Linear Regression in Python paid course free. The main aim of this course is to provide a wide understanding of Linear Regression in python using a simple casestudy
- In this course you will be introduced to Linear Regression in Python, Importing Libraries, Graphical Univariate Analysis
- Learn Boxplot, Linear Regression Boxplot, Linear Regression Outliers, Bivariate Analysis, Machine Learning Base Run and Predicting Output
Linear Regression in Python Course Requirements
- In this course, there is a need for basic knowledge of Python programming. This is better to understand the concepts very quickly and easily.
Linear Regression in Python Course Description
Forecasting in general means to display, where this exactly is to display or predict future trends using previous or historical data as inputs to obtain an efficient and effective estimation from the predictive data. Forecasting models have different methods for different situations and evaluation procedures are also conducted.
Forecasting evaluation includes a procedure to be carried out in step by step that starts with testing of assumptions, testing data and methods, replicating outputs, and accessing outputs. There are three different types of forecasting which basic types of forecasting are: qualitative techniques, time series analysis and projection, and casual models. In this course you will be introduced to Linear Regression in Python, Importing Libraries, Graphical Univariate Analysis, Boxplot, Linear Regression Boxplot, Linear Regression Outliers, Bivariate Analysis, Machine Learning Base Run and Predicting Output.
Forecasting is very important and effective technique in various sectors like business, finance, banking, weather, etc which is important in conducting the production planning and management which will help in deciding what to produce and with what available resources to produce, hence this technique is considered as an independent component in the field of business, financial, etc which aids in taking correct decision by the management in the organization or companies.
Who this course is for:
- This course can be taken who are interested and want to explore more knowledge in data and analytics.
- Students or professionals can also undergo this training who is interested in developing their career or promoting themselves in the field of data science.
- Technical managers or software engineers or IT operators can also undergo this Training which will help them upgrade their skills related to time series
- This course is helpful for analysts who can easily analyze any huge data and apply the proper algorithm to predict the future trend in the obtained data
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