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Welcome to Calculus Level 1 - Learn Differentiation, the course that will turn you into a differentiation expert. This course is the perfect stepping-stone to ignite your passion for calculus and launch you into the world of mathematical complexities with ease. We've designed this Calculus Level 1 - Learn Differentiation course to be your comprehensive guide, taking you from basic rules to advanced techniques in the realm of differentiation. In the initial sections, you'll find a clear, understandable introduction to the field of calculus and the fundamental rules of differentiation. We will then be diving into the differentiation of trigonometric, exponential, and logarithmic functions. This will equip you with the tools to handle any type of function thrown your way. As we progress, the course will gently introduce the Chain Rule and strengthen your understanding of it, making complex calculations a breeze. The advanced sections venture into intriguing areas like the differentiation of inverse and hyperbolic trig functions, implicit functions, and parametric functions. Techniques like Logarithmic Differentiation and the understanding of higher order derivatives are broken down and explained in an easy-to-digest manner. You will not only have mastered the skill of differentiation after completing the Calculus Level 1 - Learn Differentiation course, but you will also have laid a solid basis for higher calculus. This course combines theory, problem-solving, and revision portions, making it ideal for people new to the field and those wishing to improve their knowledge. Join us on this mathematical adventure to discover the brilliance of calculus in a whole new light. Sign up now! Learning Outcomes: Upon completion of the Calculus Level 1 - Learn Differentiation course, you should be able to: Understand the basics and fundamental principles of differentiation. Differentiate trigonometric and exponential functions with ease. Master the application of the Chain Rule in differentiation. Execute differentiation of inverse and hyperbolic trig functions. Comprehend and apply differentiation to implicit functions. Gain proficiency in logarithmic differentiation. Derive and solve higher order derivative functions. Who is this course for? This course is perfect for: High school students seeking a firm grasp on calculus. Undergraduates looking to bolster their mathematics foundation. Professionals needing a refresher course in calculus. Aspiring mathematicians and engineers who use calculus extensively. Career Path: Upon completion of the Calculus Level 1 - Learn Differentiation course, you open up a world of opportunities. This foundational knowledge in calculus can lead you to a wide range of careers in fields such as engineering, physics, computer science, economics, and more. Further, it serves as a stepping-stone for advanced studies in mathematics, paving the path for academic and research roles. This course ensures you have the mathematical prowess required in today's data-driven world. Certification After studying the course materials of the Calculus Level 1 - Learn Differentiation there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Prerequisites This Calculus Level 1 - Learn Differentiation does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Calculus Level 1 - Learn Differentiation 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. Course Curriculum Section 01: Introduction Module 01: Promotional video 00:02:00 Module 02: Quick Guide 00:01:00 Section 02: Fundamental Rules for Differentiation Module 01: Power Rule 00:14:00 Module 02: Practice Problems Part 1 00:09:00 Module 03: Practice Problems Part 2 00:06:00 Module 04: Product Rule 00:13:00 Module 05: Quotient Rule 00:06:00 Module 06: Chain Rule For Differentiation 00:10:00 Section 03: Differentiation of Trigonometric Functions Module 01: Derivatives of Trigonometric functions 00:08:00 Module 02: Product Rule with Trigonometric Functions 00:11:00 Module 03: Quotient Rule with Trigonometric Functions 00:13:00 Module 04: Chain Rule with Trigonometric Functions Part 1 00:11:00 Module 05: Chain Rule with Trigonometric Functions Part 2 00:10:00 Module 06: Chain Rule with Trigonometric Functions Part 3 00:10:00 Section 04: Differentiation of Exponential Functions Module 01: Exponential Derivatives 00:13:00 Module 02: Chain Rule for Exponential Functions 00:14:00 Module 03: Derivatives of Exponential functions involving Trig Functions 00:12:00 Section 05: Differentiation of Logarithmic Functions Module 01: Derivatives of Logarithmic functions Part 1 00:09:00 Module 02: Derivatives of Logarithmic functions Part 2 00:10:00 Module 03: Derivatives of Logarithmic functions Part 3 00:06:00 Module 04: Problems involving Logarithmic and Trig functions part 1 00:09:00 Module 05: Problems involving Logarithmic and Trig functions part 2 00:07:00 Module 06: Problems involving Logarithmic and Trig functions part 3 00:05:00 Section 06: Revision Section 0on Chain Rule Module 01: Revision of Chain Rule Part 1 00:08:00 Module 02: Revision of Chain Rule Part 2 00:12:00 Module 03: Practice Problems Part 1 00:09:00 Module 04: Practice Problems Part 2 00:07:00 Section 07: Differentiation of inverse Trig Function Module 01: Derivatives of Inverse Trig Functions Part 1 00:09:00 Module 02: Derivatives of Inverse Trig Functions Part 2 00:11:00 Section 08: Differentiation of Hyperbolic Trig Functions Module 01: Derivatives of Hyperbolic Trig functions part 1 00:07:00 Module 02: Derivatives of Hyperbolic Trig functions part 2 00:07:00 Module 03: Derivatives of Inverse Hyperbolic Trig functions 00:09:00 Section 09: Differentiation of Implicit functions Module 01: Differentiation of Implicit functions Part 1 00:11:00 Module 02: Differentiation of Implicit functions Part 2 00:06:00 Module 03: Differentiation of Implicit functions involving Trig functions-1 00:16:00 Module 04: Differentiation of Implicit functions involving Trig functions-2 00:10:00 Section 10: Logarithmic Differentiation Module 01: Logarithmic Differentiation Part 1 00:13:00 Module 02: Logarithmic Differentiation Part 2 00:07:00 Module 03: Logarithmic Differentiation Part 3 00:13:00 Module 04: Logarithmic Differentiation Part 4 00:08:00 Module 05: Logarithmic Differentiation Part 5 00:10:00 Module 06: Logarithmic Differentiation Part 6 00:09:00 Module 07: Logarithmic Differentiation Part 7 00:11:00 Section 11: Differentiation of Parametric Functions Module 01: Differentiation of Parametric Functions Part 1 00:12:00 Module 02: Differentiation of Parametric Functions Part 2 00:09:00 Module 03: Differentiation of a function w. r. t. another function Part 1 00:11:00 Module 04: Differentiation of a function w. r. t. another function Part 2 00:05:00 Section 12: Differentiation of Higher order derivatives Module 01: Higher order derivatives Part 1 00:09:00 Module 02: Higher order derivatives Part 2 00:04:00 Module 03: Higher order derivatives Part 3 00:10:00 Module 04: Higher order derivatives Part 4 00:09:00 Module 05: Higher Order Derivatives involving Trig Functions 00:06:00 Module 06: Second order derivatives with Parametric functions 00:13:00

CPD Accredited. This online course covers how to accurately record a 12 Lead ECG Recording and is suitable for any staff working in a healthcare environment who are required to undertake this skill as part of their role. This course acts as an ideal refresher if you have previously undertaken ECGs within your role and want to boost your confidence, or for staff who are brand new to this skill. The course will cover the basic anatomy and physiology of the heart, the conduction system and accurate placement of the leads, to allow you to practice this skill in your own time with the equipment in your environment.

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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

CPD Accredited. This online course covers how to interpret basic 12 lead ECG rhythms, ranging from Sinus Rhythm to ST elevation infarcts. It is suitable for beginners upwards as it covers an in depth 6 stage approach on how to interpret each rhythm individually and accurately. This course acts as an ideal course for those who are new to interpreting 12 lead ECGs and for those who require a refresher in order to boost their confidence and knowledge in this specialist area. The course will cover basic anatomy and physiology of the heart, along with the cardiac cycle and conductivity of the heart in order to give the background knowledge to move on to rhythm strips. The course will lead you through a visual pathway of an array of rhythms with self-test points throughout to enable you to complete the course with new found knowledge and confidence. ECG’s General Terms and Conditions, can be viewed here: https://ecgtraining.co.uk/about-us/policies/

Keep your drivers informed, compliant, and confident behind the wheel. Toolbox Talks are designed to improve driver knowledge, reduce risks, and support ongoing transport compliance. Each course tackles real-world challenges with clear, practical guidance drivers can apply immediately. 📲 24/7 online access for 3 months – start anytime, on any device!

This course introduces you to the paradigm and features of object-oriented programming using Java, an object-oriented language popular in the industry and IntelliJ. The course entails modeling objects and classes, object-oriented facilities like inheritance and polymorphism, native data structures, exception handling, file management, and streams.

How to Select and Apply the Right Leadership Style for Every Situation