250 Algorithms courses in London delivered On Demand

This course provides basic introductory guidance to FinTech. You will be using an easy programming language R to learn some basic statistics in money management. You will also understand how to time the stock market and build tradeable factor-based algorithms from scratch. This course provides some of the most basic rules of thumb and intuition that every successful trader should know.

Discover the thrilling world of artificial intelligence with the 'Machine Learning Course with Python'. Immerse yourself in a voyage from foundational concepts, unveiling the mysteries behind algorithms, to diving deep into the cores of preprocessing, regression, and classification. Crafted meticulously, this course introduces Python as the catalyst, opening doors to data-driven decision-making and predictive analysis, empowering your journey in the ever-evolving field of machine learning. Learning Outcomes Grasp the foundational knowledge of various machine learning algorithms. Attain proficiency in preprocessing data for optimal outcomes. Master the nuances of regression analysis using Python. Delve into the intricacies of classification techniques. Enhance problem-solving abilities with practical Python-driven machine learning applications. Why choose this Machine Learning Course with Python 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 Machine Learning Course with Python 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 Machine Learning Course with Python course for? Aspiring data scientists eager to harness the power of machine learning. Python enthusiasts aiming to delve into its applications in AI. Professionals in the tech industry seeking a transition into data roles. Academics and researchers wanting to employ machine learning in their work. Business analysts aiming to leverage predictive analytics for better insights. Career path Data Scientist: £40,000 - £70,000 Machine Learning Engineer: £50,000 - £80,000 AI Researcher: £45,000 - £75,000 Data Analyst: £30,000 - £50,000 Python Developer: £35,000 - £65,000 Business Intelligence Developer: £40,000 - £60,000 Prerequisites This Machine Learning Course with Python does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Machine Learning Course with Python 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: Introduction to Machine Learning Algorithms Introduction to types of ML algorithm 00:02:00 Module 02: Preprocessing Importing a dataset in python 00:02:00 Resolving Missing Values 00:06:00 Managing Category Variables 00:04:00 Training and Testing Datasets 00:07:00 Normalizing Variables 00:02:00 Normalizing Variables - Python Code 00:03:00 Summary 00:01:00 Module 03: Regression Simple Linear Regression - How it works? 00:04:00 Simple Linear Regreesion - Python Implementation 00:07:00 Multiple Linear Regression - How it works? 00:01:00 Multiple Linear Regression - Python Implementation 00:09:00 Decision Trees - How it works? 00:05:00 Random Forest - How it works? 00:03:00 Decision Trees and Random Forest - Python Implementation 00:04:00 Module 04: Classification kNN - How it works? 00:02:00 kNN - Python Implementation 00:10:00 Decision Tree Classifier and Random Forest Classifier in Python 00:10:00 SVM - How it works? 00:04:00 SVM - Python Implementation 00:06:00 Assignment Assignment - Machine Learning Course with Python 00:00:00

Dive into the intricate world of deep learning with this sharply focused course on heuristic techniques using R. This programme is designed for learners keen to understand how heuristics shape machine learning models, particularly in deep neural networks. Whether you're brushing up on your algorithmic intuition or delving deeper into optimisation patterns, this course delivers a solid framework rooted in logic, not lingo. With R as your analytical companion, you'll explore neural computation, gradient-based strategies, backpropagation nuances, and heuristic-inspired adjustments that enhance model efficiency. This isn't about flashy tools or inflated claims — it's about getting your mind around how deep learning truly works, one clever calculation at a time. No fluff, no filler — just clear insights and well-paced learning to support your journey through the matrix of machine intelligence. Learning Outcomes: Develop an understanding of experimental design and how it applies to heuristics. Learn how to apply heuristics to real-world problems in the agriculture, cryptocurrency, energy, and financial markets sectors. Understand how to use R programming language for deep learning heuristics. Develop skills in analyzing data sets and creating predictive models. Learn how to implement different heuristics algorithms in R programming. The Essential Training on Deep Learning Heuristic using R is designed to provide students with a comprehensive understanding of heuristics and how they can be applied to various real-world problems. The course covers experimental design, data analysis, and predictive modelling using R programming language. Students will learn about different heuristics algorithms and how to implement them in R to solve complex problems in the agriculture, cryptocurrency, energy, and financial markets sectors. Throughout the course, students will work on various projects that will help them develop practical skills in analyzing data sets and creating predictive models. They will learn how to design experiments, collect and analyse data, and create models that can be used to make predictions. By the end of the course, students will have a strong understanding of heuristics, and deep learning, and how to apply them to solve real-world problems. Essential Training on Deep Learning Heuristic using R Course Curriculum Section 01: Experimental Design - Heuristics Project 1-Agriculture Section 02: Experimental Design - Heuristics Project 2-Cryptocurrencies Section 03: Experimental Design: Heuristics Project 3-Energy Sector Section 04: Experimental Design: Heuristics Project 4-Financial Markets 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 scientists who want to learn how to apply deep learning heuristics to solve real-world problems. Programmers who want to learn R programming language for data analysis and predictive modeling. Professionals in the agriculture, cryptocurrency, energy, and financial markets sectors who want to apply heuristics to solve complex problems. Students who want to learn about deep learning heuristics and how they can be applied to real-world problems. Anyone interested in developing practical skills in data analysis and predictive modelling using R programming language. Requirements There are no formal entry requirements for the course, with enrollment open to anyone! Career path Deep Learning Engineer: £30,000 - £90,000 AI Researcher: £40,000 - £120,000 Data Scientist: £35,000 - £90,000 Machine Learning Engineer: £40,000 - £100,000 AI Architect: £50,000 - £150,000 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.

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Description Business Intelligence Diploma In today's fast-paced business world, understanding and utilising data is pivotal. The Business Intelligence Diploma offers individuals an online platform to gain a comprehensive understanding of the domain of business intelligence (BI). By embarking on this digital learning journey, participants will gain vital knowledge in areas such as data collection, warehousing solutions, and visualization techniques. Beginning with an introduction to Business Intelligence, learners will quickly gain a solid foundation. The importance of efficiently gathering and managing data is undisputed in modern businesses. This course provides a thorough understanding of data collection and management, ensuring that students are well-equipped to handle vast amounts of information. An equally important component of BI is how data is stored. The Business Intelligence Diploma covers data warehousing solutions, ensuring participants know how to store, retrieve, and manage data in an efficient manner. Once data is securely stored, the next step is to make sense of it. Through this course's focus on data visualization techniques, learners will discover how to present data in clear, compelling formats. Of course, data alone is just a tool. The true value comes from using this data to make informed decisions. The course delves into analytical models and algorithms, providing students with the means to interpret complex data sets and extract meaningful insights. These insights then play a pivotal role in business decision-making, another key area covered by the Business Intelligence Diploma. KPIs (Key Performance Indicators) and performance metrics are crucial elements in evaluating the success and impact of business initiatives. In the Business Intelligence Diploma, participants will learn to identify, measure, and analyse these metrics, ensuring that business strategies align with objectives and goals. Implementing business intelligence within an organisation is not without its challenges. Thus, the diploma also touches on BI implementation and change management. This ensures that students are prepared not only to understand BI but to lead its integration within their organisations. The business landscape is ever-evolving, with new trends constantly emerging. Staying abreast of these changes is crucial. The Business Intelligence Diploma provides insights into emerging trends in the BI world, ensuring that learners remain at the forefront of the industry. Lastly, measuring the impact and ROI (Return on Investment) of Business Intelligence is vital in evaluating its effectiveness. This course provides the tools and knowledge required to assess the tangible benefits of BI initiatives within an organisation. In conclusion, the Business Intelligence Diploma offers an online, comprehensive curriculum for those wishing to master the intricacies of business intelligence. From data collection to measuring BI's impact, this course ensures that its participants are well-equipped to thrive in the ever-evolving world of business. By completing this diploma, individuals will undoubtedly be better prepared to harness the power of data and use it to drive informed business decisions. Join the Business Intelligence Diploma today and embark on a transformative digital learning experience. What you will learn 1:Introduction to Business Intelligence 2:Data Collection and Management 3:Data Warehousing Solutions 4:Data Visualization Techniques 5:Analytical Models and Algorithms 6:Business Intelligence in Decision Making 7:KPIs and Performance Metrics 8:BI Implementation and Change Management 9:Emerging Trends in Business Intelligence 10:Measuring the Impact and ROI of Business Intelligence Course Outcomes After completing the course, you will receive a diploma certificate and an academic transcript from Elearn college. Assessment Each unit concludes with a multiple-choice examination. This exercise will help you recall the major aspects covered in the unit and help you ensure that you have not missed anything important in the unit. The results are readily available, which will help you see your mistakes and look at the topic once again. If the result is satisfactory, it is a green light for you to proceed to the next chapter. Accreditation Elearn College is a registered Ed-tech company under the UK Register of Learning( Ref No:10062668). After completing a course, you will be able to download the certificate and the transcript of the course from the website. For the learners who require a hard copy of the certificate and transcript, we will post it for them for an additional charge.

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

R isn’t just for statisticians hiding behind graphs — it’s a powerful tool for machine learning, and this course shows you exactly how to put it to work. Designed for curious minds with an interest in data prediction, modelling, and pattern recognition, this course guides you through the essentials of machine learning using R. You’ll explore key techniques like classification, regression, and clustering, all explained in plain English (with just the right amount of code). If you’ve ever wondered how machines “learn” from data — without trying to make them smarter than your laptop needs to be — this course breaks it down with clear logic and no unnecessary flair. Study entirely online, at your own pace, with structured content focused on helping you understand how algorithms behave, why they do what they do, and how to make them behave a little better. Whether you're dipping your toes or deepening your skills, it’s all here, minus the fluff and drama. Learning Outcomes: Understand the basics of machine learning and its implementation using R. Develop the skills to build simple and multiple linear regression models. Learn how to use R to analyse datasets and develop predictive models. Understand the concept of dummy variables and the backward elimination approach. Learn how to make accurate predictions using machine learning algorithms and extract valuable insights from data. If you're looking to expand your knowledge in data analysis and machine learning, then the "Learn Machine Learning with R" course is perfect for you. This comprehensive course comprises two sections, each designed to help you gain an in-depth understanding of machine learning concepts, starting from the very basics. You'll learn about linear regression, the equation for the algorithm, and how to make simple linear regression models. Additionally, you'll dive into multiple linear regression, dummy variable concepts, and predictions over the year. With the help of this course, you'll be able to analyse datasets, develop predictive models, and extract valuable insights from them, using R. ⊱⊱ Learn Machine Learning with R Course Curriculum Section 01: Linear Regression and Logistic Regression Working on Linear Regression Equation Making the Regression of the Algorithm Basic Types of Algorithms predicting the Salary of the Employee Making of Simple Linear Regression Model Plotting Training Set and Work Section 02: Understanding Dataset Multiple Linear Regression Dummy Variable Concept Predictions Over Year Difference Between Reference Elimination Working of the Model Working on Another Dataset Backward Elimination Approach Making of the Model with Full and Null 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? Students or professionals looking to develop their data analysis and machine learning skills. Individuals interested in pursuing a career in data science or machine learning. Anyone interested in understanding how to extract insights from data. Programmers looking to learn machine learning implementation using R. Beginners interested in learning the basics of machine learning. Career path Data analyst: £30,000 to £50,000 Machine learning engineer: £45,000 to £85,000 Data scientist: £40,000 to £80,000 Business analyst: £30,000 to £55,000 Research analyst: £25,000 to £45,000 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 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.