Applied Machine Learning in Python

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Course type: Online Instructor led Course

Platform: Skype and team viewer

Course ID: 34011

Course type: Online Instructor led Course

Platform: Skype and team viewer

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Abhi S picture
Abhi S

M. Tech. in Computer Science

About Abhi S

4 years of experience in Machine learning in various fields of Text analytics, Image processing and Speech processing.
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About the Course

A perfect hands-on practice for beginners to who want to elevate their machine learning skills.


A perfect hands-on practice for corporate developers to who want to delve into machine learning area.


A perfect hands-on practice for academicians to who want to delve into machine learning area.


"Applied Machine Learning in Python"


 


Key Features:


 



  • 30 Hours of theory classes, 30 hours of Lab along with theory classes.

  • A strong theoretical foundation step-by-step on various learning algorithms

  • Hands-on coding skill development in Python.

  • End-to-End 6 Academic/Research projects using ML models in major research areas like Text Analytics, Image Processing & Speech Analytics


 


Overview:


 


This training course is for people that would like to apply Machine Learning in practical applications.


 


Audience:


 


This course is for developers and academicians that have some familiarity with statistics and know how to program (preferably Python).


 


Prerequisites:


 



  • Acquaintance with Python Programming Language

  • Little knowledge about Linear algebra (Matrices) and Probability theory.


 


Note: Each session having 1 Hour.


 


Session 1- Introduction


 



  • What is machine learning?

  • Algorithm types of Machine learning

  • Supervised and Unsupervised Learning

  • Uses of Machine learning

  • Planning for ML techniques


 


Session 2,3,4,5 - Linear Regression with Multiple variables


 



  • Model Representation

  • Cost Function

  • Gradient Descent Optimization Method

  • Gradient Descent For Linear Regression


 


Session 6,7,8 -  Advice for applying Machine Learning


 



  • Model Selection

  • Train/Validation/Test Sets

  • Evaluating a hypothesis

  • K-Cross fold validation, Stratified K-cross

  • Machine Learning System Design


 


Session 9,10,11,12,13,14,15 - Supervised Learning


 



  • Decision Trees

  • Random forest

  • Gradient boosting Machines

  • Support Vector Machines


 


Session 16,17,18,19,20,21- Unsupervised Learning


 



  • Clustering Technique

  • K-nearest neighbor

  • Clustering - K means

  • Distance Measure and Data Preparation – Scaling

  • Evaluation and Profiling of Clusters

  • Other types of Clustering

  • Dimensionality Reduction

  • Data Compression/Dimensionality Reduction - Introduction

  • Principal Component Analysis

  • Linear Discriminant Analysis


 


Session 22, 23,24,25 - Probabilistic Classifiers


 



  • Naive bayes classifier

  • Gaussian Mixture Models


 


Session 26, 27, 28, 29, 30 - Nueral Network/Deep Learning


 



  • Neural Networks : Introduction

  • Multi-layer Perceptron

  • Deep Learning and its applications


 


End-to-End Academic/Research Projects:


Natural Language Processing (Week 1)



  1. Email Spam Filtering

  2. Sentiment analysis on Movie Reviews


 


Image Processing (Week 2)



  1. Image Compression using K-means clustering

  2. Digit Recognition on MNIST Database using MLP & DL


 


Speech Processing (Week 3)



  1. Voice Gender Detection

  2. Speaker Identification in Speech


 

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