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Overview This comprehensive course on Intermediate Python Coding will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Intermediate Python Coding 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 Intermediate Python Coding. It is available to all students, of all academic backgrounds. Requirements Our Intermediate Python Coding 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 10 sections • 59 lectures • 05:21:00 total length •Course Introduction: 00:02:00 •Course Curriculum: 00:05:00 •How to get Pre-requisites: 00:02:00 •Getting Started on Windows, Linux or Mac: 00:01:00 •How to ask Great Questions: 00:02:00 •Introduction to Class: 00:07:00 •Create a Class: 00:09:00 •Calling a Class Object: 00:08:00 •Class Parameters - Objects: 00:05:00 •Access Modifiers(theory): 00:10:00 •Summary: 00:02:00 •Introduction to methods: 00:06:00 •Create a method: 00:07:00 •Method with parameters: 00:12:00 •Method default parameter: 00:06:00 •Multiple parameters: 00:05:00 •Method return keyword: 00:04:00 •Method Overloading: 00:05:00 •Summary: 00:02:00 •Introduction to OOPs: 00:05:00 •Classes and Objects: 00:08:00 •Class Constructors: 00:07:00 •Assessment Test1: 00:01:00 •Solution for Assessment Test1: 00:03:00 •Summary: 00:01:00 •Introduction: 00:04:00 •Inheritance: 00:13:00 •Getter and Setter Methods: 00:12:00 •Polymorphism: 00:13:00 •Assessment Test2: 00:03:00 •Solution for Assessment Test2: 00:03:00 •Summary: 00:01:00 •Introduction: 00:03:00 •Access Modifiers (public, protected, private): 00:21:00 •Encapsulation: 00:07:00 •Abstraction: 00:07:00 •Summary: 00:02:00 •Introduction: 00:01:00 •Dice Game: 00:06:00 •Card and Deck Game Playing: 00:07:00 •Summary: 00:01:00 •Introduction: 00:01:00 •PIP command installations: 00:12:00 •Modules: 00:12:00 •Naming Module: 00:03:00 •Built-in Modules: 00:03:00 •Packages: 00:08:00 •List Packages: 00:03:00 •Summary: 00:02:00 •Introduction: 00:02:00 •Reading CSV files: 00:11:00 •Writing CSV files: 00:04:00 •Summary: 00:01:00 •Introduction: 00:01:00 •Errors - Types of Errors: 00:08:00 •Try - ExceptExceptions Handling: 00:07:00 •Creating User-Defined Message: 00:05:00 •Try-Except-FinallyBlocks: 00:07:00 •Summary: 00:02:00

Overview This comprehensive course on Higher Order Functions in Python - Level 03 will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Higher Order Functions in Python - Level 03 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 Higher Order Functions in Python - Level 03. It is available to all students, of all academic backgrounds. Requirements Our Higher Order Functions in Python - Level 03 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 7 sections • 7 lectures • 00:48:00 total length •Introduction: 00:01:00 •Simple Higher Order Functions: 00:05:00 •Sorting with Keys: 00:09:00 •Map Function: 00:14:00 •Filter Function: 00:08:00 •List Comprehension Alternative: 00:04:00 •Introduction to Recursion: 00:07:00

Sockets programming training course description A hands on course for programmers using Sockets. It is important to recognise that the course assumes that delegates are already familiar with TCP/IP and Python. Practical exercises follow all the major theory sessions. What will you learn Read Python programs which use Sockets. Write Python programs which use Sockets. Debug Python programs which use Sockets. Sockets programming training course details Who will benefit: Programmers working with network applications. Prerequisites: TCP/IP foundation for engineers Python for network engineers Duration 2 days Sockets programming training course contents What is a socket? Review of IP, ICMP, UDP vs TCP, IP addresses, protocol numbers, ports. API's, UNIX I/O, sockets. SOCK_STREAM, SOCK_DGRAM. Hands on Compile and run code. The systems calls Clients and servers, structs, socket(), bind(), connect(), listen(), accept(), send(), recv(), sendto (), recvfrom(), close(), shutdown(), getpeername(), gethostname(). Hands on Walk through of example client and server code. First code TCP connections, passive opens, active opens. Hands on Write a simple 'hello world' server and client. Application protocols User character stream, ASCII turn taking, binary protocols. Hands on Raw SMTP, Writing a mail client. Clients Concurrency, polling, threads, event driven programming. Hands on Conferencing application. Servers Concurrency, stateful, stateless. Forks and execs. inetd. Hands on Running servers with and without inetd, chroot jails, conferencing server modifications. Advanced techniques Blocking, select(), partial send(s). Raw sockets, example sockets using Java, Perl and PHP. Hands on A broadcast application.

Overview This comprehensive course on Computer Science 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 Computer Science 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 Computer Science With Python. It is available to all students, of all academic backgrounds. Requirements Our Computer Science 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 5 sections • 152 lectures • 04:54:00 total length •Introduction: 00:03:00 •Quiz 1: 00:02:00 •Quiz 1 Solution: 00:01:00 •What is Programming: 00:03:00 •Quiz 2: 00:01:00 •Quiz 2 Solution: 00:01:00 •Meeting the interpreter and Problem Quiz 3: 00:01:00 •Quiz 3 solution: 00:01:00 •Congratulations: 00:01:00 •Why programming and Quiz 4: 00:02:00 •Quiz 4 Solution: 00:03:00 •Grammar and Python Rules: 00:04:00 •Backus Naur Form: 00:03:00 •Quiz 4 part 2: 00:01:00 •Quiz 4 part 2 Solution: 00:01:00 •Python Grammar and Quiz 5: 00:05:00 •Quiz 5 Solution: 00:03:00 •Quiz 6: 00:01:00 •Quiz 6 Solution: 00:02:00 •Processors: 00:02:00 •Introducing Variables: 00:05:00 •Variables Quiz 7: 00:02:00 •Variables Can Vary: 00:03:00 •Variables Quiz 8: 00:01:00 •Quiz 8 Solution: 00:01:00 •Variables Quiz 9: 00:01:00 •Quiz 9 Solution: 00:01:00 •Variables Quiz 10: 00:01:00 •Quiz 10 Solution: 00:03:00 •Introducing Strings: 00:04:00 •Using Strings Quiz 11: 00:01:00 •Quiz 11 Solution: 00:03:00 •Strings and Numbers - String Concatenation Quiz Solution: 00:03:00 •String indexing: 00:02:00 •Quiz 13: 00:01:00 •Quiz 13 Solution: 00:03:00 •String subsequences: 00:04:00 •String subsequences quiz 14: 00:01:00 •Quiz 14 solution: 00:02:00 •Understanding selection quiz 15: 00:01:00 •Quiz 15 solution: 00:04:00 •Finding string in string quiz 16: 00:04:00 •Quiz 16 solution: 00:02:00 •Testing and quiz 17: 00:02:00 •Quiz 17 solution: 00:03:00 •Find With Parameter Quiz 18: 00:02:00 •Quiz 18 solution: 00:01:00 •Extracting links from a web page Quiz 19: 00:03:00 •Extracting links from a web page Quiz 19 Solution: 00:02:00 •Final Quiz: 00:01:00 •Final Quiz Solution: 00:02:00 •Congratulations: 00:01:00 •Unit Overview: 00:03:00 •Procedural Abstraction: 00:03:00 •Introducing Procedures: 00:04:00 •Procedure code quiz 1: 00:04:00 •Quiz 1 Solution: 00:01:00 •Output and quiz 2: 00:01:00 •Quiz 2 Solution: 00:02:00 •Return Statement and Quiz 3: 00:03:00 •Quiz 3 solution: 00:02:00 •Inc Procedure Quiz 4: 00:01:00 •Quiz 4 Solution: 00:01:00 •Sum Procedure and Quiz 5: 00:01:00 •Quiz 5 Solution: 00:02:00 •Sum procedure with a return statement: 00:02:00 •Square procedure quiz 6: 00:01:00 •Quiz 6 Solution: 00:02:00 •Sum 3 Quiz 7: 00:01:00 •Quiz 7 Solution: 00:02:00 •Double string procedure quiz 8: 00:01:00 •Quiz 8 Solution: 00:01:00 •Find second quiz 9: 00:02:00 •Quiz 9 Solution: 00:02:00 •Equality Comparison Quiz 10: 00:04:00 •Quiz 10 Solution: 00:01:00 •If statement quiz 11: 00:03:00 •Quiz 11 Solution: 00:03:00 •Is friend quiz 12: 00:02:00 •Quiz 12 solution: 00:02:00 •Is friend quiz 13: 00:02:00 •Quiz 13 Solution: 00:02:00 •The Or construct: 00:03:00 •Quiz 14 solution: 00:06:00 •While loop quiz 15: 00:05:00 •Quiz 15 solution: 00:03:00 •While loop quiz 16: 00:01:00 •Quiz 16 solution: 00:02:00 •Print numbers quiz 17: 00:01:00 •Quiz 17 solution: 00:02:00 •Factorial quiz 18: 00:02:00 •Quiz 18 solution: 00:02:00 •Break quiz 19: 00:04:00 •Quiz 19 solution: 00:03:00 •Quiz 20: 00:05:00 •Quiz 20 Solution: 00:01:00 •No links quiz 21: 00:01:00 •Print all links quiz 21 solution: 00:03:00 •Final Quiz: 00:01:00 •Final Quiz Solution: 00:02:00 •Unit Overview: 00:03:00 •Stooges and quiz 1: 00:01:00 •Quiz 1 Solution: 00:01:00 •Countries quiz: 00:01:00 •Quiz 3 solution: 00:01:00 •Relative Size Quiz: 00:01:00 •Quiz 4 Solution: 00:01:00 •Lists Mutation: 00:01:00 •Different Stooges quiz: 00:01:00 •Quiz 5 Solution: 00:01:00 •Secret Agent Man Quiz: 00:01:00 •Replace Spy Quiz: 00:01:00 •Quiz 7 Solution: 00:03:00 •Python List Addition and Length: 00:02:00 •List Operations In Python: 00:02:00 •Python lists length quiz: 00:01:00 •Quiz 8 Solution: 00:01:00 •Append Quiz: 00:01:00 •Hard drive quiz: 00:01:00 •Quiz 11 Solution: 00:01:00 •Python Loops on Lists Quiz: 00:02:00 •Quiz 12 solution: 00:02:00 •Python For loops: 00:03:00 •Sum List Quiz: 00:01:00 •Measure a String Quiz: 00:01:00 •Find Element Quiz: 00:02:00 •Quiz 15 solution: 00:04:00 •Quiz 16 solution: 00:01:00 •Python Union Procedure Quiz: 00:01:00 •Quiz 17 solution: 00:01:00 •Pop in Python Quiz 18: 00:02:00 •Quiz 18 solution: 00:03:00 •Collecting Links: 00:01:00 •Get All Links: 00:02:00 •Starting Get All Links Quiz: 00:01:00 •Quiz 19 solution: 00:01:00 •Updating Links Quiz: 00:01:00 •Quiz 20 Solution: 00:01:00 •Finishing Get All Links Quiz: 00:01:00 •Quiz 21 Solution: 00:01:00 •Finishing the Python Web Crawler: 00:03:00 •Crawling Process Quiz: 00:01:00 •Quiz 22 Solution: 00:01:00 •Crawl Web Quiz: 00:01:00 •Quiz 23 Solution: 00:01:00 •Crawl Web Loop Quiz: 00:01:00 •Quiz 24 Solution: 00:02:00 •Crawl If Quiz: 00:01:00 •Quiz 25 Solution: 00:01:00 •Finishing Crawl Web and Final Quiz: 00:02:00 •Final Quiz Solution & Conclusion: 00:03:00 •Assignment - Computer Science With Python: 00:00:00

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. 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 00:08:00 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 Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 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 00:07:00 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 - Python Machine Learning & Data Science Fundamentals 00:00:00

Duration 3 Days 18 CPD hours This course is intended for This course is designed for existing Python programmers who have at least one year of Python experience and who want to expand their programming proficiency in Python 3. Overview In this course, you will expand your Python proficiencies. You will: Select an object-oriented programming approach for Python applications. Create object-oriented Python applications. Create a desktop application. Create data-driven applications. Create and secure web service-connected applications. Program Python for data science. Implement unit testing and exception handling. Package an application for distribution. Python continues to be a popular programming language, perhaps owing to its easy learning curve, small code footprint, and versatility for business, web, and scientific uses. Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you'll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, creating web service-connected apps, performing data science tasks, unit testing, and creating and installing packages and executable applications. Lesson 1: Selecting an Object-Oriented Programming Approach for Python Applications Topic A: Implement Object-Oriented Design Topic B: Leverage the Benefits of Object-Oriented Programming Lesson 2: Creating Object-Oriented Python Applications Topic A: Create a Class Topic B: Use Built-in Methods Topic C: Implement the Factory Design Pattern Lesson 3: Creating a Desktop Application Topic A: Design a Graphical User Interface (GUI) Topic B: Create Interactive Applications Lesson 4: Creating Data-Driven Applications Topic A: Connect to Data Topic B: Store, Update, and Delete Data in a Database Lesson 5: Creating and Securing a Web Service-Connected App Topic A: Select a Network Application Protocol Topic B: Create a RESTful Web Service Topic C: Create a Web Service Client Topic D: Secure Connected Applications Lesson 6: Programming Python for Data Science Topic A: Clean Data with Python Topic B: Visualize Data with Python Topic C: Perform Linear Regression with Machine Learning Lesson 7: Implementing Unit Testing and Exception Handling Topic A: Handle Exceptions Topic B: Write a Unit Test Topic C: Execute a Unit Test Lesson 8: Packaging an Application for Distribution Topic A: Create and Install a Package Topic B: Generate Alternative Distribution Files

Overview This comprehensive course on Data Science 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 with Python 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 Data Science with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science 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 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 3 sections • 6 lectures • 01:15:00 total length •Module 01: Introduction to Python Data Science: 00:03:00 •Module 02: Environment Setup: 00:10:00 •Module 01: Numpy package for calculations: 00:16:00 •Module 02: Panda package for Data cleaning: 00:19:00 •Module 01: Matplotlib Data Visualization Part 1: 00:16:00 •Module 02: Matplotlib Data Visualization Part 2: 00:11:00

If you are new to Quantum computing, then this course will help you understand the fundamentals and practicalities of this field. This course will provide you with step-by-step guidance in learning the implementation and important methodologies associated with Quantum computing in a beginner-friendly environment.

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. 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 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 Mock Exam Mock Exam - Diploma in C++ and Python Programming 00:30:00 Final Exam Final Exam - Diploma in C++ and Python Programming 00:30:00 Order Your Certificates and Transcripts Order Your Certificates and Transcripts 00:00:00