Course Overview: The demand for web developers is snowballing. According to the Bureau of Labor Statistics, employment of web developers is projected to grow 22 percent from 2020 to 2030, much faster than the average for all occupations. Are you eager to craft powerful web applications that captivate users and drive business growth? Look no further! This Building Web Applications with Django and PostgreSQL course will equip you with the essential skills to master Django, a versatile web framework, and PostgreSQL, a robust database management system. "Numbers don't lie" - the soaring popularity of Python-based web frameworks like Django is a testament to their relevance and demand in the industry. As you delve into this course, you'll be pleased to know that recent surveys have highlighted Django's dominance in the web development market. Gain a competitive edge as you learn from seasoned professionals, and dive into the world of web development with confidence. Take advantage of this golden opportunity! Enrol now to embark on an exciting journey of learning and transformation. Key Features of the Course: Unleash your potential with a CPD certificate upon course completion, solidifying your expertise in web application development. Enjoy an uninterrupted learning experience with our 24/7 Learning Assistance, ensuring that you have the support you need whenever you need it. Embrace an interactive and engaging learning journey with captivating materials, real-world projects, and quizzes that inspire you. Who is This Course For? If you aspire to become a web developer, programming virtuoso, or IT enthusiast, this Building Web Applications with Django and PostgreSQL course is tailor-made. Whether you're just starting or an experienced developer looking to enhance your skill set, our course caters to learners of all levels, fostering an inclusive and supportive learning environment. What You Will Learn: Seamlessly set up Python and PyCharm IDE on diverse platforms. Unravel the architectural marvels of Django, and build your first Django app. Embrace the power of PostgreSQL for web applications and configure it flawlessly. Harness the potential of Django's built-in ORM for streamlined database interactions. Implement foolproof user authentication and authorisation mechanisms. Craft dynamic web pages using HTML, templates, and Bootstrap. Design visually stunning product card items to elevate your web applications. Why Enrol in This Course: Your success story begins with us, where you'll gain in-demand skills to create sophisticated web applications that thrive in today's digital landscape. Experience the best as you join our top-reviewed course, regularly updated to stay ahead of industry trends. With hands-on experience and industry-relevant skills, you'll elevate your employability and unlock doors to exciting opportunities. Requirements: A basic understanding of Python A computer with Python installed Career Path: Upon completion, embark on a rewarding career path in web development with diverse options, including: Web Developer - £35,000 to £55,000 per annum Full-Stack Developer - £40,000 to £65,000 per annum Django Developer - £45,000 to £70,000 per annum Python Developer - £40,000 to £65,000 per annum Frontend Developer - £30,000 to £50,000 per annum Backend Developer - £40,000 to £65,000 per annum Database Administrator - £35,000 to £55,000 per annum Certification: The crowning achievement of your learning endeavour awaits as you earn your well-deserved CPD certificate. Recognised and esteemed across industries, this certificate will validate your proficiency in building web applications with Django and PostgreSQL. Take the first step towards excellence and enrol in our course today! Course Curriculum 10 sections • 53 lectures • 04:52:00 total length •Introduction: 00:04:00 •Getting started on Windows, macOS, and Linux: 00:01:00 •How to ask great questions: 00:02:00 •FAQs: 00:01:00 •Installing Python: 00:07:00 •Installing PyCharm IDE for development: 00:12:00 •Creating the first Python project: 00:09:00 •Write and Run Hello World program: 00:05:00 •Command line basics: 00:06:00 •Installation and access pip command: 00:10:00 •Introduction to web framework and Django: 00:04:00 •Installing Django and setting up a project: 00:07:00 •Understanding Django architecture and structured: 00:08:00 •Creating a simple Django app: 00:06:00 •Introduction to PostgreSQL and its advantages for web applications: 00:04:00 •Installing PostgreSQL and setting up a database: 00:13:00 •pgAdmin Secure login and create database: 00:10:00 •Connection Django to PostgreSQL: 00:09:00 •Creating a Basic HTTP view method request: 00:04:00 •URL pattern on app: 00:05:00 •Include apps to project urls: 00:05:00 •Installing the psycopg2 module for DB connection: 00:05:00 •Defining models and creating database tables: 00:08:00 •Using Djangos built-in ORM to interact with the database: 00:06:00 •Migration to create DB tables: 00:06:00 •Assessment Test: 00:01:00 •Solution for an Assessment Test: 00:04:00 •Creating Superuser as Admin: 00:07:00 •Authentication and Authorization: 00:03:00 •User access permissions: 00:04:00 •Registering user models to the admin Dashboard: 00:04:00 •Creating data using CRUD functionality: 00:06:00 •View data on the PostgreSQL database table: 00:04:00 •Understanding the MVC: 00:05:00 •Creating views and handling HTTP requests and rendering templates: 00:10:00 •Creating a template with HTML page: 00:10:00 •Building dynamic form and handling: 00:10:00 •Creating Bootstrap template: 00:06:00 •Applying Bootstrap to Django form: 00:04:00 •Creating Stunning Product Card items: 00:09:00 •Creating model: 00:04:00 •Creating forms: 00:03:00 •Creating views: 00:07:00 •Applying ORM - migrations: 00:03:00 •Creating my_form template: 00:05:00 •Adding urls: 00:03:00 •Creating success_view template: 00:04:00 •Project urls update and adding Installed apps: 00:02:00 •Run project and submit data to database: 00:06:00 •Creating report_view method: 00:03:00 •Creating report_template: 00:03:00 •Update urls pattern: 00:02:00 •Run project and test reports: 00:03:00
This course is about developing core skills that will stay with you for a lifetime. It is designed such that you can watch the material and follow along step-by-step. It focuses on the implementation of YOLOv4 to get you up and running. You'll be an object detecting ninja in no time and be able to graduate to more advanced content.
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Definitive Salt training course description Salt is a remote execution framework and configuration management system. This course covers Salt from the basics. After a quick first taste the course moves onto execution modules, salt states, minion and master data, jinja, Salt extensions and then topology and configuration options. Hands on sessions are used to reinforce the theory rather than teach specific manufacturer equipment. What will you learn Install and use Salt. Describe the architecture of Salt. Manage configurations with Salt. Extend Salt. Definitive Salt training course details Who will benefit: Anyone working with Salt. Prerequisites: Linux fundamentals. Duration 2 days Definitive Salt training course contents Introduction What is Salt? High- level architecture, Some quick examples, system management, configuration management, A brief history, Topology options, Extending Salt. Quick start: First taste of Salt Single-master setup, from packages, bootstrap scripts, Starting up, Basic commands, salt: the main workhorse, salt-key: key management, salt-call: execution on the minion, salt-run: co-ordination of jobs on the master, summary of commands, Key management, viewing keys, accepting keys, rejecting keys, key files, Minion targeting, minion ID, list (-L), glob, regular expressions (-E), grains (-G), compound (-C), targeting summary, Additional remote execution details, Conclusion. Execution modules: The functional foundation sys: information and documentation about modules, sys.doc basic documentation, sys.list_modules, sys.list_functions: simple listings, cmd: execute via shell, cmd.run: run any command, pkg: manage packages, virtual modules, pkg.lists_pkgs: list all installed packages, pkg.available version: see what version will be installed, pkg.install: install packages, user: manage users, user.add: add users, user.list_users, user info: get user info, saltutil: access various Salt utilities, Summary. Configuration management: Salt states Salt files overview, SLS example: adding a user, working with the multi-layered state system, Highstate and the top file, the top file, State ordering, require: depend on another state, watch: run based on other changes, odds and ends, Summary. Minion data / master data Grains are minion data, performing basic grain operations, setting grains, targeting with grains in the top file, Pillars are data from the master, querying pillar data, querying other sources with external pillars, Renderers give data options. Extending Salt: part I Introduction to Jinja, Jinja basics, Templating with Jinja, filtering by grains, Custom execution module, Custom state modules, Custom grains, External pillars, Summary. More on the matter Runners, manage minions, manage jobs, The orchestrate runner, The event system, The reactor system, Summary. Extending Salt: part II Python client API, reading configuration data on a master and minion, using the master client (localclient) API, Using the caller client API, Custom runners, writing a custom runner, using the runnerclient API, Summary. Topology and configuration options Master configuration, directories and files, logging, access control, files server options, Topology variations, masterless minions, peer systems, syndication masters, multiple masters. Brief introduction to salt-cloud Overview, Setup AWS and salt-cloud, installing salt-cloud, cloud providers, cloud profiles, cloud maps, Introspection via salt cloud, Creating infrastructure, More information. Using vagrant to run Salt examples YAML.
Overview In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning & Data Science Bootcamp 2023 course can help you out. This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing. Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol today! 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? Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst Course Curriculum 18 sections • 98 lectures • 23:48:00 total length •Welcome & Course Overview6: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:17:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method.: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00