Intellipaat offers Machine learning training that mainly focuses on key modules such as Python, Algorithms, Statistics & Probability, Supervised & Unsupervised Learning, Decision Trees, Random Forests, Linear & Logistic regression, etc. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.Machine Learning, as the name suggests, provides machines with the ability to learn autonomously based on experiences, observations and analysing patterns within a given data set without explicitly programming. Machine Learning, as the name suggests, provides machines with the ability to learn autonomously based on experiences, observations, and analyzing patterns within a given data set without explicitly programming.
Project 01: Analyzing the trends of COVID-19 with Python
Problem Statement: Understanding, the trend of COVID 19 spread and if the restrictions imposed by governments around the world has helped us curb the COVID 19 cases and by what degree
Topics: In this project we will use Data Science and Python to perform data visualization to understand the data. We will use on COVID 19 data set to do Time Series Analysis in order to make a prediction about future cases if the current trend as observed thus far continues.
Using pandas to accumulate data from multiple data files Using plotly (visualization library) to create interactive visualizations Using facebooks prophet library to make time-series models Visualizing the prediction by combining these technologies Project 02 – Customer Churn Classification
Topics – This is a real-world project that gives you hands-on experience in working with most of the Machine Learning algorithms.
The main components of the project include the following:
Manipulating data in order to gain meaningful insights. Visualizing data to figure out trends and patterns among different factors. Implementing these algorithms: linear regression, decision tree, and Naïve Bayes.
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This project is part of:
Hack Week 19
This project is one of its kind!