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Welcome to the world of machine learning, Java, and Python programming! This exciting bundle offers a comprehensive learning experience with a variety of courses that will equip you with the knowledge and skills needed to excel in these fields. Within this bundle, you will find a total of eight courses, including three QLS-endorsed courses and five relevant CPD QS-accredited courses. The three QLS-endorsed courses in this bundle are the Machine Learning with Python course, the Introduction to Java course, and the Diploma in Python Programming. These courses have been carefully curated and designed to provide you with a solid foundation in machine learning and programming using both Java and Python. Upon completion of these courses, you will receive hardcopy certificates to showcase your achievement. In addition to the QLS-endorsed courses, this bundle also includes five CPD QS-accredited courses. These courses are highly relevant to the field and will further enhance your understanding and expertise. They include Azure Machine Learning, Complete Python Machine Learning & Data Science Fundamentals, Machine Learning Model Using AWS SageMaker Canvas, Statistics & Probability for Data Science & Machine Learning, and 2021 Data Science & Machine Learning with R from A-Z. Key Features of the Machine Learning, Java and Python Programming Bundle: 3 QLS-Endorsed Courses: We proudly offer 3 QLS-endorsed courses within our Machine Learning, Java and Python Programming bundle, providing you with industry-recognized qualifications. Plus, you'll receive a free hardcopy certificate for each of these courses. QLS Course 01: Machine Learning with Python QLS Course 02: Introduction to Java QLS Course 03: Diploma in Python Programming 5 CPD QS Accredited Courses: Additionally, our bundle includes 5 relevant CPD QS accredited courses, ensuring that you stay up-to-date with the latest industry standards and practices. Course 01: Azure Machine Learning Course 02: Complete Python Machine Learning & Data Science Fundamentals Course 03: Machine Learning Model Using AWS SageMaker Canvas Course 04: Statistics & Probability for Data Science & Machine Learning Course 05: 2021 Data Science & Machine Learning with R from A-Z In Addition, you'll get Five Career Boosting Courses absolutely FREE with this Bundle. Course 01: Professional CV Writing Course 02: Job Search Skills Course 03: Self-Esteem & Confidence Building Course 04: Professional Diploma in Stress Management Course 05: Complete Communication Skills Master Class Convenient Online Learning: Our Machine Learning, Java and Python Programming courses are accessible online, allowing you to learn at your own pace and from the comfort of your own home. Learning Outcomes: Gain a comprehensive understanding of machine learning principles and algorithms. Develop proficiency in programming with Python and Java, and apply these skills to real-world projects. Explore the fundamentals of data science and statistical analysis. Learn how to leverage cloud-based platforms, such as Azure and AWS, for machine learning tasks. Build and deploy machine learning models using various frameworks and tools. Acquire practical knowledge in data preprocessing, feature selection, and model evaluation techniques. Gain expertise in using R for data science and machine learning applications. This bundle combines the power of machine learning, Java programming, and Python programming to provide you with a comprehensive skill set in these high-demand fields. With a total of eight courses, including three QLS-endorsed and five CPD QS-accredited courses, this bundle covers all the essential topics and techniques needed to succeed. Whether you are a beginner looking to enter the world of machine learning and programming or an experienced professional seeking to expand your knowledge and skills, this bundle has something for everyone. From understanding the fundamentals to applying advanced algorithms, you will gain a deep understanding of machine learning principles and the ability to develop robust applications using Java and Python. CPD 250 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Individuals interested in starting a career in machine learning, Java programming, or Python programming. Professionals seeking to enhance their skills and stay updated with the latest trends in these fields. Students pursuing degrees in computer science, data science, or related disciplines. Anyone looking to broaden their knowledge and explore the exciting field of machine learning and programming. Career path Machine Learning Engineer: £45,000 - £90,000 per year Data Scientist: £40,000 - £80,000 per year Java Developer: £30,000 - £70,000 per year Python Developer: £35,000 - £75,000 per year Cloud Solutions Architect: £50,000 - £100,000 per year Statistical Analyst: £35,000 - £65,000 per year Certificates Digital certificate Digital certificate - Included Hard copy certificate Hard copy certificate - Included

I Asked A Python Programmer For A Joke. He Said, 'Import Antigravity' | 10 QLS Endorsed Courses for Python Programmer | 10 QLS Endorsed Hard Copy Certificates Included | Lifetime Access | Installment Payment | Tutor Support

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

This course is designed for beginners, although we will go deep gradually, and is a highly focused course designed to master your Python skills in probability and statistics, which covers the major part of machine learning or data science-related career opportunities.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.

Get ready for an exceptional online learning experience with the Data Analyst (Data Analytics)bundle! This carefully curated collection of 30 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations. The Data Analyst (Analytics) is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Data Analyst (Analytics) package has something for everyone. As part of the Data Analyst (Analytics) package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analyst (Analytics) bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth! This Bundle Comprises the Following Data Analyst (Data Analytics)CPD Accredited Courses: Course 01: Basic Data Analysis Course 02: Business Data Analysis Course 03: Introduction to Data Analytics with Tableau Course 04: Google Data Studio: Data Analytics Course 05: SQL Database Basics for Everyone Course 06: R Programming for Data Science Course 07: 2021 Data Science & Machine Learning with R from A-Z Course 08: Learn Python, JavaScript, and Microsoft SQL for Data science Course 09: Spatial Data Visualisation and Machine Learning in Python Course 10: Building Big Data Pipelines with PySpark MongoDB and Bokeh Course 11: Complete Python Machine Learning & Data Science Fundamentals Course 12: Clinical Data Management with SAS Programming Course 13: Certificate in Data Entry and Management Course 14: Quick Data Science Approach from Scratch Course 15: Web Mapping and Data Visualizations Course 16: Programming AutoCAD with SQL Server Database Using C# Course 17: Big Data Analytics with PySpark Power BI and MongoDB Course 18: Develop Big Data Pipelines with R & Sparklyr & Tableau Course 19: Develop Big Data Pipelines with R, Sparklyr & Power BI Course 20: Data Center Training Essentials: Power & Electrical Course 21: Business Intelligence and Data Mining Course 22: Set Menu Prices for your restaurant using data Course 23: Data Analysis In Excel Course 24: Data Protection Course 25: Reporting and Data Course 26: Career Development Plan Fundamentals Course 27: CV Writing and Job Searching Course 28: Networking Skills for Personal Success Course 29: Excel: Top 50 Microsoft Excel Formulas in 50 Minutes! Course 30: Decision Making and Critical Thinking What will make you stand out? Upon completion of this online Data Analyst (Data Analytics) bundle, you will gain the following: CPD QS Accredited Proficiency with this Data Analyst (Analytics) bundle After successfully completing the Data Analyst (Analytics) bundle, you will receive a FREE CPD PDF Certificates as evidence of your newly acquired abilities. Lifetime access to the whole collection of learning materials of this Data Analyst (Analytics) bundle The online test with immediate results You can study and complete the Data Analyst (Analytics) bundle at your own pace. Study for the Data Analyst (Analytics) bundle using any internet-connected device, such as a computer, tablet, or mobile device. Each course in this Data Analyst (Analytics) bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Data Analyst (Analytics), a rich anthology of 30 diverse courses. Each course in the Data Analyst (Analytics) bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisData Analyst (Analytics) bundle will take you on a unique and enriching educational journey. The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Data Analyst (Analytics) bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analyst (Data Analytics) package your trusted companion in your lifelong learning journey. CPD 300 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Data Analyst (Data Analytics) bundle is perfect for: Lifelong learners looking to expand their knowledge and skills. Professionals seeking to enhance their career with CPD certification. Individuals wanting to explore new fields and disciplines. Anyone who values flexible, self-paced learning from the comfort of home. Requirements You are cordially invited to enroll in this bundle; please note that there are no formal prerequisites or qualifications required. We've designed this curriculum to be accessible to all, irrespective of prior experience or educational background. Career path Unleash your potential with the Data Analyst (Data Analytics) bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the Data Analyst (Analytics)bundle package. Certificates CPD Quality Standard Certificate Digital certificate - Included 30 CPD Quality Standard Certificates - Free

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

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In this course, you will quickly learn how to build DNNs (Deep Neural Networks) and how to train them. This learning-by-doing course will also help you master the elementary concepts and methodology with Python. You need to have a basic knowledge of python to get the best out of this course.

Do you want to prepare for your dream job but strive hard to find the right courses? Then, stop worrying, for our strategically modified Data Analysis and Data Tools bundle will keep you up to date with the relevant knowledge and most recent matters of this emerging field. So, invest your money and effort in our 30 course mega bundle that will exceed your expectations within your budget. The Data Analysis and Data Tools related fields are thriving across the UK, and recruiters are hiring the most knowledgeable and proficient candidates. It's a demanding field with magnitudes of lucrative choices. If you need more guidance to specialise in this area and need help knowing where to start, then StudyHub proposes a preparatory bundle. This comprehensive Data Analysis and Data Tools bundle will help you build a solid foundation to become a proficient worker in the sector. This Data Analysis and Data Tools Bundle consists of the following 25 CPD Accredited Premium courses - Course 1: Basic Data Analysis Course 2: Excel Data Analysis Course 3: 2021 Python Programming From A-Z: Beginner To Expert Course 4: Python Data Science with Numpy, Pandas and Matplotlib Course 5: 2021 Data Science & Machine Learning with R from A-Z Course 6: Mastering SQL Programming Course 7: Statistics & Probability for Data Science & Machine Learning Course 8: Quick Data Science Approach from Scratch Course 9: R Programming for Data Science Course 10: Learn Python, JavaScript, and Microsoft SQL for Data science Course 11: Google Data Studio: Data Analytics Course 12: Introduction to Excel Data Tools and Data Management Course 13: Data Center Training Essentials: General Introduction Course 14: Web Scraping and Mapping Dam Levels in Python and Leaflet Course 15: Microsoft Power BI - Master Power BI in 90 Minutes! Course 16: PowerBI Formulas Course 17: Business Intelligence and Data Mining Course 18: Financial Ratio Analysis for Business Decisions Course 19: Research Methods in Business Course 20: Cyber Security Incident Handling and Incident Response Course 21: Microsoft Azure 2017 Course 22: AZ-104: Microsoft Azure Administrator Course 23: Microsoft Azure Cloud Concepts Course 24: Advanced Excel Analytics Course 25: Microsoft Access Tables and Queries 5 Extraordinary Career Oriented courses that will assist you in reimagining your thriving techniques- Course 1:Career Development Plan Fundamentals Course 2:CV Writing and Job Searching Course 3:Interview Skills: Ace the Interview Course 4:Video Job Interview for Job Seekers Course 5:Create a Professional LinkedIn Profile Learning Outcome This tailor-made Data Analysis and Data Tools bundle will allow you to- Uncover your skills and aptitudes to break new ground in the related fields Deep dive into the fundamental knowledge Acquire some hard and soft skills in this area Gain some transferable skills to elevate your performance Maintain good report with your clients and staff Gain necessary office skills and be tech savvy utilising relevant software Keep records of your work and make a report Know the regulations around this area Reinforce your career with specific knowledge of this field Know your legal and ethical responsibility as a professional in the related field This Data Analysis and Data Tools Bundle resources were created with the help of industry experts, and all subject-related information is kept updated on a regular basis to avoid learners from falling behind on the latest developments. Certification After studying the complete training you will be able to take the assessment. After successfully passing the assessment you will be able to claim all courses pdf certificates and 1 hardcopy certificate for the Title Course completely free. Other Hard Copy certificates need to be ordered at an additional cost of •8. CPD 300 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Ambitious learners who want to strengthen their CV for their desired job should take advantage of the Data Analysis and Data Tools bundle! This bundle is also ideal for professionals looking for career advancement. Requirements To participate in this course, all you need is - A smart device A secure internet connection And a keen interest in Data Analysis and Data Tools Career path Upon completing this essential Bundle, you will discover a new world of endless possibilities. These courses will help you to get a cut above the rest and allow you to be more efficient in the relevant fields.