938 Python courses

Unleash Your Coding Potential with the Ultimate Software Engineering Bundle! According to a recent study by Tech Nation, the UK's tech industry is booming, with an estimated 4.8 million tech workers contributing over £185 billion to the economy. It also shows a growing demand for skilled software engineers, with a projected job growth of 22% over the next decade and an average salary of £58,000 per year. Are you ready to embark on an incredible journey through the world of programming and software engineering? Our Software Engineering, Python, C++ , Javascript, CSS, HTML Coding bundle is meticulously curated to equip you with the essential skills and knowledge to thrive in this dynamic field. We've assembled a collection of 20 skill-boosting courses in this Software Engineering bundle that will teach you the fundamentals of programming, web development, machine learning, and more. You'll also gain valuable insights into cybersecurity, SaaS development, and game development, empowering you to pursue a diverse range of career paths. Don't miss out on this opportunity to enhance your coding prowess and ignite your software engineering journey. Enrol now and shape your future today! This Software Engineering, Python, C++ , Javascript, CSS, HTML Coding Bundle Contains 20 of Our Premium Courses for One Discounted Price: Course 01: Coding with Scratch Course 02: C# Programming - Beginner to Advanced Course 03: Machine Learning with Python Course Course 04: Basics of WordPress: Create Unlimited Websites Course 05: Modern PHP Web Development w/ MySQL, GitHub & Heroku Course 06: Node JS: API Development with Swagger Course 07: Refactor Javascript Course 08: Python Programming Bible | Networking, GUI, Email, XML, CGI Course 09: Web Application Penetration Testing Course Course 10: Penetration Testing with OWASP ZAP: Mastery course Course 11: How To Startup Your Own SaaS (Software As a Service) Company (SaaS Evolution) Course 12: Three.js & WebGL 3D Programming Crash Course Course 13: HTML Web Development Crash Course Course 14: CSS Web Development Crash Course Course 15: Flutter & Dart Development for Building iOS and Android Apps Course 16: Masterclass Bootstrap 5 Course - Responsive Web Design Course 17: Game Development using Cocos2d-x v3 C++ Course 18: C++ Development: The Complete Coding Guide Course 19: .NET Core API Development Course 20: Stripe with C# Learning Outcomes of Software Engineering Bundle: Fluent coding in Python, C++, JavaScript, and more. Web development mastery with HTML, CSS, and Bootstrap. Expertise in machine learning, AI, and 3D programming. Proficiency in WordPress, PHP, and Node.js. Penetration testing skills for enhanced cybersecurity. Creating iOS and Android apps using Flutter & Dart. Building a successful SaaS company from scratch. Why Choose Our Software Engineering Bundle? FREE Software Engineering certificate accredited Get a free student ID card with Software Engineering Training Get instant access to this Software Engineering course. Learn Software Engineering from anywhere in the world The Software Engineering is affordable and simple to understand The Software Engineering is an entirely online, interactive lesson with voiceover audio Lifetime access to the Software Engineering course materials The Software Engineering comes with 24/7 tutor support If you aim to enhance your Software Engineering skills, our comprehensive Software Engineering course is perfect for you. Designed for success, this Software Engineering course covers everything from basics to advanced topics in Software Engineering. Dive into the magic of coding with courses like "Coding with Scratch" and "C# Programming - Beginner to Advanced". Harness the power of AI and data with "Machine Learning with Python Course", and effortlessly create stunning websites with "Modern PHP Web Development w/ MySQL, GitHub & Heroku". Explore cutting-edge technologies such as "Node JS: API Development with Swagger" and "Three.js & WebGL 3D Programming Crash Course. With these courses, you'll not only master programming languages but also gain the skills to secure web applications with "Web Application Penetration Testing Course" and "Penetration Testing with OWASP ZAP: Mastery Course". Each lesson in this Software Engineering course is crafted for easy understanding, enabling you to become proficient in Software Engineering. Whether you are a beginner or looking to sharpen your existing skills, this Software Engineering is the ideal choice. CPD 200 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Software Engineering Bundle is ideal for: Aspiring programmers seeking comprehensive language expertise. Web developers aiming to build responsive and engaging sites. Tech enthusiasts interested in AI, machine learning, and 3D programming. Individuals looking to enter the world of app development. Requirements You will not need any prior background or expertise in this Software Engineering bundle. Career path This Software Engineering bundle will allow you to kickstart or take your career to the next stage in the related sector such as: Junior Software Engineer: £25,000 - £35,000 Web Developer: £30,000 - £40,000 Machine Learning Engineer: £40,000 - £55,000 App Developer: £35,000 - £45,000 Penetration Tester: £30,000 - £45,000 SaaS Entrepreneur: Potential for substantial earnings. Certificates Digital certificate Digital certificate - Included Hard copy certificate Hard copy certificate - Included

The comprehensive Complete U&P AI - Natural Language Processing (NLP) with Python has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Complete U&P AI - Natural Language Processing (NLP) with Python today, and learn from the very best the industry has to offer! This best selling Complete U&P AI - Natural Language Processing (NLP) with Python has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Complete U&P AI - Natural Language Processing (NLP) with Python is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Complete U&P AI - Natural Language Processing (NLP) with Python is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Complete U&P AI - Natural Language Processing (NLP) with Python is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Complete U&P AI - Natural Language Processing (NLP) with Python, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Complete U&P AI - Natural Language Processing (NLP) with Python will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Complete U&P AI - Natural Language Processing (NLP) with Python to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

Course Overview Gain the capability to multi-skill in programming and make a full stack web GIS application by taking this course on creating Machine Learning for Predictive Maps in Python and Leaflet Level 4. This expertly compiled coaching will show you how to use the tools and functions of the most widely-used programming language in the world to make powerful forecasting applications. This comprehensive Python tutorial is the perfect way to learn how to create impressive and practical pieces of IT work. From basic installations to setting up unique predictive environments, you will be shown how to produce complex forecasting routines right from scratch and for all manner of uses. By mastering the foundational skills and knowledge of machine learning on the most popular platform, you will raise your profile to a higher level for employment opportunities and organisational value in the IT sector.  This best selling Machine Learning for Predictive Maps in Python and Leaflet Level 4 has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning for Predictive Maps in Python and Leaflet Level 4 is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Machine Learning for Predictive Maps in Python and Leaflet Level 4 is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Machine Learning for Predictive Maps in Python and Leaflet Level 4 is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Machine Learning for Predictive Maps in Python and Leaflet Level 4, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning for Predictive Maps in Python and Leaflet Level 4 will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Machine Learning for Predictive Maps in Python and Leaflet Level 4 to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

About Course Data Science and Data Analytics with Python: A Comprehensive Course for Beginners Unlock the power of data and gain insights that drive informed decisions with this comprehensive course on data science and data analytics with Python. This course is designed for beginners of all skill levels, with no prior programming experience required. You will learn the essential skills to embark on your data-driven journey, including: Data manipulation with NumPy and Pandas Data visualization with Matplotlib and Seaborn Statistical analysis with Python Machine learning and artificial intelligence You will also gain hands-on experience with real-world data projects, allowing you to apply your newfound knowledge to solve real-world problems. By the end of this course, you will be able to: Understand the fundamentals of data science and data analytics Apply Python to manipulate, visualize, and analyze data Use Python to build machine learning and artificial intelligence models Solve real-world data problems This course is the perfect launchpad for your data science journey. Whether you are looking to pivot your career, enhance your skill set, or simply quench your curiosity, this course will give you the foundation you need to succeed. Enroll today and start exploring the fascinating world of data science together! What Will You Learn? Understand the fundamentals of data science and data analytics Apply Python to manipulate, visualize, and analyze data Use Python to build machine learning and artificial intelligence models Solve real-world data problems Course Content Introduction to Python Data Science Introduction to Python Data Science Environment Setup Data Cleaning Packages Working with the Numpy package Working with Pandas Data science package Data Visualization Packages Working with Matplotlib Data Science package (Part - 1) Working with Matplotlib Data Science (Part - 2) A course by Uditha Bandara Microsoft Most Valuable Professional (MVP) RequirementsBeginners level knowledge for working with Data .Programming knowledge not required. Audience Beginners with no prior programming experience Anyone interested in learning data science and data analytics Audience Beginners with no prior programming experience Anyone interested in learning data science and data analytics

In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.

Overview Python Programming for Kids and Beginners Course is yet another 'Teacher's Choice' course from Teachers Training for a complete understanding of the fundamental topics. You are also entitled to exclusive tutor support and a professional CPD-accredited certificate in addition to the special discounted price for a limited time. Just like all our courses, this Python Programming for Kids and Beginners Course and its curriculum have also been designed by expert teachers so that teachers of tomorrow can learn from the best and equip themselves with all the necessary skills. Consisting of several modules, the course teaches you everything you need to succeed in this profession. The course can be studied part-time. You can become accredited within 05 Hours studying at your own pace. Your qualification will be recognised and can be checked for validity on our dedicated website. Why Choose Teachers Training Some of our website features are: This is a dedicated website for teaching 24/7 tutor support Interactive Content Affordable price Courses accredited by the UK's top awarding bodies 100% online Flexible deadline Entry Requirements No formal entry requirements. You need to have: Passion for learning A good understanding of the English language Be motivated and hard-working Over the age of 16. Certification CPD Certification from The Teachers Training Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish. You can get your digital certificate (PDF) for £4.99 only Hard copy certificates are also available, and you can get one for only £10.99 You can get both PDF and Hard copy certificates for just £12.99! The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs. Section 01: Introduction to the Course Introduction 00:02:00 Course Curriculum 00:03:00 How to Get Pre-Requisites 00:02:00 Getting started on Windows, Linux or MacOs 00:02:00 How to ask Great Questions 00:02:00 Section 02: Setting up Local Coding Environment What is Python 00:02:00 Installing Python step by step 00:05:00 Setting up Pycharm for project 00:04:00 Installing Pycharm IDE (Code Editor) 00:08:00 Creating Python .py files 00:03:00 Hello World program 00:03:00 Section 03: Drawing with Python - Level 1 Introduction to turtle 00:03:00 Let's make a turtle 00:05:00 Move your turtle 00:03:00 Create triangle 00:05:00 Create square 00:04:00 Assessment 1 00:01:00 Solution for assessment 1 00:02:00 Section 03: Drawing with Python - Level 1 Introduction to variables 00:02:00 Datatypes 00:07:00 What is a variable 00:05:00 Using a variable 00:07:00 Learn Maths with coding 00:05:00 Assessment 2 00:02:00 Solution for assessment 2 00:02:00 Section 05: Logical operators Introduction to operators 00:02:00 How to think logically 00:04:00 Logical operators 00:02:00 AND operator 00:04:00 OR operator 00:02:00 NOT operator 00:05:00 Assessment 3 00:02:00 Solution for assessment 3 00:01:00 Section 06: IF-else Statements If-else Introduction 00:03:00 If statement 00:05:00 If-else statement 00:03:00 Assessment 4 00:01:00 Solution for assessment 4 00:02:00 Section 07: Creating Patterns Creating Patterns 00:03:00 How to Repeat 00:04:00 How a For Loop Works 00:03:00 Let's Experiment 00:03:00 Results 00:02:00 Assessment 5 00:01:00 Solution for Assessment 5 00:01:00 Section 08: Drawing with Python - Level 2 Adding colour 00:02:00 Create circle 00:05:00 Fill Colors on circles 00:03:00 Dots, Pensize 00:03:00 Assessment 6 00:01:00 Solution for assessment 6 00:02:00 Section 09: Project Project overview 00:01:00 Project Source Code Solution 00:02:00

Network management technologies course description A comprehensive tour of the available network management technologies available for todays networks. The course starts with basic tools such as syslog along with Python network automation. SNMP is then covered with the *flow technologies and streaming telemetry. Configuration management with ansible, Python, NETCONF and RESTCONF is then studied. The final part of the course looks at SDN. Hands on sessions are used throughout to reinforce the theory rather than teach specific manufacturer equipment. Note that sections are available as individual courses. What will you learn Evaluate network management technologies. Evaluate network management technologies. Recognise the weaknesses of SNMP versus NETCONF and streaming telemetry. Explain the role of NETCONF and RESTCONF. Compare & contrast *flow and streaming telemetry. Explain the role of SDN in network management. Automate network configuration with ansible and Python. Network management technologies course details Who will benefit: Those wishing to manage networks. (Previous Python experience is NOT needed) Prerequisites: Intro to data comms Duration 5 days Network management technologies course content Basic network management Network management What is network management? Benefits, issues. FCAPS model. Fault management, Configuration management, accounting, performance, security. What to manage, what not to manage. Managing network devices, managing servers. Monitoring networks Traditional network tools Ping..., SSH, syslog, TFTP for configurations. nmap. Wireshark. CLI. Web based management. Splunk. Nessus, snort, Kali. Hands on syslog, network inventories. Network automation using the CLI Programming and automating networks, netOps. Python, Git. Python network modules, SSH, paramiko, netmiko. EVE-NG. Hands onPython network modules. Structured versus unstructured data Problems with automation and unstructured data. XML, JSON, YAML. The role of YANG. Hands on Parsing data. SNMP SNMP architecture, SNMP MIBs, SMI, the SNMP protocol, polling security. Configuring SNMP. SNMPv1, v2, v3, SNMP security. Which version should you use? MIBs and MIB structure. mib-2, extra parts of mib-2, Private enterprise MIBs. Summary: What SNMP is good/bad at. Hands on Configuring agents and a NMS. MIB browsing. Server management Microsoft, Linux, application polling. WMI vs SNMP. Hands on: Application polling. Performance management *flow Polling, push vs pull, netflow, sflow, IPFIX, *flow. Flows. Where to monitor traffic. Comparing *flow with SNMP. Architecture: Generators and collectors. When flows are exported. NetFlow reporting products. SolarWinds. Hands on Netflow configuration. Collectors. Streaming telemetry Model driven telemetry, periodic/on change. Structured data. Telemetry protocol stack. gRPC and gNMI. Protobuf. gNMI operations. Telemetry architecture. Telegraf, databases, Grafana. Hands on Telemetry example. Configuration management Configuration management tools Chef, puppet, ansible, saltstack. Ansible architecture, controlling machines, nodes, agentless, SSH, modules. Inventories, playbooks, modules, network modules, jinja2 templates. Hands on Network configuration with ansible. NETCONF What is NETCONF? Protocol stack, Data stores, traffic flows, validating configurations, rollback. YANG data models and how YANG is used by NETCONF. XML. Explorers and other tools. Hands on anx, Python and NETCONF. RESTCONF The REST API, HTTP, What is RESTCONF? Tools including Postman. Comparison with NETCONF. Hands on Configuration with RESTCONF. Python network automation: configuration SSH issues. Using structured data. Jinja2. ncclient, requests, NAPALM, Nornir. Automated testing. Hands on Python network device configuration with nornir. Software Defined Networks and orchestration Classic SDN What is SDN? benefits. SDN architecture. SDN applications, SDN switches, SDN controllers, Network Operating Systems. Control plane, data plane. Northbound interfaces. SDN components. Southbound interfaces. OpenFlow. ONF, OpenFlow ports, Flow tables. Network virtualization Virtual networks, virtual switches, NfV. Service chaining. NfV and SDN. SDN implementations Classic SDN, Hybrid SDN, SDN via APIs, SDN via overlays. Data centre SDN, VXLAN, Service Provider SDN, SD WAN, Enterprise SDN, WiFi. SDN and open source OpenDaylight, OpenVSwitch, Open Networking Forum, Open Network Operating System. Hands onOpenStack. SD-WAN What is SD-WAN? Architecture: Edge, gateway, orchestrator, controller. Overlay and underlay. Use of MPLS, 4G/5G. Benefits and features. Secure Access Service Edge (SASE).

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python 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 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 00:04:00 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 Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 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 00:06:00 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 - Data Science & Machine Learning with Python 00:00:00