Machine Learning

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Machine Learning

Machine Learning

Machine Learning Duration: 40 hrs

This course provides advanced-level training on Machine Learning applications and algorithms. It will give you hands-on experience in both supervised and unsupervised learning, machine learning algorithms like regression, clustering, classification, and recommendation.
Course Objective: This Course covers supervised and unsupervised learning techniques, regression and classification problem analysis, artificial neural networks, backward propagation, SVM and Natural language processing
Prerequisite: Basic Understanding of Python /MATLAB programming language and overview of windows/Linux operating system

Module 1

  • Getting started with the programming language
  • Lab: working with functions, control structure etc.
  • Artificial Intelligence
  • Current Applications & Advancements
  • Machine Learning & Applications
  • Future Advancements and Current Trend
  • Popular Algorithms
  • Supervised & Unsupervised Learning Technique
  • Regression & Classification Problem Analysis
  • Artificial Neural Networks
  • Linear Algebra review
  • Introduction to Neuron
  • Introduction to Network Architecture
  • Designing Neural Network Model
  • Model Representation Methods
  • Weights & Activation Functions
  • Gradient Descent Algorithm
  • Single Layer Perceptron Model
  • Lab: Single Layer Perceptron Model Learning
  • Lab: Housing Prizes Prediction
  • Multilayer Neural Network Architecture
  • Training the Network
  • Backward Propagation Training
  • Lab: Multilayer ANN Training Example

Best Mean Fitting

  • Working with Best Mean Fitting
  • Lab: Single Line as Hypothesis Training

Module 2

Machine Learning – SVM & Natural Language Processing

  • Introduction to SVM
  • Decision Boundary and Hyper plane
  • Minimization of Cost function for optimized hyperplane
  • Support Vector for Classification – Parameters
  • Support Vector for Regression – Parameters

Case study & Hands On (Labs):

  • Character Recognition
  • Cancer Recognition
  • Regression Problem Example

Natural Language Processing (Different Toolbox in case of MATLAB)

  • Working with Sklearn, nltk python library for NLP
  • Accessing External Data, working with words and sentences
  • Lab: Using NLTK for extracting data
  • Lab: Sentiment Analysis Example
  • Lab:  Internet Data Analysis
  • Lab:Color Recognition, shape recognition &Real Time camera data Analysis
  • Lab: Character recognition, object recognition

Unsupervised Learning

  • Data Clustering methods
  • Multiple clusters
  • K Means clustering method
  • Nearest neighbor clustering
  • PCA Algorithm
  • PCA & K Means Clustering
  • Applications in Self Driving