Kumarans Data Academy

As a Data Professional, Instead of showing data in a sentence or table, which is hard to read, You can show Data using suitable graph in Excel, Power BI, Tableau Etc. for better understanding  Column Chart Column Chart is made of Vertical Bars. Each Bar shows Each value.  Column Chart is mostly used to show the performance of an Employee every year, every month, etc... You can also use this chart to show Sales, Quantity, Improvement by time period. It works best if you have X axis = Timeline & Y axis = Quantity / Value. Bar Chart: Bar Chart is made of Horizontal Bars. Each Bar shows Value of Each Category. You can use this Chart to show Data by Category Like Products: TV, Mobile, Fridge. It helps analyze which is a Top Category and which is a Lowest Category. It works best if you have X axis = Value & Y axis = Category. Line Chart: It is used to show Trends over time. Internet Service provider Companies can check how the Internet speed in your area changes every Second...

  Data Science மற்றும் Machine Learningல் நாம நிறைய Numbers மற்றும் Dataset-களை  கையாளுகிறோம். Dataset என்றால், பல Rows மற்றும் Columns கொண்ட Table மாதிரியான Structure. Matrix உபயோகிப்பதால், பெரிய அளவிலான Calculationகளை வேகமாகவும் எளிமையாகவும் கணக்கிடலாம். தரவுகளை சேமிப்பது நீங்கள் காணும் எந்த Dataset-யும் (Excel file, CSV, SQL table) எல்லாமே ஒரு Matrix மாதிரி Structure தான். Rows = Records (Row 1, 2, 3, …) Columns = Features or Variables (Column A, B, C, …) ஒரு Column-ல் இருக்கும் Headings: Name, Subject, Employee ID, Attendance, Height, Weight இதெல்லாம் Features. ஒரு Row-ல் இருக்கும் Values: Akash, Ajith, Maths, English, 5 feet, 6 feet — இவை எல்லாம் Values. இது தான் Data சேமிக்க மிக சிறந்த முறை .  மனிதர்களுக்கும் Computerகும் சமமாக புரியும். Image Processing Digital Images எல்லாம் Matrixஸாக சேமிக்கப்படும். ஒவ்வொரு Pixel-க்கும் ஒரு Matrix Value இருக்கும், அது Computerகு அந்த Pixel-ல் என்ன Color, density காட்டணும் என்பதை சொல்லி தரும். Black & White படம் → ...

Uses of Matrices in Data Science and Machine Learning In data science and machine learning, we deal with a lot of numbers and datasets ( dataset means Collection of Multiple tables). Matrices help us to handle large numbers easily and quickly. Here are the main uses of matrices: Storing Data Any dataset you see (Excel table, CSV file, SQL table) is like a matrix. Rows = records or samples. ( Row 1, 2, 3, etc.. ) Columns = features or variables. ( Column A, B, C etc...) Column contains Name, Subject, Employee ID, Attendance, Department, Height, Weight - Such Headings. So Columns contain Features. Row contains the List of Names, like Akash, Ajith etc.. List of Subject like Maths, English etc.. and Height values like 5 feet, 6 feet etc..  So Row Contains Values. This method of Storing Data is Best for Everything! (Both Humans and Computers) Ima ge Processing Digital Images are stored as matrices. Each Matrix Numbers represent Color or density of a Single Pixel of image. For example, ...

  Feature Scaling (Min-Max Scaling, Standardization) Linear Transformation is used to scale numerical data so that all features fall within the same range. Example:  Bringing sales figures from ₹1,000 – ₹1,00,000 into a range of 0 to 1 using Min-Max Scaling Formula to help maching learning models treat all dimensions equally. This Ensures Machine Learning Models can Handle All Dimensions Equally. Bringing student marks from 0 – 100 into a range of 0 to 1 using the Min-Max Scaling formula to help machine learning models treat all Subjects equally. Normalization It is used to change values that are on different scales into the same scale without changing the meaning or difference between them. Making Different Data types understandable and in Common Format. However this does not affect the Values, but only the data type changes to Common Format. So Computer can process the Information Equally. Example: Normalizing Age, Income, and Expenses for fair comparison using Min-Max Sc...