Beginning with Machine Learning, Data Science and Python
What you'll learn
- 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
- You will be 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
- You will be 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 trade off etc..
- You will be 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). So naturally, 85% of the interview questions come from these topics as well.
This concise course, created by UNP, focuses on what matter most. This course will help you create a solid foundation of the essential topics of data science. With this solid foundation, you will go a long way, understand any method easily, and create your own predictive analytics models.
At the end of this course, you will be able to:
independently build machine learning and predictive analytics models
confidently appear for exploratory data analysis, foundational data science, python interviews
demonstrate mastery in exploratory data science and python
demonstrate mastery in logistic and linear regression, the workhorses of data science
This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.
Special emphasis is given to regression analysis. 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. In addition, concepts of overfitting, regularization etc., are discussed in detail. These fundamental understandings are crucial as these can be applied to almost every machine learning method.
This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts with a basic explanation of Machine Learning concepts and how to set up your environment. Next, data wrangling and EDA with Pandas are discussed with hands-on examples. Next, linear and logistic regression is discussed in detail and applied to solve real industry problems. Learning the industry standard best practices and evaluating the models for sustained development comes next.
Final learnings are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put 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
- Anyone planning to build the first regression / machine learning models
- Anyone willing to learn exploratory data analysis
We are a team of working professionals from around the globe ,about 30 strong, coming from various spheres of the Data Science Universe,each bringing in a unique set of skills which we have acquired through years of experience in almost every domain of Business.The Professionals in UNP are unified by a single common goal to minimise the entry barrier to quality education at every stage of one’s life and we strongly believe that knowledge should be shared in its truest form to transcend.We are committed to provide quality education in the realms of Data Sciences coupling it with IoT and Cloud Computing, DevOps, Quantum Computing & Blockchain
At UNP- R&D emerging Tech are being nurtured and applied to create the first Decentralised Education Ecosystem, as we believe democratisation of knowledge & education is the Future.