929 Courses delivered Online

This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.

Overview This comprehensive course on Spatial Data Visualization and Machine Learning in Python Level 4 will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Spatial Data Visualization and Machine Learning in Python Level 4 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? After successfully completing the course you will be able to order your certificate, these are included in the price. Who is This course for? There is no experience or previous qualifications required for enrolment on this Spatial Data Visualization and Machine Learning in Python Level 4. It is available to all students, of all academic backgrounds. Requirements Our Spatial Data Visualization and Machine Learning in Python Level 4 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 8 sections • 21 lectures • 04:40:00 total length •Introduction: 00:14:00 •Python Installation: 00:03:00 •Installing Bokeh: 00:04:00 •Data Preparation: 00:24:00 •Creating a Bar Chart: 00:18:00 •Creating a Line Chart: 00:12:00 •Creating a Doughnut Chart: 00:22:00 •Creating a Magnitude Plot: 00:31:00 •Creating a Geo Map Plot: 00:20:00 •Creating a Grid Plot: 00:12:00 •Data Pre-processing: 00:21:00 •Building a Predictive Model: 00:21:00 •Building a Prediction Dataset: 00:07:00 •Adding predicted data to our plots - Part 1: 00:13:00 •Adding predicted data to our plots - Part 2: 00:14:00 •Adding predicted data to our plots - Part 3: 00:15:00 •Adding the Grid Plot: 00:08:00 •Installing Visual Studio Code: 00:01:00 •Creating the Project and Virtual Environment: 00:08:00 •Building and Running the Server: 00:12:00 •Resources: 00:00:00

Description: This diploma in C++ and Python programming course is a great way to get started in programming. It covers the study of the C++ and Python group of languages used to build most of the world's object oriented systems. The course is for interested students with a good level of computer literacy who wish to acquire programming skills. It is also ideal for those who wish to move to a developer role or areas such as software engineering. This is a great course to develop your coding skills. It teaches key features of imperative programming using C and is an ideal preliminary to the Object-Oriented Programming using Python. Join the course now! 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 completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. 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 recognized 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 After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Python 3 Beginners Module 01 Introduction FREE 00:29:00 Starter Examples 00:33:00 Learning C Concepts 00:13:00 Module 02 Data Types and Inference 00:20:00 Sizeof and IEEE 754 00:33:00 Constants L and R Values 00:11:00 Operators and Precedence 00:25:00 Literals 00:26:00 Module 03 Classes and Structs FREE 00:22:00 Enums 00:14:00 Unions 00:16:00 Introduction to Pointers 00:11:00 Pointers and Array Indexing 00:12:00 Using Const with Pointers 00:09:00 Pointers to String Literals 00:12:00 References 00:14:00 Smart Pointers 00:22:00 Arrays 00:15:00 Standard Library Strings 00:13:00 More Standard Library Strings 00:18:00 Functions 00:06:00 More Functions 00:16:00 Function Pointers 00:15:00 Control Statements 00:18:00 Python 3 Intermediate Module 04 Installing Python FREE 00:17:00 Documentation 00:30:00 Command Line 00:17:00 Variables 00:29:00 Simple Python Syntax 00:15:00 Keywords 00:18:00 Import Module 00:17:00 Additional Topics 00:23:00 Module 05 If Elif Else 00:31:00 Iterable 00:10:00 For 00:11:00 Loops 00:20:00 Execute 00:05:00 Exceptions 00:18:00 Data Types 00:24:00 Module 06 Number Types 00:28:00 More Number Types 00:13:00 Strings 00:20:00 More Strings 00:11:00 Files 00:08:00 Lists 00:15:00 Dictionaries 00:04:00 Tuples 00:07:00 Sets 00:09:00 Module 07 Comprehensions 00:10:00 Definitions 00:02:00 Functions 00:06:00 Default Arguments 00:06:00 Doc Strings 00:06:00 Variadic Functions 00:07:00 Factorial 00:07:00 Function Objects 00:07:00 Module 08 Lambda 00:11:00 Generators 00:06:00 Closures 00:10:00 Classes 00:09:00 Object Initialization 00:05:00 Class Static Members 00:07:00 Classic Inheritance 00:10:00 Data Hiding 00:07:00 Python 3 Advanced Iterators and Generators FREE 00:16:00 Regular Expressions 00:19:00 Introspection and Lambda Functions 00:27:00 Metaclasses and Decorators 00:24:00 Modules and Packages 00:25:00 Working with APIs 00:15:00 Metaprogramming Primer 00:19:00 Decorators and Monkey Patching 00:21:00 XML and JSON Structure 00:10:00 Generating XML and JSON 00:17:00 Parsing XML and JSON 00:19:00 Implementing Algorithms 00:19:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Overview This comprehensive course on Create Smart Maps in Python and Leaflet Level 3 will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Create Smart Maps in Python and Leaflet Level 3 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 Create Smart Maps in Python and Leaflet Level 3. It is available to all students, of all academic backgrounds. Requirements Our Create Smart Maps in Python and Leaflet Level 3 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 6 sections • 26 lectures • 03:41:00 total length •Introduction: 00:08:00 •Installing PostgreSQL and PostGIS Part1: 00:10:00 •Installing PostgreSQL and PostGIS Part2: 00:10:00 •Installing Python Django in a Virtual Environment: 00:10:00 •Installing and Configuring Atom IDE Part1: 00:10:00 •Installing and Configuring Atom IDE Part2: 00:03:00 •Creating a GeoDjango Application Skeleton: 00:10:00 •Adding a Spatial Database to our Django Backend: 00:09:00 •Updating our django models file: 00:08:00 •Registering our model in the admin file Part1: 00:09:00 •Registering our model in the admin file Part2: 00:10:00 •Registering our model in the admin file Part3: 00:10:00 •Updating the settings file: 00:07:00 •Creating the layout page Part 1: 00:09:00 •Creating the layout page Part 2: 00:10:00 •Creating the layout page Part 3: 00:07:00 •Creating the index page Part 1: 00:10:00 •Creating the index page Part 2: 00:07:00 •Updating the index page: 00:07:00 •Creating datasets: 00:10:00 •Displaying data on the map Part 1: 00:10:00 •Displaying data on the map Part 2: 00:02:00 •Creating a legend: 00:10:00 •Creating the barchart legend: 00:06:00 •Creating the barchart Part 1: 00:10:00 •Creating the barchart Part 2: 00:09:00

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Advance your data skills by mastering Spark programming in Python. This beginner's level course will help you understand the core concepts related to Apache Spark 3 and provide you with knowledge of applying those concepts to build data engineering solutions.

Build your own AI-driven image classifier web application

Introduction to web development with React. 30 Hour course over 5 weeks.

CCNP training course description The Implementing and Operating Cisco Enterprise Network Core Technologies (ENCOR) v1.2 course provides the knowledge and skills needed to configure, troubleshoot, and manage enterprise wired and wireless networks. You'll learn to implement security principles within an enterprise network and how to overlay network design using solutions such as SDAccess and SD-WAN. Course content includes 3 days of self-study material. This course helps you prepare for the 350-401 Implementing Cisco Enterprise Network Core Technologies (ENCOR) exam What will you learn Configure, troubleshoot, and manage enterprise wired and wireless networks Implement security principles within an enterprise network Prepare you prepare to take the 350-401 Implementing Cisco Enterprise Network Core Technologies (ENCOR) exam CCNP training course details Who will benefit: Mid-level network engineers, Network administrators, Network support technicians, Help desk technicians. Prerequisites: Implementation of Enterprise LAN networks. Basic understanding of Enterprise routing and wireless connectivity, and Python scripting Duration 5 days CCNP training course content Cisco Enterprise Network Architecture: Access, distribution, core in the hierarchical network. Cisco Switching Paths: Switching mechanisms, TCAM, CAM, process switching, fast switching, and CEF. Implementing Campus LAN Connectivity: Troubleshoot L2 connectivity using VLANs and trunkingBuilding Redundant Switched Topology: STP Implementing Layer 2 Port Aggregation Troubleshoot link aggregation using Etherchannel EIGRP Implement and optimize OSPFv2/v3, including adjacencies, packet types, and areas, summarization, and route filtering for IPv4/v6 Implement EBGP interdomain routing, path selection, and single and dual-homed networkingImplementing Network Redundancy: HSRP and VRRP Implement static and dynamic NAT Virtualization Protocols and TechniquesVPNs and Interfaces: Overlay technologies such as VRF, GRE, VPN, and LISP Wireless Principles: RF, antenna characteristics, and wireless standards.Wireless Deployment: Models available, autonomous AP deployments and cloud-based designs within the centralized Cisco WLC architecture Wireless Roaming and Location ServicesWireless AP Operation: How APs communicate with WLCs to obtain software, configurations, and centralized managementWireless Client Authentication: EAP, WebAuth, and PSK wireless client authentication on a WLC. Troubleshoot wireless client connectivity issues using various available tools Troubleshoot networks using services such as NTP, SNMP, Cisco IP SLAs, NetFlow, and Cisco IOS EEM Explain network analysis and troubleshooting tools, which include show and debug commands, as well as best practices in troubleshootingMulticast Protocols: IGMP v2/v3, PIM DM/SM and RPs Introducing QoS: Concepts and features. Implementing Network Services: Secure administrative access for Cisco IOS devices using CLI access, RBAC, ACL, and SSH, and device hardening concepts to secure devices from less secure applications Using Network Analysis ToolsInfrastructure Security: Scalable administration using AAA and the local database, features and benefits Enterprise Network Security Architecture: VPNs, content security, logging, endpoint security, personal firewalls, and other security features. Automation and Assurance with Cisco DNA Center: Purpose, function, features, and workflow. Intent-Based Networking, for network visibility, proactive monitoring, and application experienceCisco SD-Access Solution: Nodes, fabric control plane, and data plane, VXLAN gatewaysCisco SD-WAN Solution: Components and features of Cisco SD-WAN solutions, including the orchestration, management, control, and data planesBasics of Python Programming: Python components and conditionals with script writing and analysis Network Programmability: NETCONF and RESTCONF APIs in Cisco DNA Center and vManage Labs: Investigate the CAM. Analyze CEF. Troubleshoot VLAN and Trunk Issues. Tuning STP and Configuring RSTP. Configure MSTP. Troubleshoot EtherChannel. Implement Multi-area OSPF. Implement OSPF Tuning. Apply OSPF Optimization. Implement OSPFv3. Configure and Verify Single-Homed EBGP. Implementing HSRP. Configure VRRP. Implement NAT. Configure and Verify VRF. Configure and Verify a GRE Tunnel. Configure Static VTI Point-to-Point Tunnels. Configure Wireless Client Authentication in a Centralized Deployment. Troubleshoot Wireless Client Connectivity Issues. Configure Syslog. Configure and Verify Flexible NetFlow. Configuring Cisco IOS EEM. Troubleshoot Connectivity and Analyze Traffic with Ping, Traceroute, and Debug. Configure and Verify Cisco IP SLAs. Configure Standard and Extended ACLs. Configure Control Plane Policing. Implement Local and Server-Based AAA. Writing and Troubleshooting Python Scripts. Explore JSON Objects and Scripts in Python. Use NETCONF Via SSH. Use RESTCONF with Cisco IOS XE.

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