T06 – Missing out on beliefs system Insight in Tamil how to Tool learning course free ( Statistics Technology )

T06 - Missing values strategy Intuition in Tamil - Machine learning course free ( Data Science )

*For the playlist , please click the below Link:

*Please click the following link to download the dataset: https://drive.google.com/file/d/10DbrdE0RTG8KMkdiOPu-7r1cgBOfhcyi/view?usp=sharing

*Visit Our website : https://datasciencealive.wordpress.com/machine-learning/

*In this session we will look into strategy used in Handling missing values on the data preprocessing techniques ( Tamil ) using pandas in python .

We will use the following strategy for handling the missing values
1. Mean = Average
2.Median = Middle value
3.Mode ( categorical data and continues values) = Most Frequent values

In machine learning ( Tamil ) most of the time will be spend on data preprocessing , data mining and feature extraction . Hence please listen to this topic more carefully .

*This is a Data science course ( Tamil ). This is a full fledge course for free and we will cove all the main topics on the machine learning algorithm. This course is specifically designed to address all the queries from beginners to expert . Artificial intelligence ( AI ) is a bigger umbrella ,In that Machine learning ( ML ) and Deep Learning ( DL ) are part of Artificial Intelligence.

*In this video we will have an overview on the topics that will be covered. On high level it will be

*Data Preprocessing
*Supervised Learning – Algorithm
*Unsupervised learning – Algorithm
*Dimensionality Reduction (PCA)
*Semi -Supervised learning
*Re- Enforcement learning
*Best approach for Model selection
*Intro to Deep Learning

The above topics will be covered in-detail on the upcoming session which you can find it in the below playlist .

*For the playlist , please click the below Link:

#Data_science_tamil #Missing_values #Machine_learning_Python_tamil

Link to the code: https://github.com/mGalarnyk/Python_Tutorials/blob/master/Time_Series/Part1_Time_Series_Data_BasicPlotting.ipynb

Viewing Pandas DataFrame, Adding Columns in Pandas, Plotting Two Pandas Columns, Sampling Using Pandas, Rolling mean in Pandas (Smoothing), Subplots, Plotting against Date (numpy.datetime), Filtering DataFrame in Pandas, Simple Joins, and Linear Regression.

This tutorial is mostly focused on manipulating time series data in the Pandas Python Library.

Gretl Particular tutorial six: Fashion and Conjecturing Moment in time Show Statistics

In this video we run a linear regression on a time series dataset with time trend and seasonality dummies. Then, we perform and evaluate the accuracy of an in-sample forecast, as well as perform an out-of-sample (i.e., into the future) forecast.

00:00 Introduction
00:12 What we will do in this Video
00:40 Data
01:14 Glimpse Data in Excel
01:46 Load Data in Gretl
03:20 Plot Time Series
03:54 Create Additional Variables
04:38 Run Model with All Data
05:34 In-Sample Forecast
06:40 Evaluating Quality of In-Sample Forecast
10:37 Out-of-Sample Forecast

Stock exchange prophecy technique | MATLAB | System Discovering | Instantaneous Statistics Breakdown

Hello friends today I’m going to show you how the stock market prediction system works and how machine learning helps you to get the exact estimation of the stock market.

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