Machine Learning With Python Training In Bangalore
Machine Learning With Python
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* Course Id : DSCI-PYML
* Duration : 20 Hours
Overview
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* This course teaches you the basics of machine learning with Python
* Firstly, this course teaches you the purpose of Machine Learning and its application in the real world.
* Secondly, you will get a good overview of Machine Learning
* We cover topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms
Training Objectives
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All attendees will learn:
* Advanced Python concepts
* Machine Learning fundamentals
* Regression, classification, clustering algorithms, and SciPy
* Evaluation of regression model and its accuracy
* Pros and Cons of each Classification method
* Usage of clustering for different real-world scenarios
Pre-Requisites
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Basic knowledge on:
* Basic programming skills in Python
* Awareness of machine learning
Course Structure
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* We provide more focus on hands-on in our technical courses (typically 80% hands-on/20% theory)
* Students get the capability to apply the material they learn to real-world problems
Materials Provided
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* All participants receive
* PDF of slides
* PDF of handson
* Access to instance with lab environment
Software Requirements
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Any of the following
* Any current internet browser
* vnc client
* rdp client
Hardware Requirements
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* Processor: 1.2 GHz+
* RAM: 512 MB+
* Disk space: 1 GB+
* Network Connection with low latency (<250ms) to Internet
## Daywise Machine Learning With Python Course Outline
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## Day 1
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* Unit 1 : Introduction to Machine Learning
* Unit 2 : Regression
## Day 2
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* Unit 3 : Classification
* Unit 4 : Logistic Regression
## Day 3
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* Unit 4 : Clustering
* Unit 5 : Recommender Systems
## Detailed Machine Learning With PythonCourse Outline
Unit 1 : Introduction to Machine Learning
* Introduction to Machine Learning
* Python for Machine Learning
* Supervised vs Unsupervised ML
* Applications of Machine Learning
* Advantages of using Python libraries for ML
Unit 2 : Regression
* Introduction to Regression
* Simple Linear Regression
* Model Evaluation in Regression Models
* Evaluation Metrics in Regression Models
* Multiple Linear Regression
* Non-Linear Regression
* Applications of Regression
Unit 3 : Classification
* Introduction to Classification
* K-Nearest Neighbours
* Evaluation Metrics in Classification
* Introduction to Decision Trees
* Building Decision Trees
Unit 4 : Logistic Regression
* Intro to Logistic Regression
* Logistic regression vs Linear regression
* Logistic Regression Training
* Support Vector Machine
Unit 5 : Clustering
* Intro to Clustering
* Intro to k-Means
* More on k-Means
* Intro to Hierarchical Clustering
* More on Hierarchical Clustering
Unit 6 : Recommender Systems
* Intro to Recommender Systems
* Content-based Recommender Systems
* Collaborative Filtering