93 Algorithm courses in Liverpool delivered Live Online

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms. Course Outline: Supervised Machine Learning: Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: Clustering Algorithms: K-means clustering, Hierarchical Clustering Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting Reinforcement learning Algorithms: Q-Learning Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: Python Machine Learning Certificate on completion Python Machine Learning notes Practical Python Machine Learning exercises and code examples After the course, 1 free, online session for questions or revision Python Machine Learning. Max group size on this Python Machine Learning is 4. Refund Policy No Refunds

Computer Science GCSE Syllabus The GCSE Computer Science Tutor Syllabus is designed to provide tutors in England with a comprehensive framework for teaching the GCSE Computer Science curriculum effectively. This syllabus aims to equip tutors with the necessary knowledge and skills to support students in their understanding and application of core computer science concepts.  Module 1: Introduction to Computer Science - Overview of computer science and its relevance in today's world - Understanding the components of a computer system - Introduction to algorithms and problem-solving techniques - Exploration of programming languages and their uses Module 2: Computer Hardware - Understanding the main components of a computer system, including CPU, memory, and storage devices - Exploring input and output devices and their functionalities - Understanding the role of operating systems and software in computer systems Module 3: Software Development - Introduction to programming concepts and languages (e.g., Python or Java) - Understanding variables, data types, and operators - Building algorithms and logical reasoning skills - Introduction to flowcharts and pseudocode - Implementation of simple programs and debugging techniques Module 4: Data Representation - Understanding binary, hexadecimal, and denary number systems - Representation of text, images, and sound using binary - Introduction to data compression and encryption techniques Module 5: Computer Networks - Understanding the basics of computer networks, including LAN, WAN, and the Internet - Introduction to network topologies, protocols, and security - Exploring the impact of digital communication on society Module 6: Cybersecurity and Ethical Issues - Understanding the importance of cybersecurity and data protection - Introduction to common threats and vulnerabilities - Exploring ethical issues related to computer science, such as privacy and intellectual property rights Module 7: Algorithms and Programming Techniques - Advanced programming concepts, including conditionals, loops, and functions - Introduction to sorting and searching algorithms - Exploring data structures, such as arrays and lists Module 8: System Architecture - Understanding the structure and function of a CPU - Introduction to memory hierarchy and cache - Exploring the Von Neumann architecture and its limitations Module 9: Computational Thinking and Problem Solving - Advanced problem-solving techniques using computational thinking - Introduction to algorithms for complex problems - Exploring algorithmic efficiency and optimization techniques Module 10: Exam Preparation and Revision - Reviewing key concepts covered throughout the syllabus - Practicing past exam questions and providing guidance on exam techniques - Supporting students with exam preparation strategies Please note that the duration and depth of each module can vary depending on the level of expertise required and the specific needs of the learners. Additionally, it's important to adapt the curriculum to the learners' proficiency levels, whether they are A Level/GCSE students or adult learners with different experience levels.

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.

Data Ethics for Business Professionals is designed for individuals who are seeking to demonstrate an understanding of the ethical uses of data in business settings.

Course Overview The Financial Engineering Courses – 8 Courses Bundle is a comprehensive training package designed to equip learners with a robust understanding of the mathematical, statistical, and analytical foundations of modern finance. This bundle merges core financial theories with quantitative models to support strategic financial decision-making. Learners will explore concepts ranging from derivatives pricing and risk modelling to portfolio optimisation and stochastic processes, gaining a solid foundation in the methodologies used in financial institutions and investment firms. By the end of the course, participants will be able to interpret financial models, assess risk frameworks, and contribute effectively to quantitative analysis roles. This course is particularly valuable for those aiming to strengthen their understanding of finance through an analytical lens, aligning with roles in investment banking, financial consultancy, or quantitative research. Course Description This course bundle offers eight interconnected modules covering a wide array of financial engineering topics. Learners will be introduced to financial mathematics, stochastic calculus, fixed income securities, options pricing, quantitative risk management, and algorithmic trading strategies. The programme provides an in-depth look into the mechanics of financial markets and the computational tools that underpin asset valuation and portfolio structuring. Through clear instruction and structured content, learners will build confidence in applying quantitative techniques and economic theory to real-world financial challenges. Emphasis is placed on theoretical rigour, analytical accuracy, and strategic application. Ideal for learners with a background or interest in finance, mathematics, or economics, this course supports a deeper understanding of financial markets and prepares participants for roles that require data-driven financial modelling and risk assessment skills. Course Modules Module 01: Introduction to Financial Engineering Module 02: Financial Mathematics and Modelling Techniques Module 03: Fixed Income Securities and Yield Curves Module 04: Derivatives and Options Pricing Theory Module 05: Quantitative Risk Management Fundamentals Module 06: Stochastic Processes in Finance Module 07: Portfolio Theory and Investment Analysis Module 08: Algorithmic and Computational Finance (See full curriculum) Bundle Instructions Access all eight courses through a single enrolment. Courses are self-paced and available online 24/7. Learners receive a certificate for each completed module. Support is available throughout the learning journey. Who is this course for? Individuals seeking to build a strong foundation in quantitative finance. Professionals aiming to advance their knowledge in financial modelling and analysis. Beginners with an interest in finance, mathematics, or data-driven investment theory. Graduates or career changers targeting roles in banking, analytics, or asset management. Career Path Quantitative Analyst Financial Risk Manager Investment Banking Analyst Asset or Portfolio Manager Financial Modelling Consultant Data Analyst in Financial Services Derivatives Pricing Specialist Economic Research Associate

Twitter Marketing for Profit Course Overview The "Twitter Marketing for Profit" course equips learners with the knowledge and strategies needed to leverage Twitter as a powerful marketing tool. It covers key aspects such as crafting effective tweets, growing a dedicated following, using analytics to improve engagement, and turning Twitter presence into tangible business profit. Designed for marketers and entrepreneurs, this course delivers real-world techniques and tactics for maximising Twitter's potential in boosting brand visibility and revenue. By the end, learners will have a clear understanding of how to create and manage a successful Twitter marketing campaign that drives measurable results. Course Description This course dives deep into the core principles of Twitter marketing, focusing on audience engagement, content creation, and analytics. Learners will explore topics such as optimising profile setups, developing content calendars, understanding Twitter algorithms, and implementing targeted advertising strategies. The course also introduces the importance of hashtags, influencer partnerships, and trends. Participants will gain insights into how to monitor campaign performance and adapt strategies accordingly. The course is structured to provide learners with a comprehensive understanding of Twitter marketing, helping them to drive engagement and achieve business goals effectively. Twitter Marketing for Profit Curriculum Module 01: Introduction to Twitter Marketing Module 02: Building a Twitter Strategy Module 03: Content Creation and Curation Module 04: Growing Your Audience and Engagement Module 05: Leveraging Twitter Analytics for Insights Module 06: Advertising on Twitter Module 07: Measuring Success and Adjusting Strategies (See full curriculum) Who is this course for? Individuals seeking to enhance their digital marketing skills. Professionals aiming to optimise Twitter for business growth. Beginners with an interest in social media marketing. Entrepreneurs wanting to promote their business via Twitter. Career Path Social Media Manager Digital Marketing Specialist Content Strategist Brand Manager Twitter Marketing Consultant

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.

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 the fundamentals of coding and basics of C++ and object-oriented programming concepts. This course is for Non-Developers, or anyone who wants to have a basic understanding of and learn how to code C++ applications and syntax Overview Companies are constantly challenged to keep their applications, development projects, products, services (and programmers!) up to speed with the latest industry tools, skills, technologies and practices to stay ahead in the ever-shifting markets that make up today's fiercely competitive business landscape. The need for application, web and mobile developers and coders is seemingly endless as technologies regularly change and grow to meet the modern needs of demanding industries and clients. C++ and Programming Basics for Non-Programmers is a five-day, basic-level training course geared for IT candidates who have little or no prior experience in computer programming. Throughout this gentle introduction to programming and C++, students will learn to create applications and libraries using C++ using best practices and sound OO development techniques for writing object-oriented programs in C++. Special emphasis is placed on object-oriented concepts and best practices throughout the training. Fundamentals of the Program Development Cycle Computer Architecture The Notion of Algorithms Source Code vs. Machine Code Compile-Time vs. Run-Time Software Program Architecture Standalone Client/Server Distributed Web-Enabled IDE (Interactive Development Environment) Concepts Looping Constructs Counter-Controlled Repetition Sentinel-Controlled Repetition Nested Control Constructs break and continue Statements Structured Programming Best Practices Writing Methods (Functions) Static vs. Dynamic Allocation Declaring Methods Declaring Methods with Multiple Parameters Method-Call Stack Scope of Declarations Argument Promotion and Casting Designing Methods for Reusability Method Overloading Arrays Purpose of Arrays Declaring and Instantiating Arrays Passing Arrays to Methods Multidimensional Arrays Variable-Length Argument Lists Using Command-Line Arguments Using Environment Variables Deeper Into Classes and Objects Controlling Access to Class Members Referencing the Current Object Using this Overloading Constructors Default and No-Argument Constructors Composition of Classes Garbage Collection and Destructors The finalize Method Static Class Members Defining Classes Using Inheritance Application Development Fundamentals Structure of a C++ Program Memory Concepts Fundamental Data Type Declarations Fundamental I/O Concepts Fundamental Operators Arithmetic Operators Logical Operators Precedence and Associativity Building and Deploying a C++ Program Superclasses and Subclasses Advantages of Using Inheritance protected Class Members Constructors in Subclasses Increasing Convenience by Using Polymorphism Purpose of Polymorphic Behavior The Concept of a Signature Abstract Classes and Methods final Methods and Classes Purpose of Interfaces Using and Creating Interfaces Common Interfaces of the C++ API Files and Streams Concept of a Stream Class File Sequential Access Object Serialization to/from Sequential Access Files Fundamental Searching and Sorting Introduction to Searching Algorithms Linear Search Binary Search Introduction to Sorting Algorithms Selection Sort Insertion Sort Merge Sort Fundamental Data Structures Dynamic Memory Allocation Linked Lists Stacks Queues Trees Exception Handling Types of Exceptions Exception Handling Overview Introduction to Classes and Objects Classes, Objects and Methods Object Instances Declaring and Instantiating a C++ Object Declaring Methods set and get Methods Initiating Objects with Constructors Primitive Types vs. Reference Types Flow Control Conditional Constructs Exception Class Hierarchy Extending Exception Classes When to Throw or Assert Exceptions Formatted Output printf Syntax Conversion Characters Specifying Field Width and Precision Using Flags to Alter Appearance Printing Literals and Escape Sequences Formatting Output with Class Formatter Strings, Characters and Regular Expressions Fundamentals of Characters and Strings String Class String Operations StringBuilder Class Character Class StringTokenizer Class Regular Expressions Regular Expression Syntax Pattern Class Matcher Class Fundamental GUI Programming Concepts Overview of Swing Components Displaying Text and Graphics in a Window Event Handling with Nested Classes GUI Event Types and Listener Interfaces Mouse Event Handling Layout Managers Additional course details: Nexus Humans C Plus Plus and Programming Basics for Non-Programmers (TTCP2000) 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 C Plus Plus and Programming Basics for Non-Programmers (TTCP2000) 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.