Fundamentals of Data Science: Exploratory Data Analysis (EDA), Regression (Linear & logistic), Visualization, Basic ML
What you’ll learn
Beginning with Machine Learning & Data Science in Python Best Courses
- You will be able to apply data science algorithms for solving industry problems
- You will have a clear understanding of industry standards and best practices for predictive model building
- Able to derive key insights from data using exploratory data analysis techniques
- You will be able to efficiently handle data in a structured way using Pandas
- You will have a strong foundation of linear regression, multiple regression, and logistic regression
- Able to use python scikit-learn for building different types of regression models
- You will be able to use cross-validation techniques for comparing models, select parameters
- You will know about common pitfalls in modeling like over-fitting, bias-variance tradeoff, etc..
- Able to regularize models for reliable predictions
- Basic programming in any language
- Basic Mathematics
- Some exposure to Python (but not mandatory)
85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). Naturally, 85% of the interview questions come from these topics as well.
This course will help you create a solid foundation for the essential topics of data science.
At the end of this course, you will be able to:
- Get your hands dirty by building machine learning models
- Master logistic and linear regression, the workhorse of data science
- Build your foundation for data science
- Fast-paced course with all the basic & intermediate level concepts
- Learn to manage data using standard tools like Pandas
This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.
Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well.
This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts off with a basic explanation of Machine Learning concepts and how to set up your environment. Learning the industry standard best practices and evaluating the models for sustained development comes next.
Final learning is around some of the core challenges and how to tackle them in the industry setup. This course supplies in-depth content that puts the theory into practice.
Who this course is for:
- Anyone willing to take the first step towards data science
- Anyone willing to develop a solid foundation for data science
- Who anyone planning to build the first regression/machine learning models
- Anyone willing to learn exploratory data analysis
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