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Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:08:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:07:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Course Introduction Introduction 00:03:00 Part 1: Forecasting Basics of Forecasting 00:05:00 Creating Linear Model with Trendlines 00:08:00 1.1 Getting Data Ready For Regression Model Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Univariate analysis and EDD 00:03:00 Discriptive Data Analytics in Excel 00:10:00 Outlier Treatment 00:04:00 Identifying and Treating Outliers in Excel 00:04:00 Missing Value Imputation 00:03:00 Identifying and Treating missing values in Excel 00:04:00 Variable Transformation in Excel 00:03:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy Variable Creation in Excel 00:07:00 Correlation Analysis 00:09:00 Creating Correlation Matrix in Excel 00:08:00 1.2 Forecasting Using Regression Model The Problem Statement 00:01:00 Basic Equations and Ordinary Least Squares (OLS) method 00:08:00 Assessing accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy: RSE and R squared 00:07:00 Creating Simple Linear Regression model 00:02:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting results of Categorical variables 00:05:00 Creating Multiple Linear Regression model 00:07:00 1.3 Handling Special Events Like Holiday Sales Forecasting in presence of special events 00:02:00 Excel: Running Linear Regression using Solver 00:08:00 Excel: Including the impact of Special Events 00:22:00 1.4 Identifying Seasonality & Trend for Forecasting Models to identify Trend & Seasonality 00:06:00 Excel: Additive model to identify Trend & Seasonality 00:09:00 Excel: Multiplicative model to identify Trend & Seasonality 00:06:00 Market Basket Analysis Market Basket and Lift - Introduction 00:08:00 Named Ranges - Excel 00:10:00 Indirect Function - Excel 00:05:00 2-way lift calculation in Excel 00:11:00 2-way lift calculation - Dynamic 00:07:00 2-way lift data table creation 00:07:00 3-way lift calculation 00:19:00 Store Layout optimization using Lift values 00:15:00 RFM (Recency, Frequency, Monetary) Analysis RFM (recency, frequency, monetary) Analysis 00:08:00 RFM Analysis in Excel- Part 1 00:16:00 RFM Analysis in Excel- Part 2 00:12:00 Part 2: Pricing Part 2: Pricing Steps of setting a Pricing policy 00:03:00 Different Pricing Objectives 00:07:00 2.1 Estimating Demand Estimating Demand 00:07:00 Forms of Demand Curve 00:02:00 Excel: Estimating Linear Demand Curve 00:08:00 Excel: Estimating Power Demand curve with Elasticity 00:05:00 Excel: Estimating Power Demand Curve with points 00:03:00 Subjective Demand curve 00:01:00 Excel: Estimating Subjective Demand Curve 00:02:00 2.3 Evaluating Pricing Strategies Price Bundling 00:07:00 Types of Bundling 00:08:00 The Bundling Problem 00:04:00 Excel: Solving Bundling problem Part 1 00:14:00 Excel: Solving Bundling problem Part 2 00:08:00 Non-Linear Pricing Strategies 00:03:00 Excel: Solving Bundling problem (Price Reversal) 00:08:00 3.1 Lifetime Customer Value Lifetime Customer Value - Key concepts 00:09:00 Lifetime Customer Value - Excel model 00:11:00 3.2 Variations And Sensitivity Analysis Sensitivity Analysis in Excel 00:07:00 Variations in finding customer value 00:07:00 Appendix 1: Excel Crash Course Basics 00:08:00 Worksheet Basics 00:16:00 Entering values and Formulas 00:07:00 Data Handling Basics - Cut, Copy and Paste 00:14:00 Saving and Printing - Basics 00:09:00 Basic Formula Operations 00:13:00 Mathematical Formulas 00:19:00 Textual Formulas 00:17:00 Logical Formulas 00:11:00 Date-Time Formulas 00:07:00 Lookup Formulas ( V Lookup, Hlookup, Index-Match ) 00:08:00 Data Tools 00:19:00 Formatting data and tables 00:18:00 Pivot Tables 00:08:00 Advance Excel- Solver, Data tables 00:15:00 Assessment Assessment - Retail Analytics In Microsoft Excel Diploma 00:10:00 Obtain Your Certificate Order Your Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  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 You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python 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 course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:04:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:06:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Data Science & Machine Learning with Python 00:00:00

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

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(£10 postal charges will be applicable for international delivery) Standard-aligned lesson planning Innovative and engaging content and activities Assessments that measure higher-level thinking and skills Complete the program in your own time, at your own pace Each of our students gets full 24/7 tutor support *** Course Curriculum of Management (Delivery Manager Training)*** Here is the curriculum breakdown of the course: >> Delivery Manager Training << Module 1: Understanding Lean What is Lean? History behind Lean The Toyota Production System The TPS House Lean Tools Module 2: The Lean Culture Organisation's Culture: An Introduction Organisations Principles Identifying the Current State of The Organisation Defining Lean Principles in An Organisation Measuring the Gap Five Phases of Changing the Organisation And More.. Module 3: The Five Principles of Lean The Five Principles of Lean Module 4: Value and Waste What is Value? Value-Added Activities (VA) No-Value-Added Activities (NVA) What Is Muda or Waste? What is Mura? What is Muri? Causes of Muri Different Ways to Deal with Muri And More.. Module 5: Value Stream Mapping (VSM) Introduction to Value Stream Map (VSM) Benefits of Value Stream Map (VSM) The Purpose of a Value-Stream Map The People Who Use a Value Stream Map Takt and Cycle Time Generating the Map Map Reading Value Stream Mapping Process Managing with Maps Module 6: The Principles of Flow and Pull Understanding Flow The Seven Flows of Manufacturing Barriers to Flow Improving Flow Pull System Advantages of Using a Pull System Managing a Pull System What is continuous improvement? Tools and Techniques Module 7: Overview of Six Sigma What is Six Sigma? History of Six Sigma The Five Key Principles of Six Sigma Lean Six Sigma The Six Sigma methodologies Module 8: The Define Phase Project Planning Project Charter Voice of The Customer Critical-to-Quality Tree Module 9: The Measure Phase Process Definition Process Metrics The Process Baseline Module 10: The Analysis Phase Value Stream Analysis Root Cause Analysis Graphical Analysis Pareto Chart Box Plots Module 11: The Improve Phase Solutions Selection Matrix Cost Benefit Analysis Piloting A Solution Planning Implementation Module 12: The Control Phase Control Plan Visual Management SPC Charts Statistical Process Control Tests Celebration and Reflection Module 13: Organisational Skills Organising Daily Work Organising Workplace Organising Resources Organising Tools Cultivating Organisational Habits Module 14: Effective Planning and Scheduling Work Breakdown Structure Estimation Process and Resources Alignment Project Planning The Purpose of Project Planning Project Planning Guide Project Goals Project Deliverables Project Schedule Support Plans Human Resource Plan Communications Plan Project Management Pitfalls Risk Management And More.. Module 15: Invoicing/Petty Cash Financial Record Keeping Invoice Types of Invoices Benefits of Online Invoicing What to include in an Invoice? Petty Cash Keeping Petty Cash Petty Cash Management Tips on Running a Petty Cash Fund Budgeting Types of Budgets Sales Budget Expense Budget Production Budget Manufacturing Budget Labor Budget Assessment Process We offer an integrated assessment framework to make the process of evaluating learners easier. After completing an online module, you will be given immediate access to a specially designed MCQ test. The results will be immediately analyzed, and the score will be shown for your review. The passing score for each test will be set at 60%. You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you have completed all of the exams. CPD 150 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Is This Management (Delivery Manager Training) Course Right for You? Anyone interested in learning more about this subject should take this course. This course will help you grasp the basic concepts as well as develop a thorough understanding of the subject. All are welcome to take this course. There are no prerequisites for enrolment, and you can access the course materials from any location in the world. Requirements The program does not require any prior knowledge; everyone may participate! This course is open to anyone who is interested in learning from anywhere in the world. Every student must be over the age of 16 and have a passion for learning and literacy. This 100% online course can be accessed from any internet-connected device, such as a computer, tablet, or smartphone. This course allows you to study at your own speed and grow a quality skillset. Career path After completing this course, you are to start your career or begin the next phase of your career in this field. Our entire course will help you to gain a position of respect and dignity over your competitors. The certificate enhances your CV and helps you find work in the field concerned. Certificates CPD Accredited Certificate Digital certificate - £10 Diploma in Delivery Manager Training at QLS Level 5 Hard copy certificate - £119 After successfully completing the Diploma in Delivery Manager Training at QLS Level 5 course, you can order an original hardcopy certificate of achievement endorsed by the Quality Licence Scheme. The certificate will be home-delivered, with a pricing scheme of - 119 GBP inside the UK 129 GBP (including postal fees) for international delivery CPD Accredited Certificate 29 GBP for Printed Hardcopy Certificate inside the UK 39 GBP for Printed Hardcopy Certificate outside the UK (international delivery)

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

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 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

The Management and Leadership for Business Administration will help you initiate a course of action and take advantage of opportunities that arise within organizations. You will develop the necessary critical thinking and analytical skills to make any business a success. You will explore the nuances of strategic planning, leadership principles and practices, and performance management. The course will provide the concepts and tools to establish long-term visions and goals for an organization and to effectively manage daily routines in a dynamic work environment - regardless of the type of organization or the particular area of a business. This course will further develop your human resources consulting skills as you will work with real business clients. Why choose this course Earn an e-certificate upon successful completion. Accessible, informative modules taught by expert instructors Study in your own time, at your own pace, through your computer tablet or mobile device Benefit from instant feedback through mock exams and multiple-choice assessments Get 24/7 help or advice from our email and live chat teams Full Tutor Support on Weekdays Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Mock exams Multiple-choice assessment Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who is this course for? Management and Leadership for Business Administration is suitable for anyone who want to gain extensive knowledge, potential experience, and professional skills in the related field. This is a great opportunity for all student from any academic backgrounds to learn more on this subject. Course Content Introduction to Business Management Designing Your Organizational Structure 00:30:00 Introduction to Operations Management 00:15:00 Understanding Financial Terms 00:30:00 Getting the Right People in Place 00:15:00 Getting Your Product Together 00:15:00 Building a Corporate Brand 00:30:00 Marketing Your Product 01:00:00 Selling Your Product 00:15:00 Planning for the Future 00:15:00 Goal Setting and Goal Getting 00:30:00 Succession Planning 101 00:15:00 Managing Your Money 00:15:00 Ethics 101 00:15:00 Building a Strong Customer Care Team 00:15:00 Training Employees for Success 00:15:00 Leadership Essentials 00:15:00 Business Process Management The Fundamentals of Business Process Management 00:30:00 Defining Business Process Management 00:30:00 The Business Process Life Cycle 00:15:00 Making the Change 00:15:00 The Vision Phase 00:15:00 The Design Phase 01:00:00 How Does It Look? 00:15:00 The Modeling Phase 00:30:00 Execution Phase 00:07:00 The Monitoring Phase 00:30:00 The Optimizing Phase 01:00:00 Business Planning and Analysis Business Planning Basics 01:00:00 Market Evaluation 01:00:00 Analyze Competition 01:00:00 Determine A Marketing Strategy 01:00:00 Decide What Extras You May Need Like Staff etc 00:30:00 The Dangers In Not Making A Business Plan 00:15:00 Business Branding 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 00:05:00 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 Leadership & Managing People Module One - Getting Started 00:30:00 Module Two - The Evolution of Leadership 01:00:00 Module Three - Situational Leadership 01:00:00 Module Four - A Personal Inventory 01:00:00 Module Five - Modeling the Way 01:00:00 Module Six - Inspiring a Shared Vision 01:00:00 Module Seven - Challenging the Process 01:00:00 Module Eight - Enabling Others to Act 01:00:00 Module Nine - Encouraging the Heart 01:00:00 Module Ten - Basic Influencing Skills 01:00:00 Module Eleven - Setting Goals 01:00:00 Module Twelve - Wrapping Up 00:30:00 Management and Leadership Development Module One - Getting Started 01:00:00 Module Two - Grooming a New Manager 01:00:00 Module Three - Coaching and Mentoring (I) 01:00:00 Module Four - Coaching and Mentoring (II) 01:00:00 Module Five - Measuring Performance 01:00:00 Module Six - Motivating Managers 01:00:00 Module Seven - Signs of Poor Management 01:00:00 Module Eight - Trust Your Team of Managers 01:00:00 Module Nine - When an Employee Complains About Their Manager 01:00:00 Module Ten - When Do You Step In 00:30:00 Module Eleven - Remember These Basic Qualities 01:30:00 Module Twelve - Wrapping Up 01:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.