427 Algorithm courses delivered Online

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.

Duration 2 Days 12 CPD hours This course is intended for This intermediate course is for Business and Technical Specialist working with the Matching, Linking, and Search services of InfoSphere MDM Virtual module. Overview Understand how Matching and Linking work for both the Virtual Implementations of InfoSphere MDM Understand the MDM configuration project and database tables used by the PME Understand the PME Algorithms (Standardization, Bucketing and Comparison steps) and how to create and customize the algorithms using the workbench Understand how to analyze the Bucketing steps in an algorithm Understand how to generate weights for a given algorithm and how those weights are generated based on a sample database set Understand how to analyze the weights that are generated using the workbench Understand how to deploy the PME configuration for the Virtual implementations of InfoSphere MDM The InfoSphere MDM Virtual Module Algorithms V.11 course prepares students to work with and customize the algorithm configurations deployed to the InfoSphere MDM Probabilistic Matching Engine (PME) for Virtual MDM implementations. PME and Virtual Overview Virtual MDM Overview Terminology (Source, Entity, Member, Attributes) PME and Virtual MDM ( Algorithms, Weights, Comparison Scores, Thresholds) Virtual MDM Linkages and Tasks Virtual MDM Algorithms Standardization Bucketing Comparison Functions Virtual PME Data Model Algorithm configuration tables Member Derived Data Bucketing Data Bucket Analysis Analysis Overview Attribute Completeness Bucket Analysis Weights Weights Overview (Frequency-based weights, Edit Distance weights and Parameterize weights) The weight formula Running weight generation Analyzing weights Bulk Cross Match process Pair Manager Threshold calculations Additional course details: Nexus Humans ZZ880 IBM Virtual Module Algorithms for InfoSphere MDM V11 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 ZZ880 IBM Virtual Module Algorithms for InfoSphere MDM V11 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.

This course is intended for beginner-level individuals who are fascinated about quantum computing and want to learn more about it. It uses Jupyter notebook and IBM Qiskit tool to execute your learning into the actual computation.

Essential SDN training course description Software Defined Networking (SDN) has become one of the industries most talked technologies. This training course cuts through the hype and looks at the technology, architecture and products available for SDN along with looking at the impact it may have on your network. What will you learn Explain how SDN works. Describe the architecture of SDN. Explain the relationship between SDN and OpenFlow. Recognise the impact SDN will have on existing networks. Essential SDN training course details Who will benefit: Anyone wishing to know more about SDN. Prerequisites: None. Duration 2 days Essential SDN training course contents Introduction What is SDN? What is OpenFlow? SDN benefits. The SDN stack and architecture. SDN architecture SDN applications, SDN switches, SDN controllers, Network Operating Systems. Control plane, data plane. Control to Data Plane Interface (CDPI), Northbound interfaces. SDN components, control and data plane abstractions. Network Operating Systems Finding the topology, Global view, control program, configuration based on views, graph algorithm. OpenFlow Just one part of SDN. Open Networking Foundation, OpenFlow ports, Flow tables, OpenFlow Channels. The OpenFlow protocol, OpenFlow header, OpenFlow operations. OpenFlow versus OpFlex. SDN and open source OpenDaylight, OpenVSwitch, Open Networking Forum, Open Network Operating System. OpenStack Neutron. SDN implications Separation of control and data plane, NOS running on servers, Emphasis on edge complexity, core simplicity, OpenvSwitch, Incremental migration, importance of software. SDN vs NVF.

The Computer Science and Programming Diploma course covers the fundamental theories of Algorithm Analysis. If you want to explore the concepts and methods that make a good programmer, then the course is designed for you. Programming is all about how to solve a problem. Programming theory is not confined to a single language; rather it applies to all programming languages. By understanding the right programming theory, you will be able to analyse a problem and also able to find out the probable solution. The course teaches you these Programming theories covering Algorithm analysis, Binary Number System, Arrays and their Advantages, the process of analysing a problem, Nodes and their Importance, various sorting algorithms and their comparisons, and more. Upon completion, you will be able to understand the core theories of computer science. What Will I Learn? Understand the Fundamental Theories of Algorithm Analysis Be able to Compare Various Algorithms Understand When to use Different Data Structures and Algorithms Understand the Fundamentals of Computer Science theory Requirements A Willingness to Learn New Topics! No Prior Experience or Knowledge is Needed! Module: 01 Kurt Anderson - 1 Introduction FREE 00:01:00 Kurt Anderson - 2 Binary System FREE 00:11:00 Kurt Anderson - 3 Complexity Introduction 00:02:00 Kurt Anderson - 4 Math Refresher Logarithmic Functions 00:11:00 Kurt Anderson - 5 Math Refresher Factorial Functions.TS 007 00:03:00 Kurt Anderson - 6 Math Refresher Algebraic Expressions.TS 00:03:00 Kurt Anderson - 7 n-notation 00:19:00 Kurt Anderson - 8 Big O 00:13:00 Kurt Anderson - 9 Big O Real World Example 00:10:00 Module: 02 Kurt Anderson - 10 How is Data Stored 00:09:00 Kurt Anderson - 11 Fixed Arrays 00:20:00 Kurt Anderson - 12 Circular Arrays 00:08:00 Kurt Anderson - 13 Dynamic Arrays 00:16:00 Kurt Anderson - 14 Array Review 00:08:00 Kurt Anderson - 15 Array Real World Examples 00:06:00 Kurt Anderson - 16 Linked List 00:12:00 Kurt Anderson - 16 Nodes 00:04:00 Kurt Anderson - 17 Linked List Run Times 00:15:00 Kurt Anderson - 18 Doubly Linked Lists 00:08:00 Kurt Anderson - 19 Tail Pointer 00:05:00 Module: 03 Kurt Anderson - 20 Linked List Real World Examples 00:03:00 Kurt Anderson - 20 Stack Example 00:11:00 Kurt Anderson - 21 Linked List Review 00:04:00 Kurt Anderson - 22 Stacks 00:10:00 Kurt Anderson - 23 Queues 00:09:00 Kurt Anderson - 24 Queue Examples 00:10:00 Kurt Anderson - 25 Queue and Stack Run Times 00:06:00 Kurt Anderson - 26 Stack and Queues Real World Examples 00:07:00 Kurt Anderson - 27 Sorting Algorithm Introdcution 00:02:00 Kurt Anderson - 28 Bubble Sort 00:10:00 Kurt Anderson - 29 Selection Sort 00:10:00 Module: 04 Kurt Anderson - 30 Insertion Sort 00:09:00 Kurt Anderson - 31 Quick Sort 00:15:00 Kurt Anderson - 32 Quick Sort Run Times 00:10:00 Kurt Anderson - 33 Merge Sort 00:12:00 Kurt Anderson - 34 Merge Sort Run Times 00:08:00 Kurt Anderson - 35 Stable vs Nonstable 00:07:00 Kurt Anderson - 36 Sorting Algorithm Real World Examples 00:04:00 Kurt Anderson - 37 Basics of Trees 00:08:00 Kurt Anderson - 38 Binary Search Tree 00:09:00 Kurt Anderson - 39 BST Run Times 00:08:00 Module: 05 Kurt Anderson - 40 Tree Traversals 00:13:00 Kurt Anderson - 41 Tree Real World Examples 00:05:00 Kurt Anderson - 42 Heap Introduction 00:04:00 Kurt Anderson - 43 Heap Step by Step 00:12:00 Kurt Anderson - 44 Heap Real World Examples 00:07:00 Kurt Anderson - 45 Thank You 00:01:00

With this course, you will learn the bare-bone basics of C# by building console applications from scratch. You will first develop the application and then test it to gain a solid understanding of C# fundamentals. You will also explore the latest features released in C# 7.

This course takes you through all the important topics of data structure and algorithms from scratch. You will learn how to solve real-world problems with linked lists, stacks, queues, sorting algorithms, and a lot more using Python.

Building Data Science Products? Think Business First Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.Learning Objectives We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

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

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Throughout the programme, it stresses how to improve your competency as a person in your profession while at the same time it outlines essential career insights in this job sector. Consequently, you'll strengthen your knowledge and skills; on the other hand, see a clearer picture of your career growth in future. By the end of the Machine Learning, you can equip yourself with the essentials to keep you afloat into the competition. Along with this Machine Learning course, you will get 10 other premium courses. Also, you will get an original Hardcopy and PDF certificate for the title course and a student ID card absolutely free. This Bundle Consists of the following Premium courses: Course 01: Machine Learning with Python Course 02: Advanced Diploma in User Experience UI/UX Design Course 03: Data Science & Machine Learning with R Course 04: Python Programming for Everybody Course 05: Data Structures Complete Course Course 06: Data Science with Python Course 07: Computer Science: Graph Theory Algorithms Course 08: Higher Order Functions in Python - Level 03 Course 09: AWS Essentials Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002) Course 11: Introduction to Data Analysis So, enrol now to advance your career! Benefits you'll get choosing Apex Learning for this Machine Learning: One payment, but lifetime access to 11 CPD courses Certificate, student ID for the title course included in a one-time fee Full tutor support available from Monday to Friday Free up your time - don't waste time and money travelling for classes Accessible, informative modules taught by expert instructors Learn at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £10 * 11 = £110) Hard Copy Certificate: Free (For The Title Course) If you want to get hardcopy certificates for other courses, generally you have to pay £20 for each. But this Fall, Apex Learning is offering a Flat 50% discount on hard copy certificates, and you can get each for just £10! P.S. The delivery charge inside the U.K. is £3.99 and the international students have to pay £9.99. Curriculum of the Bundle Course 01: Machine Learning with Python Module 01: Introduction to Algorithms Module 02: Preprocessing Module 03: Regression Module 04: Classification Course 02: Data Science & Machine Learning with R Data Science and Machine Learning Course Intro Data Types and Structures in R Data Types and Structures in R Intermediate R Data Manipulation in R Data Visualization in R Creating Reports with R Markdown Building Webapps with R Shiny Introduction to Machine Learning Starting A Career in Data Science Course 03: Python Programming for Everybody Module 01 A Installing Python Documentation Command Line Variables Simple Python Syntax Keywords Import Module Module 02 Additional Topics If Elif Else Iterable For Loops Execute Exceptions Module 03 Data Types Number Types More Number Types Strings More Strings Files Lists Dictionaries Tuples Sets Module 04 Comprehensions Definitions Functions Default Arguments Doc Strings Variadic Functions Factorial Module 05 Function Objects Lambda Generators Closures Classes Object Initialization Class Static Members Classic Inheritance Data Hiding Course 04: Advanced Diploma in User Experience UI/UX Design UX/UI Course Introduction Introduction To The Web Industry Foundations of Graphic Design UX Design (User Experience Design) UI Design (User Interface Design) Optimization Starting a Career in UX/UI Design Course 05: Data Structures Complete Course Unit 01: Introduction Unit 02: Arrays Unit 03: Liked List Unit 04: Stack Unit 05: Queues Unit 06: Priority Queues (PQs) Unit 07: Union Find Unit 08: Binary Search Trees Unit 09: Fenwick Tree Unit 10: Hash Tables Unit 11: Suffix Array Unit 12: AVL Trees Unit 13: Indexed Priority Queue Unit 14: Sparse Tables Course 06: Data Science with Python Unit 01: Introduction to Python Data Science Unit 02: Data Cleaning Packages Unit 03: Data Visualization packages Course 07: Computer Science: Graph Theory Algorithms Module 00: Promo Module 01: Introduction Module 02: Common Problem Module 03: Depth First Search Module 04: Breadth First Search Module 05: Breadth First Search Shortest Path on a Grid Module 06: Trees Module 07: Topological Sort Module 08: Dijkstra Module 09: Bellman-Ford Algorithm Module 10: Floyd-Warshall Algorithm Module 11: Bridge and Algorithm Points Module 12: Tarjan Algorithm Module 13: Travelling Salesman Problem (TSP) Module 14: Eulerian Paths and Circuits Module 15: Prim's Minimum Spanning Tree Algorithm Module 16: Network Flow Course 08: Higher Order Functions in Python - Level 03 Module 01: Course Introduction Module 02: Simple Higher Order Functions Module 03: Sorting with Keys Module 04: Map Function Module 05: Filter Function Module 06: List Comprehension Alternative Module 07: Recursion Introduction Course 09: AWS Essentials Section 01: AWS Foundations and Services Section 02: AWS Security and Costs Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002) Section 01: What You Need to Know Section 02: Introducing the Cloud Section 03: System Requirements for Cloud Deployments Section 04: Cloud Storage Section 05: Cloud Compute Section 06: Cloud Networking Section 07: Cloud Security Section 08: Migrating to the Cloud Section 09: Maintaining Cloud Solutions Section 10: Troubleshooting Cloud Solutions Course 11: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding CPD 125 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Machine Learning bundle. Persons with similar professions can also refresh or strengthen their skills by enrolling in this course. Students can take this course to gather professional knowledge besides their study or for the future. Requirements Our Machine Learning is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various expertise will increase the value in your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included