329 Algorithms courses

Explore the intricate world of cyber threats with our course, 'Ransomware Uncovered: Cybersecurity Threats & Solutions.' Gain insights into the evolving threat landscape, the underground economy of cybercriminals, and the cyber kill chain. Understand the mechanics of ransomware, delivery channels, and the psychological tactics employed by criminals. Learn to make informed decisions on whether to pay ransoms. Develop robust defense strategies and incident response plans. Empower yourself in the fight against cyber threats. Enroll now for a comprehensive journey into ransomware defense and cybersecurity resilience.

This comprehensive course will help you learn the basics to advanced mechanisms of chatbot development using deep learning with Python. This course is a complete package for beginners to learn chatbot fundamentals with deep learning and its applications and build it from scratch using deep learning (RNNs) with Python.

This course primarily focuses on explaining the concepts of the Document Object Model through a project-based approach. It will help you enhance your coding skills using JavaScript along with a deeper understanding of the DOM fundamentals.

Enhance your forecasting skills with EnergyEdge's Advanced Load Forecasting Methodology course. Join our classroom training for expert insights.

Overview This comprehensive course on Statistical Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Statistical Analysis comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? At the end of the course there will be an online written test, which you can take either during or after the course. After successfully completing the test you will be able to order your certificate, these are included in the price. Who is This course for? There is no experience or previous qualifications required for enrolment on this Statistical Analysis. It is available to all students, of all academic backgrounds. Requirements Our Statistical Analysis 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 14 sections • 16 lectures • 06:28:00 total length •The Realm Of Statistics: 00:26:00 •Basic Statistical Terms: 00:41:00 •The Center of the Data: 00:07:00 •Data Variability: 00:15:00 •Binomial and Normal Distributions: 00:14:00 •Binomial Probabilities Table: 00:14:00 •Z-Table: 00:04:00 •Introduction to Probability: 00:35:00 •Estimates and Intervals: 00:34:00 •Hypothesis Testing: 00:31:00 •Regression Analysis: 00:11:00 •Algorithms, Analytics and Prediction: 00:47:00 •Learning From Experience: The Bayesian Way: 00:31:00 •Doing Statistics: The Wrong Way: 00:37:00 •How We Can Do Statistics Better: 00:41:00 •Assignment - Statistics Essentials: 00:00:00

Mechatronics is an interdisciplinary course that explores the integration of mechanical engineering, electrical engineering, and computer science to design and develop intelligent systems. You will learn how to design, model, and analyze mechatronic systems, as well as implement control algorithms to achieve desired functionalities. By the end of the Mechatronics course, students will have a comprehensive understanding of the synergistic relationship between mechanical and electronic components, enabling them to design and develop cutting-edge mechatronic solutions for various industries, including robotics, automation, and advanced manufacturing. Throughout the Mechatronics programme, it stresses how to improve your competency as a person in your chosen field while also outlining essential career insights in the relevant job sector. Along with this Mechatronics course, you will get 10 premium courses, an original hardcopy, 11 PDF certificates (Main Course + Additional Courses) Student ID card as gifts. This Mechatronics Bundle Consists of the following Premium courses: Course 01: Mechanical Engineering Course 02: Electrical Engineering DC Circuit Analysis Course 03: Energy Saving in Electric Motors Course 04: Basic Automotive Engineering: Onboard Diagnostics Course 05: Engine Lubricant System Training - Level 4 Course 06: Supercharger Automobile Engineering Course 07: Hybrid Vehicle Expert Training Course 08: Digital Electric Circuits & Intelligent Electrical Devices Course 09: Car Restoration Course 10: PUWER Course 11: Electrical and Fire Safety Training - Level 2 Key features of this Mechatronics course: This Mechatronics bundle is CPD QS Accredited Learn from anywhere in the world Entirely online Lifetime access So, enrol Mechatronics now to advance your career! The Mechatronics bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Mechatronics expertise and essential knowledge, which will assist you in reaching your goal. Moreover, you can learn from any place in your own time without travelling for classes. Curriculum of the Mechatronics Bundle: Course 01: Mechanical Engineering Module 01: Introduction Module 02: Engineering Mechanics - I Module 03: Engineering Mechanics - II Module 04: Heat and Thermodynamics Module 05: Work, Force and Energy Module 06: Fluid Mechanics Module 07: Structural Mechanics Module 08: Machines - I Module 09: Machines - II Module 10: Machine Dynamics Module 11: Mechanical Design Module 12: Hydraulic Machines Module 13: Strain Hardening Processes Module 14: Application of Solidification Module 15: Welding Module 16: Engineering Drawing ----------- 10 more Mechatronics courses--------- How will I get my Mechatronics Certificates? After successfully completing the Mechatronics 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 £6*11 = £66) Hard Copy Certificate: Free (For The Title Course: Previously it was £10) So, enrol Mechatronics now to advance your career! CPD 110 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Mechatronics bundle. Requirements This Mechatronics course has been designed to be fully compatible with tablets and smartphones. Career path **Mechatronics** Upon completion of Mechatronics course, various career opportunities include: Graduate Mechanical Engineer: £24,000 - £30,000 Mechanical Design Engineer: £32,000 - £45,000 Mechanical Systems Engineer: £38,000 - £55,000 Senior Mechanical Engineer: £45,000 - £65,000 So, enrol Mechatronics now to advance your career! Certificates Certificate of completion Digital certificate - Included Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Internal Combustion Engine Basics) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Enhance your coding skill set by learning Java multithreading and parallel programming. Figure out common problems such as debugging codes, splitting a problem into multiple independent pieces, and measuring the runtime and performance of your code. Save time and avoid going into the same pitfalls while learning multithreading.

Description Fundamentals of Mechatronics Diploma The Fundamentals of Mechatronics Diploma is a comprehensive online course tailored for those looking to gain in-depth knowledge about the intersection of mechanical engineering, electronics, computer intelligence, and control engineering. As the world continues its rapid advance towards automation and sophisticated systems integration, mechatronics stands as one of the forefronts of this revolution. This diploma not only provides a solid foundation in mechatronics but also equips learners with the practical skills required to design and develop advanced mechatronic systems. Starting with an Introduction to Mechatronics, the course paves the way for learners by outlining the core concepts and principles that underpin this multidisciplinary field. As you progress, you'll explore the Elements and Components of Mechatronic Systems, understanding the intricacies of the systems that form the backbone of modern automation and robotics. A significant part of the Fundamentals of Mechatronics Diploma is dedicated to the Modeling and Simulation of Mechatronic Systems. This module aids students in visualising and predicting how different systems will interact and behave. This is crucial for anyone looking to design or troubleshoot complex mechatronic systems. Interfacing and Signal Conditioning for Mechatronics follow, highlighting the importance of seamless communication between various mechatronic components. Here, you'll learn how to ensure the proper transmission and reception of signals, ensuring optimal system performance. Errors can be costly, especially in precision-driven mechatronic systems. That's why the course covers Error Analysis and Instrumentation for Mechatronics, giving you the tools and knowledge to spot inaccuracies and calibrate systems accordingly. Next up, the Sensors and Actuators in Mechatronics section dives into the components that serve as the eyes, ears, and limbs of these systems. Grasp how these elements detect environmental changes and respond accordingly, playing a pivotal role in the system's overall functionality. Every mechatronic system relies on some form of digital backbone. With the Digital Hardware and Microcontrollers in Mechatronics section, understand the role of these digital brains in controlling and overseeing the system's operations. As the course progresses to Control Systems for Mechatronics, students learn about the algorithms and mechanisms that keep these complex systems in check. This understanding ensures mechatronic designs function as intended, with precision and reliability. The Mechatronic System Design and Development segment ties everything together, allowing learners to apply their accumulated knowledge in real-world scenarios, crafting systems that are both innovative and functional. To wrap it all up, the Emerging Trends in Mechatronics module offers a glimpse into the future. Stay abreast of the latest developments and innovations, ensuring you remain a relevant and informed professional in the field. Enrolling in the Fundamentals of Mechatronics Diploma is a decision to embrace the future of engineering and technology. Designed with clarity and depth, this online course offers a blend of theoretical knowledge and practical insights, setting the stage for a promising career in mechatronics. Whether you're a budding engineer, a professional looking to upscale, or simply someone intrigued by the melding of mechanical and electronic realms, this diploma stands as your comprehensive guide. Join today and embark on a journey of discovery in the dynamic world of mechatronics. What you will learn 1:Introduction to Mechatronics 2:Elements and Components of Mechatronic Systems 3:Modeling and Simulation of Mechatronic Systems 4:Interfacing and Signal Conditioning for Mechatronics 5:Error Analysis and Instrumentation for Mechatronics 6:Sensors and Actuators in Mechatronics 7:Digital Hardware and Microcontrollers in Mechatronics 8:Control Systems for Mechatronics 9:Mechatronic System Design and Development 10:Emerging Trends in Mechatronics 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.

Level 5 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. 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? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 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: 00:08:00 •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: 00:07:00 •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