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)

5. Voice Gender Detection
6. Speaker Identification in Speech