This course will start with an introduction to python programming. The course covers all
the topics needed to be a good backend developer. On the scripting part, we cover Linux
and shell scripting. We also go through the basics of github. We start with setting up the
environment using python, pip and pycharm. We go through how a rest api works and
also implement few of them. Our database lessons will include, mongoDb and SQL and
we also teach some basics in cloud computing like EC2, dynamoDb, RDS, S3 etc.
This course will introduce the learner to applied machine learning, focusing more on the
techniques and methods than on the statistics behind these methods. The course will
start with a discussion of how machine learning is different than descriptive statistics.
The issue of dimensionality of data will be discussed, and the task of clustering data, as
well as evaluating those clusters, will be tackled. Supervised approaches for creating
predictive models will be described. The course will end with a look at more advanced
techniques, such as building ensembles, and practical limitations of predictive models.
By the end of this course, students will be able to identify the difference between a
supervised (classification) and unsupervised (clustering) technique, identify which
technique they need to apply for a particular dataset and need, engineer features to meet
that need, and write python code to carry out an analysis.