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Data mining using Advanced Excel functions is a great resource for every data engineer and ML modeler to learn and master in 2021. This article is focused on how to use advanced Excel course resources to master data mining. 

The failure of AI ML models have become the talk of the technology world, especially when the expectations are so heavy and the results so light! According to a leading technology journal on AI ML and Data Science trends, nearly 90% of the Machine Learning models fail in their first 50 programs. The chances of getting 100% accurate results in the first project are almost nil — 0.01 percent. That puts the ball on the data engineer’s side who is working on the AI ML algorithms.  What can be done to reduce the churn and ensure AI ML models are far more accurate?

Experts say data mining using Excel workbooks could be a verifiable answer for us to look into. For example, starting to data-mine using Linear Regression is a good answer. Advanced Excel courses are, therefore, back in action!

Linear Regression modeling works great when all your data points are neatly organized in a tabular form in Rows and Columns. Powerful data chart tools can then be deployed to measure the variance of data and create a Scatter Plot chart to show the distribution of maxim and minim of data, delivering what we call a “Trend” through the scatter plot. Once we have set a trend for a small set of data, training your ML model to follow the trend line for the rest of the data points becomes easier. You can either automate the result derivation or simply put it on the supervision of another ML model that removes anomalies off the chart.

Data Augmentation 

If you have worked with Big Data on Excel before, you can understand how important it is to make full use of the data points without losing their values/redundancy quotient. For the performance sake, we should focus on Data Augmentation at each step of data mining and therefore use ML models to generalize the different sets of values into primary and secondary worksheets. Data augmentation mostly works for images and texts, but when you have CNN models in place, you can also apply the concept to numerical values and linear regression charts. TensorFlow allows you to use Data Augmentation, and with a bit of practice and a lot of experimentation with the data models, you should be able to pull the right strings within the advanced excel course as well.

With PyXLL

Python “in Excel” sounds so exciting, isn’t it! We have an automated interface for Python in Excel training courses.

Python is a very formidable data science open-source coding language. If you want to get ahead with the Advanced excel course, python training with ML is a very useful resource. In most ML courses, learning with Python is the basics, and hence considered important by BI software developers who have foundations in Excel software. Companies have invested billions in ML with Python codes that have opened up new avenues for PyXLL coders. Using PyXLL, BI developers can use Excel Add-Ins into Python syntax and extend the benefits of both Python language and Excel workbooks into ML programming.

Data Analysis

Automation of data analysis has been the bone of contention as far as BI / BA teams were concerned for a large part of the last decade. Wondering — if we can use Machine learning to skim the Excel database? Yes, it’s possible and some ML Ops teams are already at it.

Let’s say you are working with an Excel database that has thousands of open-ended queries and answers with no major formatting done to arrange these for classification or regression models to satisfactorily work with. Getting insights from a disrupted batch of inputs is detrimental to an Excel database health. This is where AutomML comes into the picture.

Developers are using advanced ML models to classify text and numerical to assign a piece of value or numerical to classify text/content. Text analysis classification and image processing are two popular techniques that are begin used in data analysis in a Big Excel database.

Other techniques include Topic Classification, Sentiment Analysis, and Convoluted Neural Networking models to sift through survey responses, tags, and so on and grab a quick first hand-glance of their arrangement in Excel Database format.

Without ML training, it’s hard to advance in an Excel course and vice versa.


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