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Machine Learning: The Art of Digging Data
6.5 total hours
Are you ready to start your path to becoming a Data Scientist!
Are you Interested in the field of Machine Learning?
Then this course is for you!
This course has been designed by two master degree students who are specialized in Data Science and Machine Learning and having 2 years of experience in IT industry so that we can share our knowledge and experience to help you learn complex theory, algorithms and coding libraries in a layman’s way.
This course has been design in such a way that anyone who has basic knowledge of math can understand the concepts and implement them. We will do all the coding from scratch, so that a person with zero knowledge of programming language will also be able to mastery in this field.
It is structured the following way:
Python Tools for Machine Learning
Python for Machine Learning
Journey to SkLearn
Data Preprecessing and Visualization
Regression Model , Tuning and Validation
Classification Model , Tuning and Validation
Introduction to NLP
The course will not only give you grip over the concepts but it also contains some very interesting and real-life coding excercise which gives great flavour to the course.
This Course will also be helpful for those who are having machine learning in their course.
What knowledge & tools are required?
No prior knowledge necessary.
Anaconda Python distribution for coding
Who should take this course?
Any beginners interested to gain knowledge in Machine Learning
What will students achieve or be able to do after taking your course?
Hands-on experience of Machine Learning using Python.
Basic knowledge of Python coding needed for ML.
Knowledge of Machine Learning paradigms and algorithms.
Knowledge of ML model creation.
Mastery in Data Visualization.
Knowledge on using NLP in text classification problems.
Knowledge of performance metrics to increase model.
Knowledge of problem-solving based on data analysis.
- Hands-on experience of Machine Learning using Python.
- Basic knowledge of Python coding needed for ML.
- Knowledge of Machine Learning paradigms and algorithms.
- Knowledge of ML model creation.
- Mastery in Data Visualization.
- Knowledge on using NLP in text classification problems.
- Knowledge of performance metrics to increase model .
- Knowledge of problem-solving based on data analysis.
- No prior knowledge necessary.
- Anaconda Python distribution for coding.
Srikant KumarMachine Learning Enthusiast
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