124 Pandas courses delivered Online

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

This course covers Python for data science and machine learning in detail and is for a beginner in Python. You will also learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, challenges of bias, variance and overfitting, model evaluation techniques, model optimization using hyperparameter tuning, grid search cross-validation techniques, and more.

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Experienced tutors and mentors will be there for you whenever you need them, and solve all your queries through email and chat boxes. Benefits you'll get choosing Apex Learning for this Course: One payment, but lifetime access to 11 CPD courses Certificates, student ID for the title course included in a one-time fee Full tutor support available from Monday to Friday Free up your time - don't waste time and money travelling for classes Accessible, informative modules taught by expert instructors Learn at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills Gain valuable knowledge without leaving your home What other courses are included with this Course? Level 2 Microsoft Office Essentials Microsoft Teams Leadership & Management Diploma Working from Home Essentials Mental Health and Working from Home Online Meeting Management Effective Communication Skills Time Management Report Writing Emotional Intelligence and Human Behaviour Curriculum ***Data Science Course Bundle 2022*** Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview Set-up the Environment for the Course (lecture 1) Set-up the Environment for the Course (lecture 2) Two other options to setup environment Python Essentials Python data types Part 1 Python Data Types Part 2 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) Python Essentials Exercises Overview Python Essentials Exercises Solutions Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. NumPy arrays, built-in methods, array methods and attributes. Indexing, slicing, broadcasting & boolean masking Arithmetic Operations & Universal Functions Exercises Overview Exercises Solutions Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. Pandas Introduction Pandas Data Structures - Series Pandas Data Structures - DataFrame Handling Missing Data Data Wrangling - Combining, merging, joining Groupby Useful Methods and Operations Project 1 (Overview) Customer Purchases Data Project 1 (Solutions) Customer Purchases Data Project 2 (Overview) Chicago Payroll Data Project 2 (Solutions Part 1) Chicago Payroll Data Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach Matplotlib Essentials - Exercises Overview Matplotlib Essentials - Exercises Solutions Python for Data Visualization using Seaborn Introduction & Installation Distribution Plots Categorical Plots (Part 1) Categorical Plots (Part 2) Axis Grids Matrix Plots Regression Plots Controlling Figure Aesthetics Exercises Overview Exercise Solutions Python for Data Visualization using pandas Pandas Built-in Data Visualization Pandas Data Visualization Exercises Overview Panda Data Visualization Exercises Solutions Python for interactive & geographical plotting using Plotly and Cufflinks Interactive & Geographical Plotting (Part 1) Interactive & Geographical Plotting (Part 2) Interactive & Geographical Plotting Exercises (Overview) Interactive & Geographical Plotting Exercises (Solutions) Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types….. Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff Linear Regression Model - Hands-on (Part 1) Linear Regression Model Hands-on (Part 2) Good to know! How to save and load your trained Machine Learning Model! Linear Regression Model (Insurance Data Project Overview) Linear Regression Model (Insurance Data Project Solutions) Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. Logistic Regression Model - Hands-on (Part 1) Logistic Regression Model - Hands-on (Part 2) Logistic Regression Model - Hands-on (Part 3) Logistic Regression Model - Hands-on (Project Overview) Logistic Regression Model - Hands-on (Project Solutions) Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality …. K Nearest Neighbors - Hands-on K Nearest Neighbors (Project Overview) K Nearest Neighbors (Project Solutions) Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. Decision Tree and Random Forests - Hands-on (Part 1) Decision Tree and Random Forests (Project Overview) Decision Tree and Random Forests (Project Solutions) Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) Support Vector Machines - Hands-on (SVMs) Support Vector Machines (Project 1 Overview) Support Vector Machines (Project 1 Solutions) Support Vector Machines (Optional Project 2 - Overview) Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method ….. K Means Clustering - Hands-on K Means Clustering (Project Overview) K Means Clustering (Project Solutions) Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) Principal Component Analysis (PCA) - Hands-on Principal Component Analysis (PCA) - (Project Overview) Principal Component Analysis (PCA) - (Project Solutions) Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance Python for Recommender Systems - Hands-on (Part 1) Python for Recommender Systems - - Hands-on (Part 2) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) NLP-Challenges, Data Sources, Data Processing ….. Feature Engineering and Text Preprocessing in Natural Language Processing NLP - Tokenization, Text Normalization, Vectorization, BoW…. BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … Pipeline feature to assemble several steps for cross-validation… How will I get my Certificate? After successfully completing the 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 £10 * 11 = £110) Hard Copy Certificate: Free (For The Title Course) If you want to get hardcopy certificates for other courses, generally you have to pay £20 for each. But this Fall, Apex Learning is offering a Flat 50% discount on hard copy certificates, and you can get each for just £10! P.S. The delivery charge inside the U.K. is £3.99 and the international students have to pay £9.99. CPD 20 CPD hours / points Accredited by CPD Quality Standards Who is this course for? There is no experience or previous qualifications required for enrolment on this Data Science Course Bundle 2022. It is available to all students, of all academic backgrounds. Requirements Our Data Science Course Bundle 2022 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 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 Having this CPD certificate will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Certificates Certificate of completion Digital certificate - Included

This course will show you how to create a professional and attractive user interface (UI) in Django for data science using the Semantic UI framework.

Duration 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

***24 Hour Limited Time Flash Sale*** Data Science & Machine Learning, Excel Pivot & Machine Learning with Python Admission Gifts FREE PDF & Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter Immerse yourself in the world of Data Science, Machine Learning and Python with our exclusive bundle! Presenting eight thoughtfully curated courses, this bundle aims to enhance your understanding of intricate concepts. Within this collection, we proudly offer three QLS-endorsed courses: "2021 Data Science & Machine Learning with R from A-Z", "Excel Pivot Tables, Pivot Charts, Slicers, and Timelines", and "Machine Learning with Python", each complemented by a hardcopy certificate upon completion. Additionally, delve deeper with our five relevant CPD QS accredited courses. Explore Python Data Science with Numpy, Pandas, and Matplotlib. Uncover the secrets of R Programming for Data Science, enhance your statistical prowess with Statistics & Probability for Data Science & Machine Learning, and master spatial visualisation in Python. To top it all, there's a course on Google Data Studio for Data Analytics. Key Features of the Data Science & Machine Learning, Excel Pivot & Machine Learning with Python Bundle: 3 QLS-Endorsed Courses: We proudly offer 3 QLS-endorsed courses within our Data Science & Machine Learning, Excel Pivot & Machine Learning with Python bundle, providing you with industry-recognized qualifications. Plus, you'll receive a free hardcopy certificate for each of these courses. QLS Course 01: 2021 Data Science & Machine Learning with R from A-Z QLS Course 02: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines QLS Course 03: Machine Learning with Python 5 CPD QS Accredited Courses: Additionally, our bundle includes 5 relevant CPD QS accredited courses, ensuring that you stay up-to-date with the latest industry standards and practices. Course 01: Python Data Science with Numpy, Pandas and Matplotlib Course 02: R Programming for Data Science Course 03: Statistics & Probability for Data Science & Machine Learning Course 04: Spatial Data Visualisation and Machine Learning in Python Course 05: Google Data Studio: Data Analytics In Addition, you'll get Five Career Boosting Courses absolutely FREE with this Bundle. Course 01: Professional CV Writing Course 02: Job Search Skills Course 03: Self-Esteem & Confidence Building Course 04: Professional Diploma in Stress Management Course 05: Complete Communication Skills Master Class Convenient Online Learning: Our Data Science & Machine Learning, Excel Pivot & Machine Learning with Python courses are accessible online, allowing you to learn at your own pace and from the comfort of your own home. Learning Outcomes: Master the usage of Excel Pivot Tables, Pivot Charts, Slicers, and Timelines. Develop proficiency in Machine Learning using Python. Acquire skills to manipulate data using Numpy, Pandas, and Matplotlib. Learn to code in R for Data Science applications. Understand the application of Statistics & Probability in Data Science & Machine Learning. Learn to create impactful data visualisations and analyse data using Google Data Studio. The "Data Science & Machine Learning, Excel Pivot & Machine Learning with Python" bundle is a comprehensive compilation designed to equip you with the theoretical knowledge necessary for the fast-evolving data-driven world. The three QLS-endorsed courses provide foundational understanding in Data Science, Machine Learning with R, Excel Pivot functionalities, and Machine Learning with Python, thereby setting a strong base. Furthermore, the five CPD QS accredited courses offer a deeper dive into the world of Data Science. Whether it is harnessing Python's power for data science tasks, exploring R programming, mastering statistical techniques, understanding spatial data visualisation in Python, or learning to navigate Google Data Studio for Data Analytics, this bundle has you covered. With this comprehensive learning experience, gain the theoretical insight needed to navigate and succeed in the dynamic field of data science. CPD 250 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Individuals interested in theoretical concepts of Data Science and Machine Learning. Professionals looking to enhance their knowledge in Excel Pivot Tables and Charts. Aspiring data scientists who want to learn Python and R programming for data science. Anyone seeking to understand data visualisation and analytics through Python and Google Data Studio. Career path Data Scientist: Leveraging data for actionable insights (£40,000 - £90,000 per annum). Machine Learning Engineer: Designing and implementing machine learning systems (£50,000 - £90,000 per annum). Excel Analyst: Using Excel for data analysis and visualisation (£30,000 - £60,000 per annum). Python Developer: Developing applications using Python (£40,000 - £80,000 per annum). Certificates Digital certificate Digital certificate - Included Hard copy certificate Hard copy certificate - Included

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview This skills-focused combines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Building Recommendation Systems with Python (TTAI2360) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Building Recommendation Systems with Python (TTAI2360) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Applied AI: Building Recommendation Systems with Python (TTAI2360) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Python Programming: Beginner To Expert Overview Unfold the potential within you, and embark on a journey of mastering Python programming - from the fundamental building blocks to the pinnacle of expertise. This comprehensive course, crafted with meticulous care, empowers you to transform from a curious novice to a confident coding maestro, wielding Python's power with finesse. Within these engaging modules, you'll delve into the core principles of Python, meticulously exploring data types, operators, control flow, and functions. As your proficiency blossoms, you'll conquer advanced topics like object-oriented programming, powerful libraries like NumPy and Pandas, and the art of crafting polished scripts. But this journey isn't merely about acquiring technical prowess; it's about unlocking a world of possibilities. By the course's end, you'll be equipped to embark on a rewarding career path, armed with the skills to tackle real-world challenges in diverse domains - from data analysis and web development to scientific computing and automation. Learning Outcomes Gain a solid foundation in Python syntax, data structures, and control flow mechanisms. Master essential functions, user input, and error-handling techniques. Explore advanced data types, object-oriented programming concepts, and popular libraries like NumPy and Pandas. Craft polished, reusable Python scripts for various applications. Confidently navigate the Python ecosystem and continuously expand your knowledge. Why You Should Choose Python Programming: Beginner To Expert Lifetime access to the course No hidden fees or exam charges CPD Accredited certification on successful completion Full Tutor support on weekdays (Monday - Friday) Efficient exam system, assessment and instant results Download Printable PDF certificate immediately after completion Obtain the original print copy of your certificate, dispatch the next working day for as little as £9. Improve your chance of gaining professional skills and better earning potential. Who is this Course for? Python Programming: Beginner To Expert is CPD certified and IAO accredited. This makes it perfect for anyone trying to learn potential professional skills. As there is no experience and qualification required for this course, it is available for all students from any academic backgrounds. Requirements Our Python Programming: Beginner To Expert is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. Career Path You will be ready to enter the relevant job market after completing this course. You will be able to gain necessary knowledge and skills required to succeed in this sector. All our Diplomas' are CPD and IAO accredited so you will be able to stand out in the crowd by adding our qualifications to your CV and Resume. Python Programming: Beginner To Expert Module 01: Introduction to Python Programming from A-Z Intro To Python Section Overview 00:06:00 What is Python Programming? 00:10:00 Who is This Course For? 00:05:00 Python Programming Marketplace 00:06:00 Python Job Opportunities 00:05:00 How To Land a Python Job Without a Degree 00:08:00 Python Programmer Job Roles 00:09:00 Python from A-Z Course Structure 00:04:00 Module 02: Getting Familiar with Python Getting Familiar with Python Section Overview 00:06:00 Installing Python on Windows 00:10:00 Anaconda and Jupyter Notebooks Part 1 00:08:00 Anaconda and Jupyter Notebooks Part 2 00:16:00 Comments 00:05:00 Python Syntax 00:02:00 Line Structure 00:03:00 Line Structure Exercise 00:07:00 Joining Lines 00:05:00 Multiple Statements on a Single Line 00:05:00 Indentation 00:08:00 Module 03: Basic Data Types Basic Data Types Section Overview 00:08:00 String Overview 00:10:00 String Manipulation 00:07:00 String Indexing 00:04:00 String Slicing 00:08:00 Printing 00:10:00 Python Variables 00:08:00 Integers and Floats 00:08:00 Booleans 00:02:00 Mini Project 1 : Letter Counter 00:20:00 Module 04: Python Operators Python Operators Section Overview 00:04:00 Comparison Operators 00:09:00 Arithmetic Operators 00:08:00 Assignment Operators 00:05:00 Logical Operators 00:13:00 Identity Operators 00:05:00 Membership Operators 00:02:00 Bitwise Operators 00:08:00 Module 05: Advanced Data Types Python Advanced Data Types Section Overview 00:11:00 Sets 00:06:00 List Overview 00:05:00 List Slicing and Indexing 00:04:00 Tuples 00:02:00 Dictionaries 00:11:00 When to use each one? 00:05:00 Compound Data Types 00:03:00 Module 06: Control Flow Part 1 Control Flow Part 1 Section Overview 00:15:00 Intro to Control Flow 00:01:00 Basic Conditional Statements 00:14:00 More Conditional Statements 00:05:00 For Loops 00:10:00 While Loops 00:12:00 Module 07: Control Flow Part 2 Control Flow Part 2 Section Overview 00:02:00 Break Statements 00:08:00 Continue Statements 00:05:00 Zip Function 00:07:00 Enumerate Function 00:04:00 List Comprehension 00:04:00 Module 08: Python Functions Python Functions Section Overview 00:03:00 Intro to Functions 00:02:00 Python help Function 00:03:00 Defining Functions 00:09:00 Variable Scope 00:08:00 Doc Strings 00:04:00 Module 09: User Input and Error Handling User Input and Error Handling Section Overview 00:02:00 Introduction to error handling 00:03:00 User Input 00:04:00 Syntax Errors 00:04:00 Exceptions 00:11:00 Handling Exceptions Part 1 00:08:00 Handling Exceptions Part 2 00:08:00 Module 10: Python Advanced Functions Python Advanced Functions Section Overview 00:05:00 Lambda Functions 00:05:00 Functions args and kwargs 00:10:00 Iterators 00:08:00 Generators and Yield 00:12:00 Map Function 00:14:00 Filter Function 00:08:00 Module 11: Python Scripting and Libraries Python Scripting and Libraries Section Overview 00:05:00 What is a script? 00:01:00 What is an IDE? 00:17:00 What is a text editor? 00:12:00 From Jupyter Notebook to VScode Part 1 00:15:00 From Jupyter Notebook to VScode Part 2 00:05:00 Importing Scripts 00:03:00 Standard Libraries 00:04:00 Third Party Libraries 00:06:00 Module 12: NumPy NumPy Section Overview 00:04:00 Why use NumPy? 00:04:00 NumPy Arrays 00:10:00 Reshaping, Accessing, and Modifying 00:07:00 Slicing and Copying 00:06:00 Inserting, Appending, and Deleting 00:10:00 Array Logical Indexing 00:04:00 Broadcasting 00:08:00 Module 13: Pandas Intro to Pandas 00:17:00 Pandas Series 00:17:00 Pandas Series Manipulation 00:17:00 Pandas DataFrame 00:17:00 Pandas DataFrame Manipulation 00:13:00 Dealing with Missing Values 00:10:00 Module 14: Introduction to OOP Functional vs OOP 00:06:00 OOP Key Definitions 00:04:00 Create your First Class 00:12:00 How to Create and Use Objects 00:06:00 How to Modify Attributes 00:12:00 Module 15: Advanced OOP Python Decorators 00:27:00 Property Decorator 00:09:00 Class Method Decorator 00:07:00 Static Methods 00:10:00 Inheritance from A to Z 00:21:00 Module 16: Starting a Career in Python Getting Started with Freelancing 00:09:00 Building A Brand 00:12:00 Personal Branding 00:13:00 Importance of Having Website/Blog 00:04:00 Do's and Don'ts of Networking 00:06:00 Creating A Python Developer Resume 00:06:00