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This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.

The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.

Duration 3 Days 18 CPD hours This course is intended for This course is designed for existing Python programmers who have at least one year of Python experience and who want to expand their programming proficiency in Python 3. Overview In this course, you will expand your Python proficiencies. You will: Select an object-oriented programming approach for Python applications. Create object-oriented Python applications. Create a desktop application. Create data-driven applications. Create and secure web service-connected applications. Program Python for data science. Implement unit testing and exception handling. Package an application for distribution. Python© continues to be a popular programming language, perhaps owing to its easy learning curve, small code footprint, and versatility for business, web, and scientific uses. Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you'll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, creating web service-connected apps, performing data science tasks, unit testing, and creating and installing packages and executable applications. Lesson 1: Selecting an Object-Oriented Programming Approach for Python Applications Topic A: Implement Object-Oriented Design Topic B: Leverage the Benefits of Object-Oriented Programming Lesson 2: Creating Object-Oriented Python Applications Topic A: Create a Class Topic B: Use Built-in Methods Topic C: Implement the Factory Design Pattern Lesson 3: Creating a Desktop Application Topic A: Design a Graphical User Interface (GUI) Topic B: Create Interactive Applications Lesson 4: Creating Data-Driven Applications Topic A: Connect to Data Topic B: Store, Update, and Delete Data in a Database Lesson 5: Creating and Securing a Web Service-Connected App Topic A: Select a Network Application Protocol Topic B: Create a RESTful Web Service Topic C: Create a Web Service Client Topic D: Secure Connected Applications Lesson 6: Programming Python for Data Science Topic A: Clean Data with Python Topic B: Visualize Data with Python Topic C: Perform Linear Regression with Machine Learning Lesson 7: Implementing Unit Testing and Exception Handling Topic A: Handle Exceptions Topic B: Write a Unit Test Topic C: Execute a Unit Test Lesson 8: Packaging an Application for Distribution Topic A: Create and Install a Package Topic B: Generate Alternative Distribution Files

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Duration 2 Days 12 CPD hours This course is intended for This in an introductory-level course geared for QA, Test team members and others who want to use the Python testing framework PyTest to implement code testing strategies. Attendees should have prior basic Python scripting experience. Students should have some familiarity with tools to be used in this course: PyCharm, Jupyter Notebook and basic GIT. Overview Working within in a hands-on learning environment students will learn to: Become proficient with pytest from day one by solving real-world testing problems Use pytest to write tests more efficiently Scale from simple to complex and functional testing Write and run simple and complex tests Organize tests in fles and directories Find out how to be more productive on the command line Markers and how to skip, xfail and parametrize tests Explore fxtures and techniques to use them effectively, such as tmpdir, pytestconfg, and monkeypatch Convert unittest suites to pytest using little-known techniques The pytest framework is simple to use but powerful enough to cover complex testing integration scenarios. PyTest is considered by many to be the true Pythonic approach to testing in Python. Geared for QA, Test team members and others who want to use the Python testing framework PyTest to implement code testing strategies, Test Automation with Python is a hands-on, two day Python testing course that provides students with the skills required to get started with PyTest right away. Participnats will learn how to get the most out of it in their daily workflow, exploring powerful mechanisms and plugins to facilitate many common testing tasks. Students will also learn how to use pytest in existing unittestbased test suites and will learn some tricks to make the jump to a pytest-style test suite quickly and easily. Python Refresher Python Overview Python Basics Python Lab Introducing PyTest Why Spend time writing test UnitTest Module Why PyTest? Introductory Lab Writing and Running Test Installing PyTest Writing and Running Tests Organizing files and packages Command Line options Configure pytest.ini Install and Config Lab Markers and Parameters Mark Basics Built-in marks Parameterization Markers and Parameters Lab Fixtures Introduction to Fixtures Sharing fixtures with conftest.py files Scopes Autouse Parameterizing fixtures Using marks from fixtures Built-in fixtures Best Practices Fixtures Lab Fixtures Lab 2 Plugins Finding and installing plugins Overview of plugins Plugin Lab From UnitTest to PyTest Use PyTest as a Test Runner Convert asserts with unitest2pytest Handling setup/teardown Managing test hierarchies Refactoring test utilities Migration strategies Additional course details: Nexus Humans Test Automation with Python (TTPS4832) 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 Test Automation with Python (TTPS4832) 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.

Register on the Python 3 Programming Course for Beginners today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a certificate as proof of your course completion. The Python 3 Programming Course for Beginners course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablets, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Python 3 Programming Course for Beginners course Receive a digital certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who Is This Course For: The course is ideal for those who already work in this sector or are aspiring professionals. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Python 3 Programming Course for Beginners course, all you need is a passion for learning, A good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Section 01 A Installing Python 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 Section 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 Section 03 Data Types 00:24:00 Number Types 00:28:00 More Number Types 00:14:00 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 Section 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 Section 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 Data Hiding 00:07:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Duration 2 Days 12 CPD hours This course is intended for This course is designed for people who want to learn the Python programming language in preparation for using Python to develop software for a wide range of applications, such as data science, machine learning, artificial intelligence, and web development. Overview In this course, you will develop simple command-line programs in Python. You will: Set up Python and develop a simple application. Declare and perform operations on simple data types, including strings, numbers, and dates. Declare and perform operations on data structures, including lists, ranges, tuples, dictionaries, and sets. Write conditional statements and loops. Define and use functions, classes, and modules. Manage files and directories through code. Deal with exceptions. Though Python has been in use for nearly thirty years, it has become one of the most popular languages for software development, particularly within the fields of data science, machine learning, artificial intelligence, and web development?all areas in which Python is widely used. Whether you're relatively new to programming, or have experience in other programming languages, this course will provide you with a comprehensive first exposure to the Python programming language that can provide you with a quick start in Python, or as the foundation for further learning. You will learn elements of the Python 3 language and development strategies by creating a complete program that performs a wide range of operations on a variety of data types, structures, and objects, implements program logic through conditional statements and loops, structures code for reusability through functions, classes, and modules, reads and writes files, and handles error conditions. Lesson 1: Setting Up Python and Developing a Simple Application Topic A: Set Up the Development Environment Topic B: Write Python Statements Topic C: Create a Python Application Topic D: Prevent Errors Lesson 2: Processing Simple Data Types Topic A: Process Strings and Integers Topic B: Process Decimals, Floats, and Mixed Number Types Lesson 3: Processing Data Structures Topic A: Process Ordered Data Structures Topic B: Process Unordered Data Structures Lesson 4: Writing Conditional Statements and Loops in Python Topic A: Write a Conditional Statement Topic B: Write a Loop Lesson 5: Structuring Code for Reuse Topic A: Define and Call a Function Topic B: Define and Instantiate a Class Topic C: Import and Use a Module Lesson 6: Writing Code to Process Files and Directories Topic A: Write to a Text File Topic B: Read from a Text File Topic C: Get the Contents of a Directory Topic D: Manage Files and Directories Lesson 7: Dealing with Exceptions Topic A: Handle Exceptions Topic B: Raise Exceptions

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Overview This comprehensive course on Machine Learning for Predictive Maps in Python and Leaflet will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Machine Learning for Predictive Maps in Python and Leaflet comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Machine Learning for Predictive Maps in Python and Leaflet. It is available to all students, of all academic backgrounds. Requirements Our Machine Learning for Predictive Maps in Python and Leaflet is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 9 sections • 33 lectures • 05:59:00 total length •Introduction: 00:10:00 •Python Installation: 00:04:00 •Creating a Python Virtual Environment: 00:07:00 •Installing Django: 00:09:00 •Installing Visual Studio Code IDE: 00:06:00 •Installing PostgreSQL Database Server Part 1: 00:03:00 •Installing PostgreSQL Database Server Part 2: 00:09:00 •Adding the settings.py Code: 00:07:00 •Creating a Django Model: 00:10:00 •Adding the admin.py Code: 00:21:00 •Creating Template Files: 00:10:00 •Creating Django Views: 00:10:00 •Creating URL Patterns for the REST API: 00:09:00 •Adding the index.html code: 00:04:00 •Adding the layout.html code: 00:19:00 •Creating our First Map: 00:10:00 •Adding Markers: 00:16:00 •Installing Jupyter Notebook: 00:07:00 •Data Pre-processing: 00:31:00 •Model Selection: 00:20:00 •Model Evaluation and Building a Prediction Dataset: 00:11:00 •Creating a Django Model: 00:04:00 •Embedding the Machine Learning Pipeline in the Application: 00:42:00 •Creating a URL Endpoint for our Prediction Dataset: 00:06:00 •Creating Multiple Basemaps: 00:09:00 •Creating the Marker Layer Group: 00:10:00 •Creating the Point Layer Group: 00:12:00 •Creating the Predicted Point Layer Group: 00:07:00 •Creating the Predicted High Risk Point Layer Group: 00:12:00 •Creating the Legend: 00:09:00 •Creating the Prediction Score Legend: 00:15:00 •Resource: 00:00:00 •Assignment - Machine Learning for Predictive Maps in Python and Leaflet: 00:00:00

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints