5473 Software courses in Leicester delivered Online

Duration 4 Days 24 CPD hours This course is intended for Primary audience: IT administrators, architects, and business leaders who already manage Nutanix clusters in the datacenter, but who would like more in-depth knowledge of Nutanix datacenter administration. Anyone seeking preparation for the Nutanix Platform Professional (NPP) Advanced certification (in development).Secondary audience: Managers and technical staff seeking more detailed information before making a purchase decision. Overview Protect intellectual property and company data to guarantee business continuity with a complete, advanced data protection strategy.Practice advanced datacenter management procedures using hands on labs.Get the most out of Nutanix systems by maximizing configuration and operation for peak efficiency.Validate new skills by preparing for and completing the Nutanix Platform Professional Advanced certification. The Nutanix AAPM Advanced Administration & Performance Management course is an advanced level training program for experienced Nutanix data center administrators, technicians, and support personnel.The course features comprehensive coverage of performance management for Nutanix clusters, including options for performance optimization, troubleshooting issues and tuning. Learn through hands-on labs to monitor system performance, advanced networking and storage to help optimize data center administration.Advanced Administration explains in detail how to use the major Acropolis services such as Acropolis Block Services (ABS) and Acropolis File Services (AFS). The course also explains how to define and manage assets and applications using Calm, including how to connect to clouds, automation of the Life Cycle Management (LCM) application, and how to implement and configure Self Service Portal and governance.Take advantage of Flash mode to improve system performance as well as how to effectively clone and delete VMs, move them between storage containers, and how to manage VMs (tagging, sizing, migration). Performance ManagementManaging Controller VM ServicesAdvanced Virtual Machine AdministrationImplementing Business Continuity/Disaster RecoveryConfiguring Advanced NetworkingCustomizing Security ServicesManaging Acropolis ServicesPrism Central Management

Duration 2 Days 12 CPD hours This course is intended for This is an advanced course for DBAs and technical individuals who plan, implement, and maintain Db2 11.1 databases Overview Please refer to course overview This course is designed to teach you how to:Perform advanced monitoring using the Db2 administrative views and routines in SQL queries.Manage the disk space assigned in Database Managed Storage (DMS) and Automatic Storage table spaces, including the activities of the rebalancer.Use SQL queries and Db2 commands to check the high water mark on table spaces and to monitor the rebalance operation.Utilize the REBUILD option of RESTORE, which can build a database copy with a subset of the tablespaces using database or tablespace backup images.Plan and execute the TRANSPORT option of RESTORE to copy schemas of objects between two Db2 databases.Create incremental database or tablespace level backups to reduce backup processing and backup image storage requirements.Implement automatic storage management for table spaces and storage groups or enable automatic resize options for DMS managed table spaces to reduce administration requirements and complexity.Describe the various types of database memory including buffer pools, sort memory, lock memory and utility processing memory.Adjust database or Db2 instance configuration options to improve application performance or processing efficiency.Implement Db2 Self Tuning Memory management for specific database memory areas. Advanced MonitoringDb2 Table Space ManagementDb2 Database Memory ManagementDatabase rebuild supportDb2 database and tablespace relocationDb2 Incremental Backup

Duration 2 Days 12 CPD hours This course is intended for NetApp Customers, IT Generalists, Academic Alliance Students Overview Explain ONTAP operation system, Use the CLI and OnCommand System Manager to identify storage components, configure storage systems and storage virtual machines for NAS and SAN client access, create FlexVol volumes, qtrees, and LUNs, manage snapshot copies Introduces introductory concepts covered through instructor led discussions and hands-on labs are how to create aggregates, virtual interfaces, snapshots, volumes, qtrees, and storage virtual machines. Getting Started with Data ONTAP List basic storage concepts such as aggregates, RAID groups, volumes, qtrees, and LUNs Describe Data ONTAP features such as Snapshot copies, unified storage, and storage efficiency Describe the similarities and differences between the 7-Mode and clustered Data ONTAP operating systems Use the CLI and GUI for administrative purposes Hardware Basics Describe the NetApp storage system hardware platforms and the types of disks that they support Describe the hardware components of NetApp storage controllers Use OnCommand System Manager or the CLI to identify hardware components in Data ONTAP operating in 7-Mode and the clustered Data ONTAP operating system Creating & Managing Aggregates Describe aggregates and RAID groups Create aggregates in Data ONTAP operating in 7-Mode Create aggregates in the clustered Data ONTAP operating system Manage aggregates Managing NAS Client Access Configure NAS client access in Data ONTAP operating in 7-Mode Configure data storage virtual machines (SVMs*) for NAS client access in clustered Data ONTAP Create FlexVol volumes and qtrees Managing SAN Client Connections Describe SAN protocol implementation in Data ONTAP operating in 7-Mode and the clustered Data ONTAP operating system Use OnCommand System Manager to create iSCSI-attached LUNs Use NetApp SnapDrive for Windows to create and format iSCSI-attached LUNs Access and manage a LUN from a Windows host Managing Volumes Explain the relationship between space guarantees, volumes, and aggregates Define thin provisioning and explain how it is used Define deduplication and describe the benefits that it provides Use OnCommand System Manager to set quotas Managing Snapshot Copies Define the function of Snapshot copies Create and delete a Snapshot copy Create Snapshot policies in the clustered Data ONTAP operating system Restore a volume from a Snapshot copy Create FlexClone volume clones that are backed by Snapshot copies Steps to Certification Recall the steps to NetApp Certification

Duration 4 Days 24 CPD hours This course is intended for Hadoop Developers Overview Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:How data is distributed, stored, and processed in a Hadoop clusterHow to use Sqoop and Flume to ingest dataHow to process distributed data with Apache SparkHow to model structured data as tables in Impala and HiveHow to choose the best data storage format for different data usage patternsBest practices for data storage This training course is the best preparation for the challenges faced by Hadoop developers. Participants will learn to identify which tool is the right one to use in a given situation, and will gain hands-on experience in developing using those tools. Course Outline Introduction Introduction to Hadoop and the Hadoop Ecosystem Hadoop Architecture and HDFS Importing Relational Data with Apache Sqoop Introduction to Impala and Hive Modeling and Managing Data with Impala and Hive Data Formats Data Partitioning Capturing Data with Apache Flume Spark Basics Working with RDDs in Spark Writing and Deploying Spark Applications Parallel Programming with Spark Spark Caching and Persistence Common Patterns in Spark Data Processing Spark SQL and DataFrames Conclusion Additional course details: Nexus Humans Developer Training for Spark and Hadoop 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 Developer Training for Spark and Hadoop 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.

Duration 4 Days 24 CPD hours This course is intended for Designed for individuals interested in obtaining information about the CBAP and CCBA exam process especially potential exam candidates interested in pursuing business analysis certification from IIBA in the next 3 to 6 months. Overview Review the 6 Knowledge Areas of the BABOK Guide and discuss the business analysis tasks performed in each. Understand the relationships between the business analysis processes and how each defines an important aspect of the business analysis discipline. Review key terms, business analysis techniques, and competencies important to business analysis. Discuss the 5 business analysis Perspectives presented in BABOK Guide, v3. Complete simulated exam questions to assess personal readiness for taking the exam. Obtain key tips and techniques for effectively preparing for and successfully completing the CBAP or CCBA exam. This course provides you with a clear and detailed understanding of the concepts covered within the CBAP© and CCBA© exams. You will gain valuable tips and techniques to help prepare, study, and assess your personal readiness. In addition, you will earn valuable professional development hours toward meeting the exam criteria. CBAP© and CCBA© Overview Discuss the benefits of professional certification Present the CBAP©/CCBA© eligibility requirements Explain the exam process Discuss the exam blueprints Understand the recertification process Introduction to BABOK© Guide v3 Define the purpose of A Guide to the Business Analysis Body of Knowledge© (BABOK© Guide) Present the structure/components of the BABOK© Guide Identify the six business analysis Knowledge Areas Discuss the supporting areas of the BABOK© Guide BABOK© Guide Key Concepts Define key concepts from the BABOK© Guide Present the Business Analysis Core Concept Model? Discuss the requirements classification scheme Explain Requirements and Designs Present the 5 Business Analysis Perspectives Business Analysis Planning and Monitoring Identify the 5 tasks in Business Analysis Planning and Monitoring Understand the work performed in each of these tasks Explain the significance of the outputs produced within this knowledge area Elicitation and Collaboration Identify the 5 tasks in Elicitation and Collaboration Understand the work performed in each of these tasks Explain the significance of the outputs produced within this knowledge area Requirements Life Cycle Management Identify the 5 tasks in Requirements Life Cycle Management Understand the work performed in each of these tasks Explain the significance of the outputs produced within this knowledge area Strategy Analysis Identify the 5 tasks in Strategy Analysis Understand the work performed in each of these tasks Explain the significance of the outputs produced within this knowledge area Requirements Analysis and Design Definition Identify the 6 tasks in Requirements Analysis and Design Definition Understand the work performed in each of these tasks Explain the significance of the outputs produced within this knowledge area Solution Evaluation Identify the 6 tasks in Solution Evaluation Understand the work performed in each of these tasks Explain the significance of the outputs produced within this knowledge area Underlying Competencies Describe and understand the importance of the underlying competencies (UCs) Understand how to prepare for questions about UCs on the exam Business Analysis Techniques More clearly understand the 50 techniques presented in the BABOK© Guide Understand how to study techniques for the exam Assess Your Readiness 1 hour mock-exam to assess personal readiness Strategies for Success Utilize 8 strategies for preparing for the CBAP© or CCBA© certification exams Develop study tools and a plan to assist you in preparing for the exams Understand the tools and resources available to help you be successful Wrap-up Take Your Questions Next Steps Additional course details: Nexus Humans BACP02 - Certified Business Analysis Professional (CBAP) Exam Preparation 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 BACP02 - Certified Business Analysis Professional (CBAP) Exam Preparation 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.

Duration 3 Days 18 CPD hours This course is intended for This intermediate course is for application programmers who need to write embedded SQL COBOL or PL/I programs in either a DB2 9 or DB2 10 for z/OS environment. Overview Incorporate static SQL statements in an application program Prepare the program for execution Validate execution results are correct Produce code to support multiple rows being returned from the database manager using cursors Identify considerations regarding units of work, concurrency, and restart of programs Identify differences between static and dynamic SQL Provide test data for applications Discuss program and DB2 options relative to performance of static SQL This course enables you to acquire the skills necessary to produce application programs that manipulate DB2 databases. Emphasis is on embedding Structured Query Language (SQL) statements and preparing programs for execution. CV720G;CF82G;DB2 Concepts Identify DB2 family products Explain DB2 workstation component functions Identify DB2 objects Identify the key differences between static SQL and other application alternatives for accessing DB2 data Program Structure I Embed INSERT, UPDATE, DELETE and single-row SELECT statements in application programs Effectively communicate with DB2 when processing NULL values and determining success of statement execution Demonstrate use of DB2 coding aids Code CONNECT statements within an application program Identify connection types and impacts on a unit of work Program for the Call Attach Facility (CAF) Program Preparation Identify the additional steps necessary to prepare a program that contains embedded SQL for execution Describe the functions of the DB2 PRECOMPILE and BIND processes Describe factors relevant to the BIND process, including RUNSTATS positioning, package status, parameters, and authorization requirements Program Structure II Use DECLARE, OPEN, FETCH, and CLOSE CURSOR statements to handle select criteria that may return multiple rows in application programs Issue positioned UPDATE and DELETE statements Identify how scrollable cursors can be used Recovery and Locking Concepts Define a unit of recovery Identify the basic locking strategies used by DB2 Dynamic SQL Introduction Describe the difference between static and dynamic SQL List the types of dynamic statements Code dynamic SQL in a program Managing Test Data Identify methods to insert data into a table Use the LOAD or IMPORT utility Identify the purpose of the RUNSTATS utility Identify the purpose of the REORG utility Performance Considerations Use programming techniques that enhance DB2 application performance by following general guidelines, using indexable predicates, and avoiding unnecessary sorts Identify the access paths available to DB2 List common causes of deadlocks and avoid such causes when possible Use the EXPLAIN tools as aids to develop applications that emphasize performance Additional course details: Nexus Humans CV722 IBM DB2 11 for z/OS Application Programming Workshop 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 CV722 IBM DB2 11 for z/OS Application Programming Workshop 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.

Artificial Intelligence (AI) is the most disruptive technology since the internet came onto the scene. AI is transforming every aspect of how we manage projects from developing a business case, to planning the work, managing risk, and tracking performance. Because the technology and market are moving so fast, it can be difficult to know how to start using AI on projects. Generative AI for Project Management will engage you with diverse Generative AI tools to start, plan, and manage either your own project or a generic case study. We will embrace a tool agnostic approach to adopting, integrating, and scaling Generative AI without compromising data or trust. You will have hands-on practice utilizing AI tools to optimize your time and your outcomes. You will be accessing a variety of AI tools requiring you to register for a free account. A computer is required for all traditional classroom deliveries. None At the end of this program, you will be able to: Define essential terms and concepts related to artificial intelligence (AI) Illustrate how prompts facilitate interaction with Generative AI Recognize the capabilities of Large Language Models Craft prompts to develop project origination documents Create prompts to assist in planning a project Develop user stories with Generative AI Analyze project performance using Generative AI Identify the limitations of Generative AI Identify the risks associated with using Generative AI Articulate the need for governance and ethics when establishing an AI program in an organization Course Overview Getting Started Foundation Concepts Understanding essential terms and concepts related to AI Exploring various Generative AI Models Understanding Prompts Creating Prompts for Project Startup Prompts for starting a project Prompts for planning a project Best Practices for prompt engineering Creating Prompts for Managing Projects Creating agile user stories Measuring project performance Analyzing a schedule Using Generative AI Responsibly Limitations of AI Models Establishing an AI governance framework Future trends and next steps Summary and Next Steps

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.