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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

Students Interested 0 (Seats Left 0)

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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




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




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




  • 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


Date and Time

Not decided yet.

About the Trainer

Abhi S picture

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0 Students

1 Courses

Abhi S

M. Tech. in Computer Science

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


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Students Interested 0 (Seats Left 0)

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