250 Matrix courses in Leicester delivered Online

This five-day programme empowers participants with the skills and knowledge to understand and effectively apply best practice commercial and contracting principles and techniques, ensuring better contractor performance and greater value add. This is an assessed programme, leading to the International Association for Contracts & Commercial Management (IACCM)'s coveted Contract and Commercial Management Practitioner (CCMP) qualification. By the end of this comprehensive programme the participants will be able to: Develop robust contracting plans, including scopes of work and award strategies Undertake early market engagements to maximise competition Conduct effective contracting and commercial management activities, including ITT, RFP, negotiated outcomes Understand the legalities of contract and commercial management Negotiate effectively with key stakeholders and clients, making use of the key skills of persuading and influencing to optimise outcomes Undertake effective Supplier Relationship Management Appreciate the implications of national and organisational culture on contracting and commercial activities Appreciate professional contract management standards Set up and maintain contract and commercial management governance systems Take a proactive, collaborative, and agile approach to managing commercial contracts Develop and monitor appropriate and robust KPIs and SLAs to manage the contractor and facilitate improved contractor performance Appreciate the cross-functional nature of contract management Collaborate with clients to deliver sustainable performance and to manage and exceed client expectations Understand the roles and responsibilities of contract and commercial managers Use effective contractor selection and award methods and models (including the 10Cs model) and use these models to prepare robust propositions to clients Make effective use of lessons learned to promote improvements from less than optimal outcomes, using appropriate templates Effectively manage the process of change, claims, variations, and dispute resolution Develop and present robust propositions Make appropriate use of best practice contract and commercial management tools, techniques, and templates DAY ONE 1 Introduction Aims Objectives KPIs Learning strategies Plan for the programme 2 The contracting context Key objectives of contract management Importance and impact on the business 3 Critical success factors Essential features of professional commercial and contract management and administration The 6-step model 4 Putting the 'management' into commercial and contract management Traditional v 'new age' models The need for a commercial approach The added value generated 5 Definitions 'Commercial management' 'Contract management' 'Contracting' ... and why have formal contracts? 6 Stakeholders Stakeholder mapping and analysis The 'shared vision' concept Engaging with key functions, eg, HSE, finance, operations 7 Roles and responsibilities Contract administrators Stakeholders 8 Strategy and planning Developing effective contracting plans and strategies DAY TWO 1 Contract control Tools and techniques, including CPA and Gantt charts A project management approach Developing effective contract programmes 2 The contracting context Key objectives of contract management Importance and impact on the business 3 Tendering Overview of the contracting cycle Requirement to tender Methods Rationale Exceptions Steps Gateways Controls One and two package bids 4 Tender assessment and contract award I - framework Tender board procedures Role of the tender board (including minor and major tender boards) Membership Administration Developing robust contract award strategies and presentations DAY THREE 1 Tender assessment and contract award II - processes Pre-qualification processes CRS Vendor registration rules and processes Creating bidder lists Disqualification criteria Short-listing Using the 10Cs model Contract award and contract execution processes 2 Minor works orders Process Need for competition Role and purpose Controls Risks 3 Contract strategy Types of contract Call-offs Framework agreements Price agreements Supply agreements 4 Contract terms I: Pricing structures Lump sum Unit price Cost plus Time and materials Alternative methods Target cost Gain share contracts Advance payments Price escalation clauses 5 Contract terms II: Other financial clauses Insurance Currencies Parent body guarantees Tender bonds Performance bonds Retentions Sub-contracting Termination Invoicing 6 Contract terms III: Risk and reward Incentive contracts Management and mitigation of contractual risk DAY FOUR 1 Contract terms IV: Jurisdiction and related matters Applicable laws and regulations Registration Commercial registry Commercial agencies 2 Managing the client-contractor relationship Types of relationship Driving forces Link between type of contract and style of relationships Motivation - use of incentives and remedies 3 Disputes Types of dispute Conflict resolution strategies Negotiation Mediation Arbitration DAY FIVE 1 Performance measurement KPIs Benchmarking Cost controls Validity of savings Balanced scorecards Using the KPI template 2 Personal qualities of the contract manager Negotiation Communication Persuasion and influencing Working in a matrix environment 3 Contract terms V: Drafting skills Drafting special terms 4 Variations Contract and works variation orders Causes of variations Risk management Controls Prevention Negotiation with contractors 5 Claims Claims management processes Controls Risk mitigation Schedules of rates 6 Close-out Contract close-out and acceptance / completion HSE Final payments Performance evaluation Capturing the learning 7 Close Review Final assessment Next steps

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.

Overview This Marketing Principles course will unlock your full potential and will show you how to excel in a career in Marketing Principles. So upskill now and reach your full potential. Everything you need to get started in Marketing Principles is available in this course. Learning and progressing are the hallmarks of personal development. This Marketing Principles will quickly teach you the must-have skills needed to start in the relevant industry. In This Marketing Principles Course, You Will: Learn strategies to boost your workplace efficiency. Hone your Marketing Principles skills to help you advance your career. Acquire a comprehensive understanding of various Marketing Principles topics and tips from industry experts. Learn in-demand Marketing Principles skills that are in high demand among UK employers, which will help you to kickstart your career. This Marketing Principles course covers everything you must know to stand against the tough competition in the Marketing Principles field.  The future is truly yours to seize with this Marketing Principles. Enrol today and complete the course to achieve a Marketing Principles certificate that can change your professional career forever. Additional Perks of Buying a Course From Institute of Mental Health Study online - whenever and wherever you want. One-to-one support from a dedicated tutor throughout your course. Certificate immediately upon course completion 100% Money back guarantee Exclusive discounts on your next course purchase from Institute of Mental Health Enrolling in the Marketing Principles course can assist you in getting into your desired career quicker than you ever imagined. So without further ado, start now. Process of Evaluation After studying the Marketing Principles course, your skills and knowledge will be tested with a MCQ exam or assignment. You must get a score of 60% to pass the test and get your certificate.  Certificate of Achievement Upon successfully completing the Marketing Principles course, you will get your CPD accredited digital certificate immediately. And you can also claim the hardcopy certificate completely free of charge. All you have to do is pay a shipping charge of just £3.99. Who Is This Course for? This Marketing Principles is suitable for anyone aspiring to start a career in Marketing Principles; even if you are new to this and have no prior knowledge on Marketing Principles, this course is going to be very easy for you to understand.  And if you are already working in the Marketing Principles field, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  Taking this Marketing Principles course is a win-win for you in all aspects.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements This Marketing Principles course has no prerequisite.  You don't need any educational qualification or experience to enrol in the Marketing Principles course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online Marketing Principles course. Moreover, this course allows you to learn at your own pace while developing transferable and marketable skills. Course Curriculum Section 01: Marketing and the Firm Marketing and the Firm 00:01:00 Marketing Orientation and Context 00:01:00 Creating Value and Competitive Advantage #1 00:01:00 Creating Value and Competitive Advantage #2 00:01:00 Marketing Definition 00:01:00 Marketing Exchange 00:01:00 Marketing Interactions #1 00:01:00 Marketing Interactions #2 00:02:00 BCG's Advantage Matrix Model 00:01:00 Section 02: Situational Marketing Analysis External Environment - Analysis #1 00:01:00 External Environment - Analysis #2 00:01:00 External Environment - Analysis #3 00:01:00 External Forces Facing Marketing 00:01:00 Demographic Shift 00:01:00 Demographic Changes 00:02:00 Stakeholder Mapping #1 00:01:00 Stakeholder Mapping #2 00:01:00 Section 03: Microenvironments - Industry and Market Microenvironments - Industry and Market 00:01:00 Difference between an Industry and a Sector 00:02:00 Common Mistakes #1 00:02:00 Common Mistakes #2 00:01:00 Market Analysis #1 00:01:00 Market Analysis #2 00:01:00 Internal Firm Analysis and Environment Analysis 00:02:00 Enterprise Value Chain Analysis #1 00:01:00 Enterprise Value Chain Analysis #2 00:01:00 Enterprise Value Chain Analysis #3 00:01:00 Porter's Value Chain 00:01:00 Elements in Porter's Value Chain #1 00:01:00 Elements in Porter's Value Chain #2 00:01:00 SWOT - Gap Analysis 00:02:00 TOWS and Building Conversion Strategies 00:01:00 Section 04: Formulating a Marketing Plan - Building Components Formulating a Marketing Plan - Building Components #1 00:01:00 Formulating a Marketing Plan - Building Components #2 00:02:00 Formulating a Marketing Plan - Building Components #3 00:01:00 Product Life Cycle and Building a Product Strategy 00:01:00 Segmentation and Targeting- Geography, Consumer Behaviour and Cultures 00:01:00 Positioning Your Market Offering 00:01:00 Pricing Your Product for the Market 00:01:00 Channels and Distribution 00:01:00 Promotion and Brand Advertising 00:01:00 Business Development and Customer Relationship Marketing (CRM) 00:01:00 Hard-selling 00:01:00 Sales Funnel 00:01:00 Stages in the Sales Funnel 00:02:00 Section 05: Executing the Marketing Plan Action Planning, Roles and Responsibilities 00:01:00 Metrics and Measures 00:02:00 Marketing and Information Technology 00:01:00 Marketing Information (MIS) Systems: Sorting Data and Reporting 00:02:00 Marketing and Information Technology 00:01:00 The Marketing Profession and Building the Marketing Team 00:01:00

Are basic leadership and critical thinking abilities your strong point? Do you like being able to create products and sell it? Do you know how to set goals clearly? Do you like managing your own business? If so, then this diploma course is for you. Description: Beginning your own business is an accomplishment in itself that you ought to be amped up for however it is just the beginning stage. The next thing you should know is to figure out how to make it become unique that will be recognized. In this course, you will be able to use the information about your business and what you can do to make it astounding. In this course, you will learn appropriate hints and thoughts for making your business emerge in the market. This will thus give you an edge over your competitions. Moreover, this course will set you up for a profession in a business administration condition where you will have the capacity to perform business-related exercises and in addition administration obligations viable. You will cover various scopes like Business Communication, Financial Accounting, Principles of Marketing, Human Resources and Project Management. Who is the course for? For employees, businessmen and entrepreneurs who are interested in upgrading their business administration skills. Or anyone who is interested in leading and managing their own business. Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Assessment:  At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. Before sitting for your final exam you will have the opportunity to test your proficiency with a mock exam. Certification: After you have successfully passed the test, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. PDF certificate's turnaround time is 24 hours and for the hardcopy certificate, it is 3-9 working Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognised accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path: This Diploma in Business Administration and Branding Course will be useful and would be beneficial for the management occupations especially the following careers: Administrative Services Manager Advertising, Promotions and Marketing Managers Agricultural Managers Architectural and Engineering Managers Compensation and Benefits Managers Computer and Information Systems Manager Construction Managers Financial Managers Food Services Managers Human Resources Managers Industrial Production Managers Management Directors Medical and Health Managers Natural Sciences Managers And other Top Executives. Business Administration What is Business Administration? 00:30:00 Business and Organisations 00:15:00 Management of Business 00:30:00 Business Organization Perspectives 00:30:00 Decision Making 00:30:00 Approaches to Leadership 00:30:00 Business Communication Communication Basics 00:30:00 Know Thyself and The Message 00:30:00 Learn To Use Terms of Business Communication That Invoke Emotions 00:30:00 Pay Attention To Body Language 00:30:00 Learn To Use Terms That Spark Interest 00:30:00 The Benefits Of Presenting Your Message In 1 Minute 00:30:00 Branding Basics Introduction 00:15:00 The Purple Cow 00:30:00 Try to be an Exceptionist 00:30:00 Make Yourself Well Recognised Before Your Customers 00:15:00 Wow Your Customers 00:30:00 Develop a Personal Style 00:30:00 Use Creative Imageries to Catch the Attention 00:30:00 Be a Traditionalist, Not a Fad Chaser 00:15:00 Bringing Out the Purple Cow in Your Business 00:30:00 Conclusion 00:15:00 Brand Management Defining Branding 00:15:00 What Are You All About? 00:30:00 Creating a Mission 00:15:00 Creating a Vision of the Future 00:15:00 Positioning Your Brand 00:15:00 Developing Your Style 00:15:00 Developing a Brand Name and Slogan 00:15:00 Creating a Visual Identity 00:30:00 Living Your Brand 00:15:00 Connecting with Customers 00:15:00 Launching Your Brand 00:15:00 Taking Your Brand's Pulse 00:15:00 Performing a SWOT Analysis 00:15:00 Measuring Brand Health with a Balanced Scorecard 00:15:00 Middleton's Brand Matrix 00:15:00 Interpreting Evaluation Results 00:15:00 Keeping the Brand Alive 00:15:00 Going Beyond the Brand 00:15:00 Business Branding What Is Branding 00:30:00 Research Your Topic Thoroughly 00:30:00 Let People Know Who You Are: Distinguish Yourself 01:00:00 Represent Yourself 00:30:00 Networking With Social Media 01:00:00 Use Videos 00:30:00 Offer Training Courses 00:30:00 Provide Quality Material And Customer Service 00:30:00 Live And Breathe Your Brand 00:30:00 How Not Branding Can Spell Disaster 00:15:00 Personal Branding Module One - Getting Started 00:30:00 Module Two - Defining Yourself (I) 00:30:00 Module Three - Defining Yourself (II) 00:30:00 Module Four - Controlling and Developing Your Image 00:30:00 Module Five - Personal and Professional Influences 00:30:00 Module Six - Sharpening Your Brand 00:30:00 Module Seven - Appearance Matters 00:30:00 Module Eight - Social Media (I) 00:30:00 Module Nine - Social Media (II) 02:00:00 Module Ten - Brand Management During a Crisis 01:00:00 Module Eleven - Branding Personality Traits 00:30:00 Module Twelve - Wrapping Up 00:30:00 Mock Exam Mock Exam- Advanced Diploma in Business Administration and Branding 00:20:00 Final Exam Final Exam- Advanced Diploma in Business Administration and Branding 00:20:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, 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 Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data 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 Having these various qualifications 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. Course Curriculum 19 sections • 99 lectures • 00:08:00 total length •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 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 •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 •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 •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 •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 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •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 •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 •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 •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 •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 •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 •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 •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 •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 •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 •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- Python for Data Analysis: 00:00:00

This course helps you prepare for your CISSP certification. In this course, we will be discussing CISSP? Certification Domain 3: Security Architecture and Engineering, which makes up 13% of the weighted exam questions; being so broad, it contains close to 25% of the exam materials.

Overview In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning & Data Science Bootcamp 2023 course can help you out. This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing.  Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol 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? Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets. 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 Course Curriculum 18 sections • 98 lectures • 23:48:00 total length •Welcome & Course Overview6: 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 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 •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 •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 •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 •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 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •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): 00:17:00 •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 •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 •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 •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 •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 •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 •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 •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 •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 •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

Overview This comprehensive course on Data Visualization and Reporting with Power BI will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Visualization and Reporting with Power BI 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 Visualization and Reporting with Power BI. It is available to all students, of all academic backgrounds. Requirements Our Data Visualization and Reporting with Power BI 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 15 sections • 140 lectures • 14:25:00 total length •Welcome!: 00:01:00 •What is Power BI?: 00:03:00 •Download & Installing Power BI Desktop: 00:04:00 •Getting to know the interface: 00:03:00 •Mini Project: Transform Data: 00:07:00 •Mini Project: Visualize Data: 00:05:00 •Mini Project: Creating a Data Model: 00:07:00 •Course Outline: What will you learn in this course?: 00:05:00 •How to learn best with this course?: 00:03:00 •Creating our initial project file: 00:04:00 •Working with the attached project files: 00:04:00 •Exploring the Query Editor: 00:06:00 •Connecting to our data source: 00:07:00 •Editing rows: 00:08:00 •Changing data types: 00:08:00 •Replacing values: 00:03:00 •Close & Apply: 00:03:00 •Connecting to a csv file: 00:03:00 •Connecting to a web page: 00:05:00 •Extracting characters: 00:06:00 •Splitting & merging columns: 00:09:00 •Creating conditional columns: 00:06:00 •Creating columns from examples: 00:09:00 •Merging Queries: 00:17:00 •Pivoting & Unpivoting: 00:06:00 •Appending Queries: 00:08:00 •Practice & Solution: Population table: 00:15:00 •The Fact-Dimension-Model: 00:09:00 •Practice: Load the dimension table: 00:04:00 •Organizing our queries in groups: 00:03:00 •Entering data manually: 00:05:00 •Creating an index column: 00:03:00 •Workflow & more transformations: 00:05:00 •Module summary: 00:05:00 •Exercise 1 - Instruction: 00:02:00 •Exercise 1 - Exercise Solution: 00:11:00 •Advanced Editor - Best practices: 00:09:00 •Performance: References vs. Duplicating: 00:10:00 •Performance: Enable / Disable Load & Report Refresh: 00:05:00 •Group by: 00:05:00 •Mathematical Operations: 00:05:00 •Run R Script: 00:15:00 •Using Parameters to dynamically transform data: 00:06:00 •M formula language: Basics: 00:07:00 •M formula language: Values, Lists & Tables: 00:14:00 •M formula language: Functions: 00:13:00 •M formula language: More functions & steps: 00:05:00 •Exercise 2 - Instructions: 00:01:00 •Exercise 2 - solution: 00:05:00 •Understanding the relationship: 00:05:00 •Create & edit relationships: 00:06:00 •One-to-many & one-to-one relationship: 00:06:00 •Many-to-many (m:n) relationship: 00:08:00 •Cross filter direction: 00:06:00 •Activate & deactivate relationships: 00:06:00 •Model summary: 00:03:00 •Exercise 3 Create Model: 00:02:00 •Exercise 3 Solution: 00:02:00 •Our first visual: 00:08:00 •The format tab: 00:12:00 •Understanding tables: 00:10:00 •Conditional formatting: 00:09:00 •The Pie Chart: 00:06:00 •All about the filter visual: 00:13:00 •The filter pane for developers: 00:09:00 •Cross filtering & edit interactions: 00:04:00 •Syncing slicers across pages: 00:07:00 •Creating drill downs: 00:08:00 •Creating drill throughs: 00:07:00 •The tree map visual: 00:07:00 •The decomposition tree: 00:05:00 •Understanding the matrix visual: 00:05:00 •Editing pages: 00:07:00 •Buttons & Actions: 00:09:00 •Bookmarks to customize your report: 00:10:00 •Analytics and Forecasts with line charts: 00:10:00 •Working with custom visuals: 00:07:00 •Get data using R Script & R Script visual: 00:08:00 •Asking questions - Q&A visual: 00:04:00 •Wrap up - data visualization: 00:08:00 •Python in Power BI - Plan of attack: 00:03:00 •Setting up Python for Power BI: 00:03:00 •Transforming data using Python: 00:11:00 •Creating visualizations using Python: 00:08:00 •Violin plots, pair plots & ridge plots using Python: 00:15:00 •Machine learning (BayesTextAnalyzer) using Python: 00:00:00 •Performance & Troubleshooting: 00:03:00 •Introduction: 00:01:00 •Show Empathy & Identify the Requirement: 00:03:00 •Finding the Most Suitable KPI's: 00:02:00 •Choose an Effective Visualization: 00:04:00 •Make Use of Natural Reading Pattern: 00:03:00 •Tell a Story Using Visual Cues: 00:05:00 •Avoid Chaos & Group Information: 00:02:00 •Warp Up - Storytelling with Data: 00:02:00 •Introduction: 00:03:00 •The project data: 00:04:00 •Measures vs. Calculated Columns: 00:15:00 •Automatically creating a date table in DAX: 00:08:00 •CALENDAR: 00:05:00 •Creating a complete date table with features: 00:04:00 •Creating key measure table: 00:03:00 •Aggregation functions: 00:06:00 •The different versions of COUNT: 00:14:00 •SUMX - Row based calculations: 00:09:00 •CALCULATE - The basics: 00:11:00 •Changing the context with FILTER: 00:07:00 •ALL: 00:08:00 •ALL SELECTED: 00:03:00 •ALL EXCEPT: 00:07:00 •How to go on now?: 00:03:00 •Power BI Pro vs Premium & Signing up: 00:04:00 •Exploring the interface: 00:04:00 •Discovering your workspace: 00:03:00 •Connecting Power BI Desktop & Cloud: 00:04:00 •Understanding datasets & reports: 00:03:00 •Working on reports: 00:04:00 •Updating reports from Power BI Desktop: 00:04:00 •Creating and working with workspaces: 00:07:00 •Installing & using a data gateway: 00:13:00 •Get Quick Insights: 00:03:00 •Creating dashboards: 00:04:00 •Sharing our results through Apps: 00:10:00 •Power BI Mobile App: 00:05:00 •Creating the layout for the Mobile App: 00:04:00 •Wrap up - Power BI Cloud: 00:07:00 •Introduction: 00:03:00 •Creating a Row-Level Security: 00:05:00 •Row-Level Security in the Cloud: 00:04:00 •Row-Level Security & Data Model: 00:05:00 •Dynamic Row-Level Security: 00:07:00 •Dynamic Many-to-Many RLS: 00:04:00 •Hierarchical Row-Level Security: 00:13:00 •JSON & REST API: 00:10:00 •Setting up a local MySQL database: 00:14:00 •Connecting to a MySQL database in Power BI: 00:05:00 •Connecting to a SQL database (PostgreSQL): 00:05:00 •Congratulations & next steps: 00:06:00 •The End: 00:01:00 •Resources - Data Visualization and Reporting with Power BI: 00:00:00

Overview This comprehensive course on Complete Microsoft Power BI 2021 will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Complete Microsoft Power BI 2021 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 Complete Microsoft Power BI 2021. It is available to all students, of all academic backgrounds. Requirements Our Complete Microsoft Power BI 2021 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 15 sections • 140 lectures • 14:25:00 total length •Welcome!: 00:01:00 •What is Power BI?: 00:03:00 •Download & Installing Power BI Desktop: 00:04:00 •Getting to know the interface: 00:03:00 •Mini Project: Transform Data: 00:07:00 •Mini Project: Visualize Data: 00:05:00 •Mini Project: Creating a Data Model: 00:07:00 •Course Outline: What will you learn in this course?: 00:05:00 •How to learn best with this course?: 00:03:00 •Creating our initial project file: 00:04:00 •Working with the attached project files: 00:04:00 •Exploring the Query Editor: 00:06:00 •Connecting to our data source: 00:07:00 •Editing rows: 00:08:00 •Changing data types: 00:08:00 •Replacing values: 00:03:00 •Close & Apply: 00:03:00 •Connecting to a csv file: 00:03:00 •Connecting to a web page: 00:05:00 •Extracting characters: 00:06:00 •Splitting & merging columns: 00:09:00 •Creating conditional columns: 00:06:00 •Creating columns from examples: 00:09:00 •Merging Queries: 00:17:00 •Pivoting & Unpivoting: 00:06:00 •Appending Queries: 00:08:00 •Practice & Solution: Population table: 00:15:00 •The Fact-Dimension-Model: 00:09:00 •Practice: Load the dimension table: 00:04:00 •Organizing our queries in groups: 00:03:00 •Entering data manually: 00:05:00 •Creating an index column: 00:03:00 •Workflow & more transformations: 00:05:00 •Module summary: 00:05:00 •Exercise 1 - Instruction: 00:02:00 •Exercise 1 - Exercise Solution: 00:11:00 •Advanced Editor - Best practices: 00:09:00 •Performance: References vs. Duplicating: 00:10:00 •Performance: Enable / Disable Load & Report Refresh: 00:05:00 •Group by: 00:05:00 •Mathematical Operations: 00:05:00 •Run R Script: 00:15:00 •Using Parameters to dynamically transform data: 00:06:00 •M formula language: Basics: 00:07:00 •M formula language: Values, Lists & Tables: 00:14:00 •M formula language: Functions: 00:13:00 •M formula language: More functions & steps: 00:05:00 •Exercise 2 - Instructions: 00:01:00 •Exercise 2 - solution: 00:05:00 •Understanding the relationship: 00:05:00 •Create & edit relationships: 00:06:00 •One-to-many & one-to-one relationship: 00:06:00 •Many-to-many (m:n) relationship: 00:08:00 •Cross filter direction: 00:06:00 •Activate & deactivate relationships: 00:06:00 •Model summary: 00:03:00 •Exercise 3 Create Model: 00:02:00 •Exercise 3 Solution: 00:02:00 •Our first visual: 00:08:00 •The format tab: 00:12:00 •Understanding tables: 00:10:00 •Conditional formatting: 00:09:00 •The Pie Chart: 00:06:00 •All about the filter visual: 00:13:00 •The filter pane for developers: 00:09:00 •Cross filtering & edit interactions: 00:04:00 •Syncing slicers across pages: 00:07:00 •Creating drill downs: 00:08:00 •Creating drill throughs: 00:07:00 •The tree map visual: 00:07:00 •The decomposition tree: 00:05:00 •Understanding the matrix visual: 00:05:00 •Editing pages: 00:07:00 •Buttons & Actions: 00:09:00 •Bookmarks to customize your report: 00:10:00 •Analytics and Forecasts with line charts: 00:10:00 •Working with custom visuals: 00:07:00 •Get data using R Script & R Script visual: 00:08:00 •Asking questions - Q&A visual: 00:04:00 •Wrap up - data visualization: 00:08:00 •Python in Power BI - Plan of attack: 00:03:00 •Setting up Python for Power BI: 00:03:00 •Transforming data using Python: 00:11:00 •Creating visualizations using Python: 00:08:00 •Violin plots, pair plots & ridge plots using Python: 00:15:00 •Machine learning (BayesTextAnalyzer) using Python: 00:00:00 •Performance & Troubleshooting: 00:03:00 •Introduction: 00:01:00 •Show Empathy & Identify the Requirement: 00:03:00 •Finding the Most Suitable KPI's: 00:02:00 •Choose an Effective Visualization: 00:04:00 •Make Use of Natural Reading Pattern: 00:03:00 •Tell a Story Using Visual Cues: 00:05:00 •Avoid Chaos & Group Information: 00:02:00 •Warp Up - Storytelling with Data: 00:02:00 •Introduction: 00:03:00 •The project data: 00:04:00 •Measures vs. Calculated Columns: 00:15:00 •Automatically creating a date table in DAX: 00:08:00 •CALENDAR: 00:05:00 •Creating a complete date table with features: 00:04:00 •Creating key measure table: 00:03:00 •Aggregation functions: 00:06:00 •The different versions of COUNT: 00:14:00 •SUMX - Row based calculations: 00:09:00 •CALCULATE - The basics: 00:11:00 •Changing the context with FILTER: 00:07:00 •ALL: 00:08:00 •ALL SELECTED: 00:03:00 •ALL EXCEPT: 00:07:00 •How to go on now?: 00:03:00 •Power BI Pro vs Premium & Signing up: 00:04:00 •Exploring the interface: 00:04:00 •Discovering your workspace: 00:03:00 •Connecting Power BI Desktop & Cloud: 00:04:00 •Understanding datasets & reports: 00:03:00 •Working on reports: 00:04:00 •Updating reports from Power BI Desktop: 00:04:00 •Creating and working with workspaces: 00:07:00 •Installing & using a data gateway: 00:13:00 •Get Quick Insights: 00:03:00 •Creating dashboards: 00:04:00 •Sharing our results through Apps: 00:10:00 •Power BI Mobile App: 00:05:00 •Creating the layout for the Mobile App: 00:04:00 •Wrap up - Power BI Cloud: 00:07:00 •Introduction: 00:03:00 •Creating a Row-Level Security: 00:05:00 •Row-Level Security in the Cloud: 00:04:00 •Row-Level Security & Data Model: 00:05:00 •Dynamic Row-Level Security: 00:07:00 •Dynamic Many-to-Many RLS: 00:04:00 •Hierarchical Row-Level Security: 00:13:00 •JSON & REST API: 00:10:00 •Setting up a local MySQL database: 00:14:00 •Connecting to a MySQL database in Power BI: 00:05:00 •Connecting to a SQL database (PostgreSQL): 00:05:00 •Congratulations & next steps: 00:06:00 •The End: 00:01:00 •Resources - Complete Microsoft Power BI 2021: 00:00:00

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