Participants should have a background of programming in Python or should have gone through the Python for Data Science course before taking this up.
Applicable to those with a beginner level of Python proficiency!
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Machine Learning with Python
This course will take you through the fundamentals of machine learning, the different algorithms involved in supervised and unsupervised learning, model evaluation and model optimization techniques.
What you'll learn
This course is a highly practical course which focuses on introducing the various data science and machine learning libraries and algorithms which are used across various use cases in the industry today. Participants will be exposed to supervised and unsupervised learning using scikit-learn, evaluating and comparing different models. Participants who complete this course will have a grasp of intermediate python and data science and would be ready to tackle the more advanced levels of machine and deep learning.
About this course
Machine Learning with Python is an advanced level course in understanding how to perform predictive analytics using complex algorithms and machine learning.
Participants will learn to write programs in Python which can perform complex level of analytics and create, predict and evaluate using various machine learning models. Participants who complete learning these skills will finish the course at an advanced level of Python and will be ready to take up further advanced courses in machine and deep learning.
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Course Duration
1
Hours
1
Days
Have a look
Course Plan
Understand the pre-requisites to machine learning in terms of the functional and nonfunctional process.
Reinstating the basic techniques in python as a prerequisite to machine learning
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
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
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
Understanding and implementing
Logistic Regression
Support Vector Machines
Decision Trees
Implementing the above algorithms with real use cases
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
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