6 Natural Language Processing (NLP) courses

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This course will allow you to explore the potential of self-service business intelligence using Power BI Desktop to analyse and connect to different sources of data, creating Relationships between those different datasets, Query the data using Shaping and data Modelling, to create Visualizations, and publish Reports to different platforms . Course Objectives At the end of this course you will be able to: Connect to data from different sources. Use the Query Editor Perform Power BI desktop data Shaping and Transformation. Create Power BI desktop Modelling. Create Power BI desktop Visualizations and Reports. ' 1 year email support service Take a closer look at the consistent excellent feedback from our growing corporate clients visiting our site ms-officetraining co uk With more than 20 years experience, we deliver courses on all levels of the Desktop version of Microsoft Office and Office 365; ranging from Beginner, Intermediate, Advanced to the VBA level and Business Intelligence. Our trainers are Microsoft certified professionals with a proven track record with several years experience in delivering public, one to one, tailored and bespoke course Tailored training courses: in in company training, you can choose to run the course exactly as they are outlined by us or we can customise it so that it meets your specific needs. A tailored or bespoke course will follow the standard outline but may be adapted to your specific organisational needs. Please visit our site (ms-officetraining co uk) to get a feel of the excellent feedback our courses have had and look at other courses you might be interested in. Introduction to Power BI Power BI Jargon explained A quick look at Power BI Desktop A quick look at the Power BI service Helpful resources Power BI and Excel Introduction to using Excel data in Power BI Upload Excel data to Power BI Import Power View and Power Pivot to Power BI Getting started with Power BI Desktop Overview of Power BI Desktop Accessing Help and Helpful resources Connect to data sources in Power BI Desktop Shaping and Transforming Data with Query Editor Introduction to the Query Editor Data Sources Power BI Desktop can Connect to Introduction to Steps and M code Combining Data Using Merge and Append Queries Data Type Properties Working with Delimiters Clean and transform your data with the Query Editor Text Specific Transformation Tools Number Specific Transformation Tools Date Specific Transformation Tools Split and Merge columns Creating an Index Column Adding Conditional Columns Columns From Examples Grouping and Aggregating data Pivoting and Unpivoting Using filters Modeling the data Introduction to modeling your data How to manage your data relationships Create calculated columns Optimizing data models Create calculated measures Show Values As and Quick Measures Create calculated tables Explore your time-based data Introduction to DAX DAX calculation types DAX functions Visualizations Introduction to visuals in Power BI Create and customize simple visualizations Modify colors in charts and visuals Shapes, text boxes, and images Page layout and formatting Group interactions among visualizations Visual hierarchies and drill-down Using custom visualizations Create a KPI Visualization Geo-Data and Maps Reports, Publishing and Sharing Introduction to the Power BI service Quick insights in Power BI Create and configure a dashboard Ask questions of your data with natural language Create custom Q&A suggestions Share dashboards with your organization Introduction to content packs, security, and groups Publish Power BI Desktop reports Print and export dashboards and reports Create groups in Power BI Use content packs Update content packs Publish to web Who is this course for? Who is this course for? This course facilitates you with knowledge on the potential for Power BI Desktop to analyse and connect to different sources of data, creating Relationships between those different datasets, Query the data using Shaping and data Modelling, and to create Visualisations, and publish Reports to different platforms. Requirements Requirements Before attending this course, delegates should have: - A general knowledge of database concepts (fields, records and relationships) - Familiarity with Excel. Career path Career path Business Intelligence Data Analysis ETL & Data Warehousing

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

This course will allow you to explore the potential of self-service business intelligence using Power BI Desktop to analyse and connect to different sources of data, creating Relationships between those different datasets, Query the data using Shaping and data Modelling, to create Visualizations, and publish Reports to different platforms . Course Objectives At the end of this course you will be able to: Connect to data from different sources. Use the Query Editor Perform Power BI desktop data Shaping and Transformation. Create Power BI desktop Modelling. Create Power BI desktop Visualizations and Reports. ' 1 year email support service Take a closer look at the consistent excellent feedback from our growing corporate clients visiting our site ms-officetraining co uk With more than 20 years experience, we deliver courses on all levels of the Desktop version of Microsoft Office and Office 365; ranging from Beginner, Intermediate, Advanced to the VBA level and Business Intelligence. Our trainers are Microsoft certified professionals with a proven track record with several years experience in delivering public, one to one, tailored and bespoke courses. Our competitive rates start from £550.00 per day of training Tailored training courses: You can choose to run the course exactly as they are outlined by us or we can customise it so that it meets your specific needs. A tailored or bespoke course will follow the standard outline but may be adapted to your specific organisational needs. Please visit our site (ms-officetraining co uk) to get a feel of the excellent feedback our courses have had and look at other courses you might be interested in. Introduction to Power BI Power BI Jargon explained A quick look at Power BI Desktop A quick look at the Power BI service Helpful resources Power BI and Excel Introduction to using Excel data in Power BI Upload Excel data to Power BI Import Power View and Power Pivot to Power BI Getting started with Power BI Desktop Overview of Power BI Desktop Accessing Help and Helpful resources Connect to data sources in Power BI Desktop Shaping and Transforming Data with Query Editor Introduction to the Query Editor Data Sources Power BI Desktop can Connect to Introduction to Steps and M code Combining Data Using Merge and Append Queries Data Type Properties Working with Delimiters Clean and transform your data with the Query Editor Text Specific Transformation Tools Number Specific Transformation Tools Date Specific Transformation Tools Split and Merge columns Creating an Index Column Adding Conditional Columns Columns From Examples Grouping and Aggregating data Pivoting and Unpivoting Using filters Modeling the data Introduction to modeling your data How to manage your data relationships Create calculated columns Optimizing data models Create calculated measures Show Values As and Quick Measures Create calculated tables Explore your time-based data Introduction to DAX DAX calculation types DAX functions Visualizations Introduction to visuals in Power BI Create and customize simple visualizations Modify colors in charts and visuals Shapes, text boxes, and images Page layout and formatting Group interactions among visualizations Visual hierarchies and drill-down Using custom visualizations Create a KPI Visualization Geo-Data and Maps Reports, Publishing and Sharing Introduction to the Power BI service Quick insights in Power BI Create and configure a dashboard Ask questions of your data with natural language Create custom Q&A suggestions Share dashboards with your organization Introduction to content packs, security, and groups Publish Power BI Desktop reports Print and export dashboards and reports Create groups in Power BI Use content packs Update content packs Publish to web Who is this course for? Who is this course for? This course facilitates you with knowledge on the potential for Power BI Desktop to analyse and connect to different sources of data, creating Relationships between those different datasets, Query the data using Shaping and data Modelling, and to create Visualizations, and publish Reports to different platforms. Requirements Requirements Before attending this course, delegates should have: - A general knowledge of database concepts (fields, records and relationships) - Familiarity with Excel. Career path Career path Business Intelligence Data Analysis ETL & Data Warehousing

In the past, popular thought treated artificial intelligence (AI) as if it were the domain of science fiction or some far-flung future. In the last few years, however, AI has been given new life. The business world has especially given it renewed interest. However, AI is not just another technology or process for the business to consider - it is a truly disruptive force.

Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune