444 Algorithm courses in London delivered Online

Dive into the fascinating world of deep learning with this expertly crafted course designed to unravel the mysteries of neural networks using R. This course guides you through the core principles of neural networks, illustrating how layers of algorithms mimic the human brain’s ability to identify patterns and make decisions. Whether you’re a data enthusiast or a professional seeking to enhance your analytical toolkit, this course offers a clear and engaging path to understanding deep learning concepts through the power of R programming. With a sharp focus on theory and application, you will explore how to build, train, and optimise neural networks effectively, while leveraging R’s rich ecosystem of libraries and tools. The course content is designed to maintain a perfect balance between depth and clarity, making complex topics accessible without oversimplification. By the end, you will be equipped with a strong conceptual foundation and the confidence to approach deep learning projects with R, all through an engaging online format that fits seamlessly into your schedule. Learning Outcomes: Understanding of single-layer and multi-layer neural networks Knowledge of R programming for neural network applications Implementation of neural networks in real-world projects Familiarity with agriculture and war datasets for neural network modelling Ability to evaluate neural network model accuracy and performance The Deep Learning Neural Network with R course is designed to provide learners with a comprehensive understanding of how to build and evaluate neural networks using R programming language. The course includes four modules that cover single-layer and multi-layer neural networks applied to agriculture and war datasets. Each module contains practical hands-on projects that allow learners to gain real-world experience in neural network development and evaluation. By the end of the course, learners will have a solid understanding of neural network concepts, R programming language, and practical experience with real-world datasets. Deep Learning Neural Network with R Course Curriculum Section 01: Single Layer Neural Networks Project - Agriculture (Part - 1) Section 02: Single Layer Neural Networks Project - Agriculture (Part - 2) Section 03: Multi-Layer Neural Networks Project - Deaths in wars (Part - 1) Section 04: Multi-Layer Neural Networks Project - Deaths in wars (Part - 2) How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Data analysts and scientists seeking to expand their knowledge of neural networks and R programming Professionals interested in applying neural networks to agriculture or war datasets Students and researchers interested in deep learning and machine learning techniques Anyone looking to enhance their skills in data analysis and modelling using neural networks and R programming Requirements There are no formal entry requirements for the course, with enrollment open to anyone! Career path Data Analyst Machine Learning Engineer Data Scientist Artificial Intelligence Developer Research Scientist Entry-level positions such as Data Analysts can expect to earn between £25,000 to £35,000 per annum, whereas senior-level positions such as Machine Learning Engineers can earn upwards of £70,000 per annum. Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

If you're fascinated by the mysteries of market movement and want to explore how machines attempt to predict tomorrow's numbers today, this Stock Price Prognostics Course will suit your curious streak perfectly. Built around deep learning models tailored for time series forecasting, the course takes a closer look at how algorithms can be trained to recognise patterns, spot trends, and occasionally raise an eyebrow at the randomness of stock data. It’s technical, structured, and surprisingly satisfying — no crystal ball involved, just code that tries its best. Focusing on stock price prognostics through real datasets, the course guides you through model building, evaluation metrics and common forecasting techniques using neural networks — all in plain English, with clear explanations and zero mystique. It’s suited to those who enjoy a challenge, appreciate a bit of data drama, and prefer graphs that actually say something. Whether you're brushing up on deep learning or building from the ground up, this course connects theory with application in a focused, jargon-light approach — and no need to wear a tie or trade a share. Learning Outcomes: Build a deep learning model using RNN that can accurately predict stock prices. Preprocess data and perform exploratory data analysis. Scale features and make predictions on test data. Gain real-world experience in deep learning development. Contribute to the development of cutting-edge technology in the finance industry. The Hands-on Deep Learning Projects - Stock Price Prognostics course is designed to provide you with the skills and knowledge needed to develop a deep learning model using RNN that can accurately predict stock prices. In this course, you'll learn how to preprocess data, perform exploratory data analysis, scale features, and make predictions on test data. The course is perfect for aspiring data scientists, machine learning engineers, and developers interested in deep learning development. By the end of this course, you'll have a deep understanding of how to build a deep learning model that can revolutionise the world of finance. With hands-on experience in developing cutting-edge technology, you'll be well-equipped to start a career in deep learning and contribute to the development of cutting-edge technology in the finance industry. ⊱⊱ Hands on Deep Learning Projects - Stock price Prognostics Course Curriculum Section 01: Introduction Section 02: Installation of Tools and Libraries Section 03: Dataset Section 04: EDA Section 05: Feature Scaling Section 06: Building RNN Section 07: Prediction on Test Data Section 08: Output How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Aspiring data scientists. Machine learning engineers. Developers interested in deep learning development. Anyone interested in the field of deep learning. Professionals looking to upskill in the latest technology. Career path Data Scientist: £40,000 to £80,000 per year. Machine Learning Engineer: £55,000 to £90,000 per year. Artificial Intelligence Developer: £40,000 to £80,000 per year. Quantitative Analyst: £30,000 to £80,000 per year. Financial Data Analyst: £25,000 to £60,000 per year. Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

Welcome to the wonderfully witty world of ChatGPT, where your curiosity meets cutting-edge technology without needing a computer science degree or a coffee the size of your head. This crash course is your friendly, no-fluff guide to understanding what ChatGPT is, how it works, and most importantly—how you can use it without asking it “Are you conscious?” every five minutes. Whether you're a student, a professional, or someone who thinks AI only lives in sci-fi films, you'll walk away knowing how to get useful answers, craft better prompts, and avoid the common mistakes people make when chatting with this digital wordsmith. Think of this as the sat-nav for navigating the ChatGPT landscape—clear directions, a few warnings about the potholes, and no annoying robot voice. You’ll learn the what, why and how of AI-powered chat, from drafting content and brainstorming ideas to handling repetitive tasks like a pro. With jargon-free language and an engaging tone, this course is designed to bring you up to speed in less time than it takes to brew a proper cup of tea. So, pull up a chair and prepare to get acquainted with the future of communication—without the fuss. Learning Outcomes: Understand the capabilities of ChatGPT and its potential applications Learn how to sign up for an OpenAI account and set up ChatGPT Identify the benefits and limitations of using ChatGPT for business, teaching, and research Develop skills in using ChatGPT to improve customer engagement, personalised learning, and information retrieval Explore additional resources and videos to enhance your ChatGPT experience The Beginner Crash Course on ChatGPT is designed to provide learners with a comprehensive understanding of this cutting-edge technology and its potential applications. Through six modules, learners will gain an understanding of the capabilities of ChatGPT, how to sign up for an OpenAI account, and how to set up ChatGPT for business, teaching, and research purposes. Upon completing this course, learners will have the knowledge and skills to use ChatGPT to improve customer engagement, personalised learning, and information retrieval. With expert guidance and a comprehensive curriculum, this course is the key to unlocking the potential of ChatGPT and taking your interactions with technology to the next level. A Beginner Crash Course on ChatGPT Course Curriculum Sign up for an OpenAI Account What can ChatGPT do for you? ChatGPT for Business ChatGPT for Teaching ChatGPT for Research Limitations of ChatGPT How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Business owners seeking to improve customer engagement Teachers looking to provide personalised support to their students Researchers seeking answers to complex questions Anyone interested in learning about AI-powered chatbots Individuals seeking to enhance their technology skills Career path Customer service representative Online tutor or trainer Research analyst Content writer Data analyst £20,000 - £60,000+ (depending on career path and experience) Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

Curious about how to make the most of ChatGPT without getting lost in technical jargon? This Beginner Crash Course on ChatGPT offers a straightforward introduction to one of today’s most talked-about AI tools. Designed to familiarise you with the basics, it covers how to interact with ChatGPT effectively, crafting prompts that get you the best responses and understanding its capabilities and limitations. You’ll soon find yourself having conversations with AI that are surprisingly helpful — and perhaps even a little entertaining. Ideal for anyone interested in AI but unsure where to begin, this course guides you through the essentials with clarity and a touch of wit. Whether for personal curiosity, enhancing your work, or simply staying ahead of the digital curve, you’ll gain a practical grasp of how ChatGPT can assist in writing, research, brainstorming, and more. Delivered entirely online, it suits a variety of schedules and skill levels, offering a well-paced yet engaging journey into the world of conversational AI without any fuss. Learning Outcomes: Understand the capabilities of ChatGPT and its potential applications Learn how to sign up for an OpenAI account and set up ChatGPT Identify the benefits and limitations of using ChatGPT for business, teaching, and research Develop skills in using ChatGPT to improve customer engagement, personalised learning, and information retrieval Explore additional resources and videos to enhance your ChatGPT experience The Beginner Crash Course on ChatGPT is designed to provide learners with a comprehensive understanding of this cutting-edge technology and its potential applications. Through six modules, learners will gain an understanding of the capabilities of ChatGPT, how to sign up for an OpenAI account, and how to set up ChatGPT for business, teaching, and research purposes. Upon completing this course, learners will have the knowledge and skills to use ChatGPT to improve customer engagement, personalised learning, and information retrieval. With expert guidance and a comprehensive curriculum, this course is the key to unlocking the potential of ChatGPT and taking your interactions with technology to the next level. ⊱⊱ A Beginner Crash Course on ChatGPT Course Curriculum Sign up for an OpenAI Account What can ChatGPT do for you? ChatGPT for Business ChatGPT for Teaching ChatGPT for Research Limitations of ChatGPT How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Business owners seeking to improve customer engagement Teachers looking to provide personalised support to their students Researchers seeking answers to complex questions Anyone interested in learning about AI-powered chatbots Individuals seeking to enhance their technology skills Career path Customer service representative Online tutor or trainer Research analyst Content writer Data analyst £20,000 - £60,000+ (depending on career path and experience) Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

Embark on a journey into the world of technology with Spark Generation! Learn the fundamentals of computer science, coding languages, and algorithmic thinking. Discover the logic behind programs and explore the creative potential of digital innovation.

Embark on a journey into the world of technology with Spark Generation and our Cambridge self-paced courses! Learn the fundamentals of computer science, coding languages, and algorithmic thinking. Discover the logic behind programs and explore the creative potential of digital innovation.

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.