This is a Beginner’s course that covers basic Machine Learning and Data Analytics concepts.
The Objective of this course is to teach students how to do an End-2-End data science project:
- From Problem definition, data sourcing, wrangling and modelling
- To analyzing, visualizing and deploying & maintaining the models
- It will cover the main principles/tools that are required for data science
This course is for anyone interested in learning data science – analyst, programmer, non-technical professional, student, etc
Having seen available data science courses and books, we feel there is a lack of an End 2 End approach:
- Quite often you learn the different algorithms but don’t get a holistic view, especially around the process and deployment
- Also, either too much or limited mathematical details are provided for different algorithms
The course will cover all the basics in programming, maths, statistics and probability required for building machine learning models Throughout the course detailed lectures covering the maths and logic of the algorithms, python code examples and online resources are provided to support the learning process Students will learn how to build and deploy machine learning models using tools and libraries like anaconda, spyder, python, pandas, numpy, scikit-learn, xgboost, matplotlib, seaborn, joblib, flask, AWS Cloud S3, Elastic Beanstalk and Sagemaker More details are available on our website – datawisdomx. Course material including python code and data is available in github repository – datawisdomx, DataScienceCourse.
