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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

Data Entry Administrator Diploma with Transcription and Translation Diploma Welcome to the Data Entry Administrator Diploma with Transcription and Translation. This multifaceted course aims to provide you with comprehensive skills in data entry, alongside a specialised focus on Transcription and Translation. The demand for professionals who can provide accurate and fast transcription and translation services is rapidly growing. This course prepares you to meet that demand head-on. Learning Outcomes: Gain foundational knowledge in Transcription basics. Understand the role of context in Transcription. Attain skills to improve Transcription accuracy. Master the tools used in Transcription and Translation. Acquaint yourself with the translation industry's dynamics. Develop strategies for effective translation. More Benefits: LIFETIME access Device Compatibility Free Workplace Management Toolkit Key Modules from Data Entry Administrator Diploma with Transcription and Translation Diploma: Basics of Transcription: Begin your journey by understanding the fundamentals of Transcription, the cornerstone for developing specialised skills in the field. Context in Transcription: Here, you'll delve into the nuances of context, learning how it influences the accuracy and effectiveness of Transcription. Transcription Accuracy: Accuracy is paramount in Transcription. This module will equip you with techniques to enhance your Transcription accuracy. Tools in Transcription: This module introduces you to the tools integral to Transcription, from software to hardware, to optimise your workflow. Translation Industry in the Realm of Transcription: An essential overview of the translation industry, explaining how Transcription skills can be effectively utilised within it. Translation Strategies Complementing Transcription: Finalize your training by learning the best strategies for translating content in a manner that complements your Transcription skills.

This course covers the important topics needed to pass the AWS Certified Data Analytics-Specialty exam (AWS DAS-C01). You will learn about Kinesis, EMR, DynamoDB, and Redshift, and get ready for the exam by working through quizzes, exercises, and practice exams, along with exploring essential tips and techniques.

Criminal Intelligence: Criminal Intelligence Analyst Course Online Enhance Your Career as a Criminal Intelligence: Criminal Intelligence Analyst Course with Our Comprehensive Course! Are you passionate about fighting crime and uncovering hidden patterns? Do you have a keen eye for detail and a strong analytical mindset? If so, our Criminal Intelligence: Criminal Intelligence Analyst Course is the perfect opportunity for you to unlock a rewarding and exciting career in law enforcement! As crime becomes more sophisticated and complex, the demand for highly skilled Criminal Intelligence: Criminal Intelligence Analyst Course is at an all-time high. This comprehensive Criminal Intelligence: Criminal Intelligence Analyst Course is designed to equip you with the knowledge and skills necessary to excel in this dynamic field. Whether you're a recent graduate or a seasoned professional looking to transition into a new role, our Criminal Intelligence: Criminal Intelligence Analyst Course offers the perfect blend of theoretical knowledge and practical training to help you succeed. Our Criminal Intelligence: Criminal Intelligence Analyst Course curriculum has been meticulously crafted by industry experts with extensive experience in criminal : criminal intelligence analysis. Through this Criminal Intelligence: Criminal Intelligence Analyst Course you will gain a deep understanding of the principles and techniques used in gathering, analyzing, and interpreting data to support investigations and solve complex criminal cases. From mastering the fundamentals of criminal law to learning advanced data analysis methodologies, our Criminal Intelligence: Criminal Intelligence Analyst Course covers it all. Main Course: Criminal Intelligence Analyst Course Free Course: Course 01: Forensic Science [ Note: Free PDF certificate as soon as completing the Criminal Intelligence: Criminal Intelligence Analyst Course] Criminal Intelligence: Criminal Intelligence Analyst Course Online This Criminal Intelligence: Criminal Intelligence Analyst Course consists of 09 modules. Assessment Method of Criminal Intelligence: Criminal Intelligence Analyst Course After completing Criminal Intelligence: Criminal Intelligence Analyst Course, you will get quizzes to assess your learning. You will do the later modules upon getting 60% marks on the quiz test. Apart from this, you do not need to sit for any other assessments. Certification of Criminal Intelligence: Criminal Intelligence Analyst Course After completing the Criminal Intelligence: Criminal Intelligence Analyst Course, you can instantly download your certificate for FREE. The hard copy of the certification will also be delivered to your doorstep via post, which will cost £13.99. Who is this course for? Criminal Intelligence: Criminal Intelligence Analyst Course Online This Criminal Intelligence: Criminal Intelligence Analyst Course is open to everyone. Requirements Criminal Intelligence: Criminal Intelligence Analyst Course Online To enrol in this Criminal Intelligence: Criminal Intelligence Analyst Course, students must fulfil the following requirements: Good Command over English language is mandatory to enrol in our Criminal Intelligence: Criminal Intelligence Course. Be energetic and self-motivated to complete our Criminal Intelligence: Criminal Intelligence Course. Basic computer Skill is required to complete our Criminal Intelligence: Criminal Intelligence Course. If you want to enrol in our Criminal Intelligence: Criminal Intelligence Analyst Course, you must be at least 15 years old. Career path Criminal Intelligence: Criminal Intelligence Analyst Course Online

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

Anti-Money Laundering (AML): This comprehensive Anti-Money Laundering (AML) course is designed for individuals interested in safeguarding organizations against financial crime. Through up-to-date knowledge and practical guidance, you will be trained in the essential skills required to identify and prevent suspicious activities within any organization. With step-by-step instructions on conducting client risk assessments, this course equips you with a strong foundation in AML procedures.

Overview This comprehensive course on Interactive Dashboards with Data Studio will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Interactive Dashboards with Data Studio 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 Interactive Dashboards with Data Studio. It is available to all students, of all academic backgrounds. Requirements Our Interactive Dashboards with Data Studio 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 2 sections • 5 lectures • 02:41:00 total length •Module 01: Introduction to GDS: 00:36:00 •Module 02: Data Visualization: 01:29:00 •Module 03: Geo-visualization: 00:16:00 •Module 04: A Socio-Economic Case Study: 00:20:00 •Assignment - Interactive Dashboards with Data Studio: 00:00:00

Prepare for success with the Microsoft PL-900 Certification Course, covering the fundamentals of Power Platform, including Power BI, Power Apps, Power Automate, Power Virtual Agents, and related topics such as Dataverse, AI Builder, Connectors, Dynamics 365, Teams, Security, and Administration. Suitable for beginners with no prerequisites.

Overview With the ever-increasing demand for Google Data Studio in personal & professional settings, this online training aims at educating, nurturing, and upskilling individuals to stay ahead of the curve - whatever their level of expertise in Google Data Studio may be. Learning about Google Data Studio or keeping up to date on it can be confusing at times, and maybe even daunting! But that's not the case with this course from Compete High. We understand the different requirements coming with a wide variety of demographics looking to get skilled in Google Data Studio . That's why we've developed this online training in a way that caters to learners with different goals in mind. The course materials are prepared with consultation from the experts of this field and all the information on Google Data Studio is kept up to date on a regular basis so that learners don't get left behind on the current trends/updates. The self-paced online learning methodology by compete high in this Google Data Studio course helps you learn whenever or however you wish, keeping in mind the busy schedule or possible inconveniences that come with physical classes. The easy-to-grasp, bite-sized lessons are proven to be most effective in memorising and learning the lessons by heart. On top of that, you have the opportunity to receive a certificate after successfully completing the course! Instead of searching for hours, enrol right away on this Google Data Studio course from Compete High and accelerate your career in the right path with expert-outlined lessons and a guarantee of success in the long run. Who is this course for? While we refrain from discouraging anyone wanting to do this Google Data Studio course or impose any sort of restrictions on doing this online training, people meeting any of the following criteria will benefit the most from it: Anyone looking for the basics of Google Data Studio , Jobseekers in the relevant domains, Anyone with a ground knowledge/intermediate expertise in Google Data Studio , Anyone looking for a certificate of completion on doing an online training on this topic, Students of Google Data Studio , or anyone with an academic knowledge gap to bridge, Anyone with a general interest/curiosity Career Path This Google Data Studio course smoothens the way up your career ladder with all the relevant information, skills, and online certificate of achievements. After successfully completing the course, you can expect to move one significant step closer to achieving your professional goals - whether it's securing that job you desire, getting the promotion you deserve, or setting up that business of your dreams. Course Curriculum Module 01 Introduction to Google Data Studio Introduction to Google Data Studio 00:00 Module 02 Why Use Google Data Studio Over Others Why Use Google Data Studio Over Others 00:00 Module 03 How to Use Google Data Studio How to Use Google Data Studio 00:00 Module 04 Dashboard In Google Data Studio Dashboard In Google Data Studio 00:00 Module 05 How to Create Report In Google Data Studio How to Create Report In Google Data Studio 00:00 Module 06 Additional Features in Google Data Studio Additional Features in Google Data Studio 00:00 Module 07 Google Data Studio Calculated Formula and Functions Google Data Studio Calculated Formula and Functions 00:00 Module 08 Google Data Studio Data Blending and Sharing Reports Google Data Studio Data Blending and Sharing Reports 00:00 Module 09 Ways to Share Google Data Studio Reports Ways to Share Google Data Studio Reports 00:00

Are you someone wishing to learn the skills to analyze data professionally using Power BI? Do you want to learn to create impressive reports? Then this is the right course for you! Learn the complete workflow in Power BI from A-to-Z and advanced analytics with DAX-calculated columns and measures, forecasting, and advanced analytics.