ML-capsule is a Project for beginners and experienced data science Enthusiasts who don't have a mentor or guidance and wish to learn Machine learning. Using our repo they can learn ML, DL, and many related technologies with different real-world projects and become Interview ready.
Machine learning technique to analysis data that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. ### Importance of Machine Learning Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.
Extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy
Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed. Python offers multiple great graphing libraries that come packed with lots of different features.
the process of selecting a subset of relevant features for use in model.Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.
A).Understand the Type of Analytics
Descriptive Analytics tells us what happened in the past and helps a business understand how it is performing by providing context to help stakeholders interpret information.
Diagnostic Analytics takes descriptive data a step further and helps you understand why something happened in the past.
Predictive Analytics predicts what is most likely to happen in the future and provides companies with actionable insights based on the information.
Prescriptive Analytics provides recommendations regarding actions that will take advantage of the predictions and guide the possible actions toward a solution
B). Probability
C). Central Tendency
D). Variability
E). Relationship Between Variables
F). Probability Distribution
G). Hypothesis Testing and Statistical Significance
H). Regression
Linear Regression ** Assumptions of Linear Regression
- Linear Relationship
- Multivariate Normality
- No or Little Multicollinearity
- No or Little Autocorrelation
- Homoscedasticity
Multiple Linear Regression
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
In business, the goal of data science is to provide intelligence about consumers and campaigns and help companies create strong plans to engage their audience and sell their products.
Data scientists must rely on creative insights using big data, the large amounts of information collected through various collection processes, like data mining.
On an even more fundamental level, big data analytics can help brands understand the customers who ultimately help determine the long-term success of a business or initiative. In addition to targeting the right audience, data science can be used to help companies control the stories of their brands.
Because big data is a rapidly growing field, there are constantly new tools available, and those tools need experts who can quickly learn their applications. Data scientists can help companies create a business plan to achieve goals based on research and not just intuition.
Data science plays a very important role in security and fraud detection, because the massive amounts of information allow for drilling down to find slight irregularities in data that can expose weaknesses in security systems.It is a driving force between highly specialized user experiences created through personalization and customization. The analysis can be used to make customers feel seen and understood by a company.
The six major areas of data science include the following:
https://www.kdnuggets.com/2020/06/8-basic-statistics-concepts.html
https://www.w3schools.com/python/python_ml_getting_started.asp
https://www.freecodecamp.org/learn/machine-learning-with-python/
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