338 Algorithm courses in Manchester delivered On Demand

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Demystify the power of graphs and unlock their potential with our Graph Theory Algorithms Course. Master fundamental algorithms like Depth-First Search and Breadth-First Search, explore pathfinding techniques, and delve into advanced graph concepts. Enrol now to sharpen your skills and become proficient in graph theory.

Make your code and programs faster and more efficient by using algorithms

This video course takes you through the basic and advanced JavaScript methods, enabling you to understand and implement them in a correct way. The course is filled with tips and tricks that will help you tackle tough interview questions to get a job.

What Will I Learn? Choose a perfect automated trading strategy Build a portfolio consisting of trading algorithms Learn what criteria a trading algorithm should match Learn when to exclude a strategy from portfolio Learn advantages of using trading algorithms Learn where to find automated trading strategies Understand why it's better to trust an algorithm than a human Learn what markets to trade using trading algorithms Requirements No programming skills required Just an open mind Basic Excel Description Updated in June'16! Learn how to make 95% of profit in just 14 months! Learn how to choose automated trading strategies and force them to earn money for you! In this course, I will show you several platforms where you can find a variety of trading robots. But, more importantly, I will give you a list of criteria to choose perfect strategies and teach you how to select them in practice. I will explain what to take into account when you have to choose between two similar strategies. You will also get a basic knowledge of trading algorithms, their advantages as compared to manual trading, which markets it's better to trade using automated trading strategies. I believe by the end of this course you will be able to build your own portfolio and make profits. Take this course now and learn from my 12+ years of experience. This course is for beginners as well as for advanced traders and algorithm developers! All you need is just your aspiration to learn! With this course you also get: unlimited lifetime access at no extra costs all future additional lectures, live trading examples never any questions asked full 30-day money-back-in-full guarantee Do not hesitate to ask me any questions concerning this course or trading financial markets! Viktor Who is the target audience? Anyone interested in trading Financial Markets Professional traders and algorithm developers Anyone who wants to create a passive income on Financial Markets, but has no time to spare how to trade Forex, stocks, options and especially futures traders Those, who want to trade algorithms, but don't know where to start And finally, anyone who has bad experience in trading Introduction What this course is about FREE 00:02:00 What do you need to start this course FREE 00:02:00 Live Results 00:03:00 Performance Update (November'15) 00:04:00 Performance Update (January'16) 00:05:00 Performance update (June'16) 00:06:00 Performance Update (October'16) 00:06:00 What instruments do you prefer to invest? What are Financial Markets? 00:06:00 What is algorithmic trading? 00:03:00 Risks from using trading algorithms 00:05:00 What markets to trade (Futures against Forex) 00:06:00 The external side of the robot What criteria to use to choose a strategy 00:09:00 How to create a portfolio 00:02:00 How to exclude a strategy from portfolio 00:03:00 Where to invest 00:05:00 iSystems in details 00:09:00 Learn from experience What strategies to use. Creating a portfolio (part 1) 00:08:00 What strategies to use. Creating a portfolio (part 2) 00:07:00

This course covers essential topics required for working with data structures and algorithms using JavaScript. From basics of data structures and algorithms to key concepts, such as arrays, lists, Big O time-space asymptomatic analysis, trees, and maps, this course will teach you everything with the help of engaging examples and activities.

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.

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.

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.