314 Algorithms courses delivered Online

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

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

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

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GLA Tutors: Empowering Young Minds in Computer Coding At GLA Tutors, we are passionate about equipping children with the essential skills needed to thrive in today's digital world. Our tutoring website offers a comprehensive and engaging learning experience for children who are eager to explore the exciting world of computer coding. With our online tutoring services, we strive to make coding education accessible and convenient for children of all ages. Our team of expert tutors are highly skilled in teaching computer coding concepts in a fun and interactive manner. They have a deep understanding of various programming languages and frameworks, ensuring that students receive top-notch instruction tailored to their individual needs and skill levels. Our provision for tutoring computer coding to children is designed to foster creativity, problem-solving skills, and logical thinking. Through our carefully curated curriculum, we introduce young learners to the fundamentals of coding, including algorithms, variables, loops, conditionals, and more. We believe in a hands-on approach, allowing students to actively apply what they learn through practical coding exercises and projects. At GLA Tutors, we understand that each child has their own unique learning style and pace. That's why our tutors provide personalized attention to every student, offering guidance and support every step of the way. Whether your child is a beginner or has some coding experience, we have tailored programs to suit their specific needs and help them progress confidently.

Overview With the ever-increasing demand for Data Analysis Level 3 Diploma in personal & professional settings, this online training aims at educating, nurturing, and upskilling individuals to stay ahead of the curve - whatever their level of expertise in Data Analysis Level 3 Diploma may be. Learning about Data Analysis Level 3 Diploma or keeping up to date on it can be confusing at times, and maybe even daunting! But that's not the case with this course from Compete High. We understand the different requirements coming with a wide variety of demographics looking to get skilled in Data Analysis Level 3 Diploma . That's why we've developed this online training in a way that caters to learners with different goals in mind. The course materials are prepared with consultation from the experts of this field and all the information on Data Analysis Level 3 Diploma is kept up to date on a regular basis so that learners don't get left behind on the current trends/updates. The self-paced online learning methodology by compete high in this Data Analysis Level 3 Diploma course helps you learn whenever or however you wish, keeping in mind the busy schedule or possible inconveniences that come with physical classes. The easy-to-grasp, bite-sized lessons are proven to be most effective in memorising and learning the lessons by heart. On top of that, you have the opportunity to receive a certificate after successfully completing the course! Instead of searching for hours, enrol right away on this Data Analysis Level 3 Diploma course from Compete High and accelerate your career in the right path with expert-outlined lessons and a guarantee of success in the long run. Who is this course for? While we refrain from discouraging anyone wanting to do this Data Analysis Level 3 Diploma course or impose any sort of restrictions on doing this online training, people meeting any of the following criteria will benefit the most from it: Anyone looking for the basics of Data Analysis Level 3 Diploma , Jobseekers in the relevant domains, Anyone with a ground knowledge/intermediate expertise in Data Analysis Level 3 Diploma , Anyone looking for a certificate of completion on doing an online training on this topic, Students of Data Analysis Level 3 Diploma , or anyone with an academic knowledge gap to bridge, Anyone with a general interest/curiosity Career Path This Data Analysis Level 3 Diploma course smoothens the way up your career ladder with all the relevant information, skills, and online certificate of achievements. After successfully completing the course, you can expect to move one significant step closer to achieving your professional goals - whether it's securing that job you desire, getting the promotion you deserve, or setting up that business of your dreams.     Course Curriculum Module 1 Introduction to Data Analysis. Introduction to Data Analysis. 00:00 Module 2 Mathematics and Statistics. Mathematics and Statistics. 00:00 Module 3 Data Manipulation. Data Manipulation. 00:00 Module 4 Data Visualisation. Data Visualisation. 00:00 Module 5 Data Wrangling. Data Wrangling. 00:00 Module 6 Data Exploration. Data Exploration. 00:00 Module 7 Machine Learning Fundamentals. Machine Learning Fundamentals. 00:00 Module 8 Machine Learning Algorithms. Machine Learning Algorithms. 00:00 Module 9 Data Analysis with Python and Libraries. Data Analysis with Python and Libraries. 00:00 Module 10 Data Analysis with R and Libraries. Data Analysis with R and Libraries. 00:00