329 Algorithms courses

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

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

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

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

In this course, you will learn how to author machine learning models in Python without the aid of frameworks or libraries from scratch. Discover the process of loading data, evaluating models, and implementing machine learning algorithms.

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Are you familiar with JavaScript and programming, perhaps considering a coding interview soon, but looking to train, sharpen, and master your JavaScript skills? Are you looking to improve your data structures and algorithms with an anthology of some of the most important practice examples and a journey that can smoothly get you there? Then this course is specifically designed for you!

UMTS training course description An in-depth study of the UMTS technologies and network structure. What will you learn Explain what 3G and UMTS are. Describe the migration path to UMTS. Describe the UMTS architecture. UMTS training course details Who will benefit: Anyone who needs to know more about UMTS. Prerequisites: Total GSM Duration 3 days UMTS training course contents Introduction 3G WCDMA 2G WCDMA comparison. European and international spectrum allocations. UMTS Services UMTS QoS classes, Conversational, Streaming, Interactive and Background. UMTS bearer services. Radio Access Network (RAN) Architecture System architecture. The Radio Network Controller (RNC) and Node B functionality. Protocol model. The Iu interface for Circuit and Packet switching. The Iur interface and RNSAP. RNC node B interface and NBAP. The Physical layer Spread spectrum coding and modulation. Logical and physical channels. User data rates and transmission. Power control. Signalling, synchronisation, common control, access and indicator channels. Procedures for transmit diversity, measurement, power control and handover. Radio Resource Management Fast and outer loop power control. Transmit power and power rise. Handover algorithms. Intra frequency and inter system handovers. Load, measurement on the air interface. Admission and load control. Packet Access Packet data traffic. Packet data transport channels. Packet scheduling algorithms. Handover, load and administration control. Packet data performance. UTRA TDD Mode Time Division Duplex (TDD). UTRA TDD modulation and spreading, transport channels, physical channels and their structure. Noise and interference limited network. Interference, FDD and TDD co-existence.

Covering ARM systems design and architecture and practical assembly programming, this is a comprehensive ARM assembly video course to get you up and running. You'll develop the skills necessary for starting your career as an ARM embedded developer, such as developing algorithms and creating state machines in assembly.

“Quantum Computing for Finance” is a 2-course online program designed for learners to realize the true potential of quantum technologies and its use-cases in Finance. The first course “Introduction to Quantitative and Computational Finance” will develop fundamental concepts required for an understanding of quantum algorithms and more advanced topics in computational finance. This course will teach you the basics of derivative products, the Black-Scholes-Merton model for pricing vanilla derivatives, and how to compute the price of exotic options with a computer. The second course “Quantum Algorithms for Computational Finance” is an advanced course that will develop an understanding of quantum algorithms for their implementation on quantum computers and their applications in finance. This course covers several aspects of quantum programming for people already familiar with the basics of quantum programming and quantitative finance. Buy the program at a reduced price.