computer Science with Python Course: Understand Python's foundational concepts, including its installation, documentation, and basic syntax. Gain proficiency in working with various Python data types such as strings, lists, dictionaries, and tuples. Develop the ability to create and manipulate functions, including lambda functions, generators, and closures. Acquire skills in object-oriented programming with a focus on classes, inheritance, and data hiding. Implement advanced programming constructs and handle exceptions efficiently.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2020/01/Computer-Science-with-Python-Course-Introduction-Video-1.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Computer Science with Python Course? Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Computer Science with Python Course you will be able to take the MCQ test that will assess your knowledge. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this Computer Science with Python course for? Beginners eager to delve into the realm of computer science and programming. Individuals looking to add Python programming to their skill set. Tech enthusiasts keen on understanding advanced Python concepts. Students pursuing computer science and needing a comprehensive Python guide. Professionals in tech roles aiming to enhance their coding capabilities. Prerequisites This Computer Science with Python Course was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. Career Path Python Developer: £45,000 - £70,000 Annually Data Scientist: £50,000 - £80,000 Annually Software Engineer: £40,000 - £75,000 Annually Backend Developer: £45,000 - £72,000 Annually Systems Analyst: £35,000 - £60,000 Annually DevOps Engineer: £50,000 - £85,000 Annually Course Curriculum Module 01 A Installing Python FREE 00:17:00 Documentation 00:30:00 Command Line 00:17:00 Variables 00:29:00 Simple Python Syntax 00:15:00 Keywords 00:18:00 Import Module 00:17:00 Module 02 Additional Topics 00:23:00 If Elif Else 00:31:00 Iterable 00:10:00 For 00:11:00 Loops 00:20:00 Execute 00:05:00 Exceptions 00:18:00 Module 03 Data Types 00:24:00 Number Types 00:28:00 More Number Types 00:13:00 Strings 00:20:00 More Strings 00:11:00 Files 00:08:00 Lists 00:15:00 Dictionaries 00:04:00 Tuples 00:07:00 Sets 00:09:00 Module 04 Comprehensions 00:10:00 Definitions 00:02:00 Functions 00:06:00 Default Arguments 00:06:00 Doc Strings 00:06:00 Variadic Functions 00:07:00 Factorial 00:07:00 Module 05 Function Objects 00:07:00 Lambda 00:11:00 Generators 00:06:00 Closures 00:10:00 Classes 00:09:00 Object Initialization 00:05:00 Class Static Members 00:07:00 Classic Inheritance 00:10:00 Data Hiding 00:07:00 Mock Exam Mock Exam - Python Developer 00:20:00 Final Exam Final Exam - Python Developer 00:20:00

Learn Python OOP language used diversely in applications like data science, game/web development, machine learning, and AI. This course provides all you need to master OOPs like classes, objects, data abstraction, methods, overloading, and inheritance. The course primarily aims to help you tackle complex programming and use OOP paradigms efficiently.

Work with tables, partition, indexes, encryption, and database administration in the AWS Cloud with AWS DynamoDB

This course bundle is ideal for anyone looking to establish their Cisco networking career. It consists of one Cisco Certified Network Associate (CCNA) certification, one Cisco Certified DevNet Associate (CCDA) certification, and four Cisco Certified Networking Professional (CCNP) certifications. Although there are no formal prerequisites to enrol on the CCNA, CCDA, or CCNP certification courses and sit the exams, learners should have a good foundation knowledge in networking. The newly retooled CCNA covers a breadth of topics, including: Network fundamentals Network access IP connectivity IP services Security fundamentals Automation and programmability Achieving CCNA certification is the first step in preparing for a career in networking technologies. To earn your CCNA certification, you only need to pass one exam – which covers a broad range of fundamentals for IT careers, based on the latest networking technologies. The Cisco Certified DevNet Associate certification validates your skills and knowledge in understanding and using APIs, Cisco platforms and development, application development and security, and infrastructure and automation. Ideally, DevNet Associates candidates also have one or more years of experience with software development including Python programming. The CCNP is the next level from the CCNA and CCDA. As with the CCNA, there are no formal prerequisites to enrol on the CCNP certification course and sit for the exams. Learners taking a CCNP course generally have an in-depth knowledge of networking, as well as a good understanding of Cisco technologies. The typical certification path for most learners would begin with either the CCDA or CCNA, then progress onto the CCNP. Learners need to pass two exams (one core exam and one concentration exam) in order to gain one CCNP certification. This course bundle includes the core exam and three concentration exams. Once a learner has passed the core exam, they can choose to specialise in one or all three of the CCNP concentration areas listed in this course. The core exam’s focus is based around implementing and operating Cisco enterprise network core technologies.

This course offers an immersive experience in data analysis, guiding you from initial setup with Python and Pandas, through series and DataFrame manipulation, to advanced data visualization techniques. Perfect for enhancing your data handling and analysis skills.

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

Description: This diploma in C++ and Python programming course is a great way to get started in programming. It covers the study of the C++ and Python group of languages used to build most of the world's object oriented systems. The course is for interested students with a good level of computer literacy who wish to acquire programming skills. It is also ideal for those who wish to move to a developer role or areas such as software engineering. This is a great course to develop your coding skills. It teaches key features of imperative programming using C and is an ideal preliminary to the Object-Oriented Programming using Python. Join the course now! Entry Requirement This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Assessment:  At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. Before sitting for your final exam you will have the opportunity to test your proficiency with a mock exam. Certification: After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Python 3 Beginners Module 01 Introduction FREE 00:29:00 Starter Examples 00:33:00 Learning C Concepts 00:13:00 Module 02 Data Types and Inference 00:20:00 Sizeof and IEEE 754 00:33:00 Constants L and R Values 00:11:00 Operators and Precedence 00:25:00 Literals 00:26:00 Module 03 Classes and Structs FREE 00:22:00 Enums 00:14:00 Unions 00:16:00 Introduction to Pointers 00:11:00 Pointers and Array Indexing 00:12:00 Using Const with Pointers 00:09:00 Pointers to String Literals 00:12:00 References 00:14:00 Smart Pointers 00:22:00 Arrays 00:15:00 Standard Library Strings 00:13:00 More Standard Library Strings 00:18:00 Functions 00:06:00 More Functions 00:16:00 Function Pointers 00:15:00 Control Statements 00:18:00 Python 3 Intermediate Module 04 Installing Python FREE 00:17:00 Documentation 00:30:00 Command Line 00:17:00 Variables 00:29:00 Simple Python Syntax 00:15:00 Keywords 00:18:00 Import Module 00:17:00 Additional Topics 00:23:00 Module 05 If Elif Else 00:31:00 Iterable 00:10:00 For 00:11:00 Loops 00:20:00 Execute 00:05:00 Exceptions 00:18:00 Data Types 00:24:00 Module 06 Number Types 00:28:00 More Number Types 00:13:00 Strings 00:20:00 More Strings 00:11:00 Files 00:08:00 Lists 00:15:00 Dictionaries 00:04:00 Tuples 00:07:00 Sets 00:09:00 Module 07 Comprehensions 00:10:00 Definitions 00:02:00 Functions 00:06:00 Default Arguments 00:06:00 Doc Strings 00:06:00 Variadic Functions 00:07:00 Factorial 00:07:00 Function Objects 00:07:00 Module 08 Lambda 00:11:00 Generators 00:06:00 Closures 00:10:00 Classes 00:09:00 Object Initialization 00:05:00 Class Static Members 00:07:00 Classic Inheritance 00:10:00 Data Hiding 00:07:00 Python 3 Advanced Iterators and Generators FREE 00:16:00 Regular Expressions 00:19:00 Introspection and Lambda Functions 00:27:00 Metaclasses and Decorators 00:24:00 Modules and Packages 00:25:00 Working with APIs 00:15:00 Metaprogramming Primer 00:19:00 Decorators and Monkey Patching 00:21:00 XML and JSON Structure 00:10:00 Generating XML and JSON 00:17:00 Parsing XML and JSON 00:19:00 Implementing Algorithms 00:19:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Python: Arduino SMS Sending Motion Detector Course Overview Are you looking to begin your Arduino SMS Sending Motion Detector career or want to develop more advanced skills in Arduino SMS Sending Motion Detector? Then this Python: Arduino SMS sending motion detector course will set you up with a solid foundation to become a confident Python progammer or electronic and electrical engineer and help you to develop your expertise in Arduino SMS Sending Motion Detector. This Python: Arduino SMS sending motion detector course is accredited by the CPD UK & IPHM. CPD is globally recognised by employers, professional organisations and academic intuitions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. CPD certified certificates are accepted by thousands of professional bodies and government regulators here in the UK and around the world. Whether you are self-taught and you want to fill in the gaps for better efficiency and productivity, this Python: Arduino SMS sending motion detector course will set you up with a solid foundation to become a confident Python progammer or electronic and electrical engineer and develop more advanced skills. Gain the essential skills and knowledge you need to propel your career forward as a Python progammer or electronic and electrical engineer. The Python: Arduino SMS sending motion detector course will set you up with the appropriate skills and experience needed for the job and is ideal for both beginners and those currently working as a Python progammer or electronic and electrical engineer. This comprehensive Python: Arduino SMS sending motion detector course is the perfect way to kickstart your career in the field of Arduino SMS Sending Motion Detector. This Python: Arduino SMS sending motion detector course will give you a competitive advantage in your career, making you stand out from all other applicants and employees. If you're interested in working as a Python progammer or electronic and electrical engineer or want to learn more skills on Arduino SMS Sending Motion Detector but unsure of where to start, then this Python: Arduino SMS sending motion detector course will set you up with a solid foundation to become a confident Python progammer or electronic and electrical engineer and develop more advanced skills. As one of the leading course providers and most renowned e-learning specialists online, we're dedicated to giving you the best educational experience possible. This python: Arduino SMS sending motion detector course is crafted by industry expert, to enable you to learn quickly and efficiently, and at your own pace and convenience. Who should take this course? This comprehensive Python: Arduino SMS sending motion detector course is suitable for anyone looking to improve their job prospects or aspiring to accelerate their career in this sector and want to gain in-depth knowledge of Arduino SMS Sending Motion Detector. Entry Requirements There are no academic entry requirements for this Python: Arduino SMS sending motion detector course, and it is open to students of all academic backgrounds. As long as you are aged seventeen or over and have a basic grasp of English, numeracy and ICT, you will be eligible to enrol. Career path This python: Arduino SMS sending motion detector course opens a brand new door for you to enter the relevant job market and also provides you with the chance to accumulate in-depth knowledge at the side of needed skills to become flourishing in no time. You will also be able to add your new skills to your CV, enhance your career and become more competitive in your chosen industry. Course Curriculum Introduction Introduction Who We Are Hardware and Software Requirements Hardware and Software Requirements Circuit Design Circuit Design Arduino Coding Arduino Coding SMS API Service Sign up for SMS API Service Activate SMS API Service Via Trial Request Get Your Online Phone Number Coding Download and Install Pycharm Python Editor Download and Install Required Libraries Using Pycharm Get the Send SMS Python API and Test it Practical Motion Detector SMS Sending Process Python Programming Download and Install Required Python Libraries Download and Install Software Section Download and Install Arduino Pro IDE Recognised Accreditation CPD Certification Service 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. CPD certificates are accepted by thousands of professional bodies and government regulators here in the UK and around the world. Many organisations look for employees with CPD requirements, which means, that by doing this course, you would be a potential candidate in your respective field. Certificate of Achievement Certificate of Achievement from Lead Academy After successfully passing the MCQ exam you will be eligible to order your certificate of achievement as proof of your new skill. The certificate of achievement is an official credential that confirms that you successfully finished a course with Lead Academy. Certificate can be obtained in PDF version at a cost of £12, and there is an additional fee to obtain a printed copy certificate which is £35.   FAQs Is CPD a recognised qualification in the UK? CPD is globally recognised by employers, professional organisations and academic intuitions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. CPD-certified certificates are accepted by thousands of professional bodies and government regulators here in the UK and around the world. Are QLS courses recognised? Although QLS courses are not subject to Ofqual regulation, they must adhere to an extremely high level that is set and regulated independently across the globe. A course that has been approved by the Quality Licence Scheme simply indicates that it has been examined and evaluated in terms of quality and fulfils the predetermined quality standards. When will I receive my certificate? For CPD accredited PDF certificate it will take 24 hours, however for the hardcopy CPD certificate takes 5-7 business days and for the Quality License Scheme certificate it will take 7-9 business days. Can I pay by invoice? Yes, you can pay via Invoice or Purchase Order, please contact us at info@lead-academy.org for invoice payment. Can I pay via instalment? Yes, you can pay via instalments at checkout. How to take online classes from home? Our platform provides easy and comfortable access for all learners; all you need is a stable internet connection and a device such as a laptop, desktop PC, tablet, or mobile phone. The learning site is accessible 24/7, allowing you to take the course at your own pace while relaxing in the privacy of your home or workplace. Does age matter in online learning? No, there is no age limit for online learning. Online learning is accessible to people of all ages and requires no age-specific criteria to pursue a course of interest. As opposed to degrees pursued at university, online courses are designed to break the barriers of age limitation that aim to limit the learner's ability to learn new things, diversify their skills, and expand their horizons. When I will get the login details for my course? After successfully purchasing the course, you will receive an email within 24 hours with the login details of your course. Kindly check your inbox, junk or spam folder, or you can contact our client success team via info@lead-academy.org

Python training course description This Python course focusses on teaching Python for use in network automation and network DevOps. We focus on getting delegates up and running with Python and network automation as quickly as possible rather than making them great programmers. In other words we concentrate on enabling delegates to use network automation libraries such as netmiko, NAPALM and Nornir, and APIs such as NETCONF and RESTCONF rather than enabling delegates to produce object oriented programs. Hands on sessions use Cisco and Juniper devices. What will you learn Run Python programs. Read Python programs. Write Python programs. Debug Python programs. Automate network tasks with Python programs. Configure network devices with Python. Collect data from network devices with Python. Python training course details Who will benefit: Network engineers. Prerequisites: TCP/IP Foundation Duration 5 days Python training course contents What is Python? Programming languages, Why Python? Python in interactive mode, Python scripts, ipython, Python version 2 versus version 3. A simple Python script. Comments. Hands on Installing Python, Hello world. A network example On box vs off box Python. telnet, ssh, NETCONF, HTTP, APIs, manufacturers and API support, analysis of a simple telnetlib program. Hands on Using Python to retrieve the configuration from a network device. Using wireshark to analyse the actions. Python basics I/O, operators, variables and assignment, types, indentation, loops and conditionals. Hands on Modifying the telnet program, changing configurations on a network devices. Functions, classes and methods What are functions, calling functions, builtin functions, useful builtin functions, file handling, classes, objects, creating instances. Hands on Storing configurations in files, configuring devices from files, using an inventory file to work on multiple devices. Libraries and modules Modules, files and packages, import, from-import, Python standard library, other packages, pip install, executing other programs. Managing python libraries. Hands on Using pip, installing and using ipaddress, subprocess to access netsnmp. For the more advanced, using the sockets library. Paramiko and netmiko SSH, enabling SSH on devices, keys. Paramiko versus netmiko, example scripts. pexpect. Hands on Configuring VLANs from Python. pySNMP Gathering facts using previous methods, SNMP review, pySNMP GET, pySNMP and SNMPv3. easySNMP library. Hands on Walking a MIB from Python. NETCONF What is NETCONF? Enabling NETCONF on devices, A first ncclient script, device handlers, get_config, edit_config, copy_config, delete_config, commit, validate, pyEZ, utils_config, utils.sw. Hands on Configuration using ncclient and PyEZ. This session is expanded for those interesting in JunOS automation. Manipulating configuration files Builtin functions, string handling. Unicode. Sequences, strings, lists, tuples. Dictionaries. TextFSM. Regular expressions. JSON, YAML, XML, YANG, Jinja2, templates. Hands on Jinja2 templating with Python to configure network devices. NAPALM Getters, configuration operations, supported devices, NAPALM transport, Config-replace, Config-merge, Compare config, Atomic changes, rollback. Example NAPLAM scripts. Hands on Using NAPALM to gather facts, Using NAPALM for configuration management REST and RESTCONF What is REST, HTTP methods, GET, POST, cURL, Postman, Python requests library. RESTCONF, a RESTCONF example. Hands on Modifying a configuration using RESTCONF. Scapy What is scapy, Scapy in interactive mode, Scapy as a module. Hands on Packet crafting from Python. Warning Errors and exceptions, Exception handling, try, except. Memory management. Garbage collection. Context management, With. Hands on Improving Python code. Nornir What is Nornir? A network automation framework, inventories, connection management and parallelization. Nornir architecture and other libraires. Hands on Setting up nornir, nornir fact gathering, nornir tasks. Optional Writing your own functions, Writing your own classes. pyntc. Hands on Writing reusable code.