Dive into the transformative world of Machine Learning with our intensive 2-day
course. Tailored for those keen on understanding the core algorithms and
techniques in Machine Learning, this course provides a detailed exploration of
classification, time series analysis, and clustering. Focusing on financial data
analysis, it offers practical skills in Python for Machine Learning applications.

Module 1: Fundamentals of Data Preparation
Understand the pre-requisites to machine learning in terms of the functional
and non-functional process.
• Reinstating the basic techniques in python as a prerequisite to machine
learning

Module 2: Introduction to Machine Learning with Scikit-learn
The objective is to understand the basics of machine learning and what it
means. The module also introduces the basic concepts of supervised and
unsupervised machine learning and gives an introduction to a very important
library used for machine learning on Python – scikit-learn.
• Introducing the machine learning flow and concepts
• Functions within scikit-learn
• Introduction to supervised and unsupervised machine learning

Module 3: Unsupervised Machine Learning
This module aims to equip participants with the fundamentals of unsupervised
machine learning using a very popular python library called scikit-learn.
Unsupervised learning is very important across various business cases today,
right from customer segmentation to property analysis.
• Understanding unsupervised ML algorithms
• Introduction to clustering (k-means, SOM)
• Implementing clustering with real use cases

Module 4: Supervised Machine Learning
Supervised machine learning is one of the most popular technique in machine
learning today. This module will stress on some of the most popular algorithms
in regression and classification and equip participants with an understanding of
how the algorithms work and where they can be used.
• Introduction to various supervised learning algorithms
• Understanding feature engineering and feature sets
• Implementing various Supervised ML algorithms with real use cases

Module 5: Evaluating Machine Learning Models
One of the key steps in the data science lifecycle is to evaluate machine
learning models to make sure the right one is selected for use in the business.
Also, these models need to be trained and optimized over time. This module
aims to do just that by covering the techniques aiding model selection and
evaluation and optimization.
• Understanding model selection and evaluation methods
• Optimize machine learning models