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Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This 'Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3' course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you'll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning. The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python's robust libraries. Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions. Learning Outcomes Understanding the foundations and functionalities of Python, focusing on its application in data science. Applying various Python libraries like NumPy and Pandas for effective data analysis. Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly. Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques. Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques. Exploring advanced topics, encompassing recommender systems and natural language processing with Python. Attaining the confidence to independently analyse complex data sets and translate them into actionable insights. Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/Data-Science-and-Visualisation-with-Machine-Learning.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for? Aspiring data scientists aiming to harness the power of Python. Researchers keen to enrich their analytical and visualisation skills. Analysts aiming to add machine learning to their toolkit. Developers striving to integrate data analytics into applications. Business professionals desiring data-driven decision-making capabilities. Career path Data Scientist: £55,000 - £85,000 Per Annum Machine Learning Engineer: £60,000 - £90,000 Per Annum Data Analyst: £30,000 - £50,000 Per Annum Data Visualisation Specialist: £45,000 - £70,000 Per Annum Natural Language Processing Specialist: £65,000 - £95,000 Per Annum Business Intelligence Developer: £40,000 - £65,000 Per Annum Prerequisites This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 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. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Visualisation with Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00
Overview This comprehensive course on Electric Circuits for Electrical Engineering will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Electric Circuits for Electrical Engineering 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 Electric Circuits for Electrical Engineering. It is available to all students, of all academic backgrounds. Requirements Our Electric Circuits for Electrical Engineering 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 7 sections • 135 lectures • 10:29:00 total length •Module 1- What Is an Electric Circuit: 00:02:00 •Module 2-System of Units: 00:07:00 •Module 3- What Is an Electric Charge: 00:05:00 •Module 4- What Is an Electric Current: 00:08:00 •Module 5-Example 1: 00:09:00 •Module 6- Example 2: 00:02:00 •Module 7- Example 3: 00:13:00 •Module 8- What Is Voltage: 00:07:00 •Module 9- What Is Power: 00:06:00 •Module 10- What Is Energy: 00:04:00 •Module 11- Example 4: 00:03:00 •Module 12- Example 5: 00:02:00 •Module 13- Dependent and Independent Sources: 00:05:00 •Module 14- Example 6 Part 1: 00:04:00 •Module 15- Example 6 Part 2: 00:01:00 •Module 16- Application 1 Cathode Ray Tube: 00:04:00 •Module 17-Example 7: 00:04:00 •Module 18- Application 2 Electricity Bills: 00:02:00 •Module 19- Example 8: 00:03:00 •Module 1- Introduction to Basic Laws: 00:01:00 •Module 2- Definition of Resistance: 00:06:00 •Module 3- Ohm's Law: 00:02:00 •Module 4- Types of Resistances: 00:06:00 •Module 5- Open and Short Circuit: 00:05:00 •Module 6- Definition of Conductance: 00:04:00 •Module 7-Example 1: 00:01:00 •Module 8-Example 2: 00:01:00 •Module 9- Example 3: 00:03:00 •Module 10- Branch, Node and Loops: 00:07:00 •Module 11- Series and Parallel Connection: 00:04:00 •Module 12- KCL: 00:04:00 •Module 13- KVL: 00:03:00 •Module 14- Example 4: 00:05:00 •Module 15- Example 5: 00:02:00 •Module 16- Example 6: 00:06:00 •Module 17- Series Resistors and Voltage Division: 00:07:00 •Module 18-Parallel Resistors and Current Division: 00:12:00 •Module 19- Analogy between Resistance and Conductance: 00:07:00 •Module 20-Example 7: 00:03:00 •Module 21-Example 8: 00:04:00 •Module 22- Introduction to Delta-Wye Connection: 00:06:00 •Module 23-Delta to Wye Transformation: 00:05:00 •Module 24- Wye to Delta Transformation: 00:07:00 •Module 25-Example 9: 00:03:00 •Module 26- Example 10: 00:15:00 •Module 27- Application Lighting Bulbs: 00:03:00 •Module 28-Example 11: 00:05:00 •Module 1- Introduction to Methods of Analysis: 00:02:00 •Module 2- Nodal Analysis with No Voltage Source: 00:15:00 •Module 3-Example 1: 00:04:00 •Module 4-Cramer's Method: 00:04:00 •Module 5-Nodal Analysis with Voltage Source: 00:07:00 •Module 6- Example 2: 00:02:00 •Module 7- Example 3: 00:13:00 •Module 8-Mesh Analysis with No Current Source: 00:10:00 •Module 9-Example 4: 00:04:00 •Module 10- Example 5: 00:06:00 •Module 11-Mesh Analysis with Current Source: 00:07:00 •Module 12-Example 6: 00:08:00 •Module 13-Nodal Vs Mesh Analysis: 00:04:00 •Module 14-Application DC Transistor: 00:04:00 •Module 15-Example 7: 00:04:00 •Module 1-Introduction to Circuit theorems: 00:02:00 •Module 2-Linearity of Circuit: 00:07:00 •Module 3-Example 1: 00:04:00 •Module 4-Superposition Theorem: 00:07:00 •Module 5- Example 2: 00:04:00 •Module 6-Example 3: 00:06:00 •Module 7-Source Transformation: 00:08:00 •Module 8-Example 4: 00:05:00 •Module 9-Example 5: 00:03:00 •Module 10-Thevenin Theorem: 00:10:00 •Module 11-Example 6: 00:06:00 •Module 12-Example 7: 00:05:00 •Module 13- Norton's Theorem: 00:05:00 •Module 14-Example 8: 00:04:00 •Module 15-Example 9: 00:05:00 •Module 16-Maximum Power Transfer: 00:05:00 •Module 17-Example 10: 00:03:00 •Module 18-Resistance Measurement: 00:05:00 •Module 19-Example 11: 00:01:00 •Module 20-Example 12: 00:04:00 •Module 21-Summary: 00:05:00 •Module 1-Introduction to Operational Amplifiers: 00:03:00 •Module 2-Construction of Operational Amplifiers: 00:07:00 •Module 3-Equivalent Circuit of non Ideal Op Amp: 00:10:00 •Module 4-Vo Vs Vd Relation Curve: 00:04:00 •Module 5-Example 1: 00:09:00 •Module 6-Ideal Op Amp: 00:07:00 •Module 7- Example 2: 00:04:00 •Module 8-Inverting Amplifier: 00:05:00 •Module 9-Example 3: 00:02:00 •Module 10-Example 4: 00:02:00 •Module 11-Non Inverting Amplifier: 00:08:00 •Module 12-Example 5: 00:03:00 •Module 13-Summing Amplifier: 00:05:00 •Module 14-Example 6: 00:02:00 •Module 15-Difference amplifier: 00:06:00 •Module 16-Example 7: 00:08:00 •Module 17-Cascaded Op Amp Circuits: 00:06:00 •Module 18-Example 8: 00:04:00 •Module 19-Application Digital to Analog Converter: 00:06:00 •Module 20-Example 9: 00:04:00 •Module 21-Instrumentation Amplifiers: 00:05:00 •Module 22-Example 10: 00:01:00 •Module 23-Summary: 00:04:00 •Module 1-Introduction to Capacitors and Inductors: 00:02:00 •Module 2-Capacitor: 00:06:00 •Module 3-Capacitance: 00:02:00 •Module 4-Voltage-Current Relation in Capacitor: 00:03:00 •Module 5-Energy Stored in Capacitor: 00:06:00 •Module 6-DC Voltage and Practical Capacitor: 00:02:00 •Module 7-Example 1: 00:01:00 •Module 8-Example 2: 00:01:00 •Module 9-Example 3: 00:02:00 •Module 10-Equivalent Capacitance of Parallel Capacitors: 00:02:00 •Module 11-Equivalent Capacitance of Series Capacitors: 00:03:00 •Module 12-Example 4: 00:02:00 •Module 13-Definition of Inductors: 00:06:00 •Module 14-Definition of Inductance: 00:03:00 •Module 15-Voltage-Current Relation in Inductor: 00:03:00 •Module 16-Power and Energy Stored in Inductor: 00:02:00 •Module 17-DC Source and Inductor: 00:04:00 •Module 18-Example 5: 00:02:00 •Module 19-Series Inductors: 00:03:00 •Module 20-Parallel Inductors: 00:04:00 •Module 21-Example 6: 00:01:00 •Module 22-Small Summary to 3 Basic Elements: 00:02:00 •Module 23-Example 7: 00:05:00 •Module 24-Application Integrator: 00:05:00 •Module 25-Example 8: 00:03:00 •Module 26-Application Differentiator: 00:02:00 •Module 27-Example 9: 00:06:00 •Module 28-Summary: 00:05:00 •Assignment - Reporting and Data: 00:00:00
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