326 Algorithms courses

Dive into the world of numbers and patterns with our 'Statistical Analysis Course,' where data tells stories and predictions shape the future. In the first module, you're introduced to the vast landscape of statistics, a toolset essential for deciphering the tales hidden within data. As you progress, familiarise yourself with the fundamental statistical terminology, paving the way for a deeper grasp of how data clusters around central values. The journey through this course is a blend of theory and application, from mastering the intricacies of data variability to the advanced realms of regression analysis and predictive algorithms. Learning Outcomes Gain a solid understanding of statistics and its significance in various fields. Learn to describe and utilise basic statistical terminology and methods. Comprehend and calculate measures of central tendency and data dispersion. Develop skills in probability, distribution analysis, and statistical inference. Apply statistical methods correctly and appreciate the Bayesian approach for learning from data. Why choose this Statistical Analysis Course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Statistical Analysis Course Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Statistical Analysis Course for? Aspiring data analysts seeking a foundation in statistics. Business professionals who require analytical skills for data-driven decision-making. Students of the social sciences, economics, or any field involving data interpretation. Researchers needing a robust grasp of statistical analysis methods. Anyone interested in understanding how to utilise data for predictions and analytics. Career path Data Analyst - £25,000 to £40,000 Market Research Analyst - £23,000 to £35,000 Quantitative Analyst - £35,000 to £70,000 Statistical Researcher - £27,000 to £45,000 Business Intelligence Analyst - £30,000 to £55,000 Econometrician - £30,000 to £60,000 Prerequisites This Statistical Analysis Course does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Statistical Analysis Course 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. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 01: The Realm of Statistics The Realm Of Statistics 00:26:00 Module 02: Basic Statistical Terms Basic Statistical Terms 00:41:00 Module 03: The Center of the Data The Center of the Data 00:03:00 Module 04: Data Variability Data Variability 00:15:00 Module 05: Binomial and Normal Distributions Binomial and Normal Distributions 00:14:00 Binomial Probabilities Table 00:00:00 Z-Table 00:00:00 Module 06: Introduction to Probability Introduction to Probability 00:35:00 Module 07: Estimates and Intervals Estimates and Intervals 00:34:00 Module 08: Hypothesis Testing Hypothesis Testing 00:31:00 Module 09: Regression Analysis Regression Analysis 00:11:00 Module 10: Algorithms, Analytics and Predictions Algorithms, Analytics and Prediction 00:47:00 Module 11: Learning From Experience: The Bayesian Way Learning From Experience: The Bayesian Way 00:31:00 Module 12: Doing Statistics: The Wrong Way Doing Statistics: The Wrong Way 00:37:00 Module 13: How We Can Do Statistics Better How We Can Do Statistics Better 00:41:00 Assignment Assignment - Statistical Analysis Course 00:00:00

Duration 5 Days 30 CPD hours This course is intended for This course is designed for people who are seeking to launch a career in cybersecurity. Overview Assess the security posture of an enterprise environment and recommend and implement appropriate security solutions; Monitor and secure hybrid environments, including cloud, mobile, and IoT; Operate with an awareness of applicable laws and policies, including principles of governance, risk, and compliance; Identify, analyze, and respond to security events and incidents. CompTIA Security+ is a global certification that validates the baseline skills necessary to perform core security functions and is the first security certification a candidate should earn. CompTIA Security+ establishes the core knowledge required of any cybersecurity role and provides a springboard to intermediate-level cybersecurity jobs. Lesson 1: Summarize Fundamental Security Concepts Security Concepts Security Controls Lesson 2: Compare Threat Types Threat Actors Attack Surfaces Social Engineering Lesson 3: Explain Cryptographic Solutions Cryptographic Algorithms Public Key Infrastructure Cryptographic Solutions Lesson 4: Implement Identity and Access Management Authentication Authorization Identity Management Lesson 5: Secure Enterprise Network Architecture Enterprise Network Architecture Network Security Appliances Secure Communications Lesson 6: Secure Cloud Network Architecture Cloud Infrastructure Embedded Systems and Zero Trust Architecture Lesson 7: Explain Resiliency and Site Security Concepts Asset Management Redundancy Strategies Physical Security Lesson 8: Explain Vulnerability Management Device and OS Vulnerabilities Application and Cloud Vulnerabilities Vulnerability Identification Methods Vulnerability Analysis and Remediation Lesson 9: Evaluate Network Security Capabilities Network Security Baselines Network Security Capability Enhancement Lesson 10: Assess Endpoint Security Capabilities Implement Endpoint Security Mobile Device Hardening Lesson 11: Enhance Application Security Capabilities Application Protocol Security Baselines Cloud and Web Application Security Concepts Lesson 12: Explain Incident Response and Monitoring Concepts Incident Response Digital Forensics Data Sources Alerting and Monitoring Tools Lesson 13: Analyze Indicators of Malicious Activity Malware Attack Indicators Physical and Network Attack Indicators Application Attack Indicators Lesson 14: Summarize Security Governance Concepts Policies, Standards, and Procedures Change Management Automation and Orchestration Lesson 15: Explain Risk Management Processes Risk Management Processes and Concepts Vendor Management Concepts Audits and Assessments Lesson 16: Summarize Data Protection and Compliance Concepts Data Classification and Compliance Personnel Policies Additional course details: Nexus Humans CompTIA Security Plus Certification (Exam SY0-601) 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 CompTIA Security Plus Certification (Exam SY0-601) 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.

If you are new to Quantum computing, then this course will help you understand the fundamentals and practicalities of this field. This course will provide you with step-by-step guidance in learning the implementation and important methodologies associated with Quantum computing in a beginner-friendly environment.

This course aims to teach the programming language C++ with an emphasis on the modern features introduced in C++17. The course will cover both old and new concepts in C++, including classes, operator overloading, inheritance, polymorphism, templates, and concurrency. By the end of the course, the students will have gained the knowledge needed to become proficient C++ developers.

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

Duration 5 Days 30 CPD hours This course is intended for This course is intended for anyone who is new to software development and wants, or needs, to gain an understanding of programming fundamentals and object-oriented programming concepts. They will typically be high school students, post-secondary school students, or career changers, with no prior programming experience. They might want to gain an understanding of the core programming fundamentals before moving on to more advanced courses such as Programming in C#. Overview Explain core programming fundamentals such as computer storage and processing. Explain computer number systems such as binary. Create and use variables and constants in programs. Explain how to create and use functions in a program. Create and use decisions structures in a computer program. Create and use repetition (loops) in a computer program. Explain pseudocode and its role in programming. Explain the basic computer data structures such as arrays, lists, stacks, and queues. Implement object-oriented programming concepts. Create and use classes in a computer program. Implement encapsulation, inheritance, and polymorphism. Describe the base class library (BCL) in the .NET Framework. Explain the application security concepts. Implement simple I/O in a computer program. Identify application errors and explain how to debug an application and handle errors. Identify the performance considerations for applications. In this 5-day course, students will learn the basics of computer programming through the use of Microsoft Visual Studio 2022 and the Visual C# and Visual Basic programming languages. The course assumes no prior programming experience and introduces the concepts needed to progress to the intermediate courses on programming, Programming in C#. The focus will be on core programming concepts such as computer storage, data types, decision structures, and repetition by using loops. The course also covers an introduction to object-oriented programming covering classes, encapsulation, inheritance, and polymorphism. Coverage is also included around exception handling, application security, performance, and memory management. 1 - Introduction to Core Programming Concepts Computer Data Storage and Processing Application Types Application Lifecycle Code Compilation 2 - Core Programming Language Concepts Syntax Data Types Variables and Constants 3 - Program Flow Introduction to Structured Programming Concepts Introduction to Branching Using Functions Using Decision Structures Introducing Repetition 4 - Algorithms and Data Structures Understand How to Write Pseudocode Algorithm Examples Introduction to Data Structures 5 - Error Handling and Debugging Introduction to Program Errors Introduction to Structured Error Handling Introduction to Debugging 6 - Introduction to Object-Oriented Programming Introduction to Complex Structures Introduction to Structs Introduction to Classes Introducing Encapsulation 7 - More Object-Oriented Programming Introduction to Inheritance Introduction to Polymorphism Introduction to .NET and the Base Class Library 8 - Introduction to Application Security Authentication and Authorization Code Permissions on Computers Introducing Code Signing 9 - Core I/O Programming Using Console I/O Using File I/O 10 - Application Performance and Memory Management Value Types vs Reference Types Converting Types The Garbage Collector Additional course details: Nexus Humans 55337 Introduction to Programming 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 55337 Introduction to Programming 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.

In this course, you will quickly learn how to build DNNs (Deep Neural Networks) and how to train them. This learning-by-doing course will also help you master the elementary concepts and methodology with Python. You need to have a basic knowledge of python to get the best out of this course.

Level 5 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune

Sales Overview: Roadway/Highway Design and Engineering Software Are you ready to revolutionize your roadway and highway projects? Introducing our comprehensive Roadway/Highway Design and Engineering software, meticulously crafted to streamline every aspect of your design process. From initial conceptualization to final construction, our software offers a suite of modules tailored to meet the diverse needs of modern transportation infrastructure projects. 1. Introduction To Roadway/Highway Design and Engineering: Kickstart your projects with a solid foundation in roadway and highway design principles. Our software provides an intuitive introduction module, offering users a clear understanding of the fundamentals essential for successful project execution. With interactive tutorials and detailed resources, users can quickly familiarize themselves with industry best practices and regulatory requirements. 2. Geometric Design of Roadways/Highways: Efficient and safe roadway geometry is paramount to any transportation project. Our software's Geometric Design module empowers engineers to create optimal road alignments, intersections, and transitions with precision and ease. Through advanced algorithms and customizable parameters, users can simulate various design scenarios to achieve optimal traffic flow and safety standards. 3. Pavement Design for Roadways/Highways: Ensure the longevity and performance of your road surfaces with our Pavement Design module. Tailored to accommodate diverse traffic loads and environmental conditions, our software offers state-of-the-art pavement analysis tools. From flexible to rigid pavements, our algorithms optimize material selection and thickness design, empowering engineers to deliver sustainable infrastructure solutions. 4. Drainage Design for Roadways/Highways: Combat water accumulation and mitigate flood risks with our Drainage Design module. Our software integrates hydraulic modeling and stormwater management techniques to design efficient drainage systems for roadways and highways. With intuitive interfaces and predictive analysis capabilities, engineers can confidently implement drainage solutions that meet regulatory standards and minimize environmental impact. 5. Materials and Construction: Seamlessly transition from design to construction with our Materials and Construction module. Access a comprehensive database of construction materials and techniques, complete with cost estimations and procurement guidelines. Whether it's asphalt mixes or bridge components, our software empowers project stakeholders to make informed decisions and optimize construction processes for efficiency and quality. 6. Environmental Considerations in Roadway/Highway Design and Engineering: Embrace sustainability and environmental stewardship in every phase of your project with our Environmental Considerations module. From ecological impact assessments to carbon footprint analyses, our software equips engineers with the tools to minimize environmental disturbances and enhance project sustainability. With built-in compliance checks and mitigation strategies, users can navigate regulatory requirements with confidence while preserving natural resources. Experience the future of roadway and highway design with our cutting-edge software solution. Empower your team to deliver innovative infrastructure projects that prioritize safety, efficiency, and sustainability. Contact us today to learn more about how our Roadway/Highway Design and Engineering software can elevate your projects to new heights. Course Curriculum Module 1: Introduction To Roadway/Highway Design and Engineering Introduction To Roadway/Highway Design and Engineering 00:00 Module 2: Geometric Design of Roadways/Highways Geometric Design of Roadways/Highways 00:00 Module 3: Pavement Design for Roadways/Highways Pavement Design for Roadways/Highways 00:00 Module 4: Drainage Design for Roadways/Highways Drainage Design for Roadways/Highways 00:00 Module 5: Materials and Construction Materials and Construction 00:00 Module 6: Environmental Considerations in Roadway/Highway Design and Engineering Environmental Considerations in Roadway/Highway Design and Engineering 00:00