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Practical Machine Learning Using Python for Engineers & Technicians

  • Schedule : Online
  • Who is this for: Software Engineers , Students

Duration: 5-8 Hours
Schedule: Online
Level: Beginner
Rs: 63,000

Course Overview

Objectives

  • To understand topics such as basic machine learning terminology and processes
  • To understand how to implement machine learning to solve your engineering problems using the Python language.

  • Basic Mchine Learning Terminology
  • Linear Algebra with Python using Numpy & Pandas
  • Probability Theory & Statistics  with Python using Numpy & Pandas
  • Feature Engineering
  • Unsupervised Learning
  • Supervised Learning
  • Feedforward Neural Networks
  • Convolutional & Recurrent Neutral Networks
  • Natural Language Processing Part -1
  • Natural Language Processing Part -2
  • Practical Applications
  • Wed Development

Topics covered are

  • Machine Learning and Artificial Intelligence

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • Building a Machine Learning System

  • Evaluating a Machine Learning System

  • Linear Algebra Review

  • Introduction to Anaconda

  • Introduction to Pandas

  • Introduction to Numpy

  • Linear Algebra using Numpy

  • Data Plotting in Python

  • Statistics

  • Probability and Random Variables

  • Useful Probability Distributions

  • Derivatives

  • Data Loading and Manipulation using Pandas and Numpy

  • Working on Images

  • Features and Feature Vectors

  • One-hot Encoding

  • Feature Normalization

  • Clustering using K-means Algorithm

  • K-Means Implementation

  • Clustering using Expectation-Maximization

  • Association Rules and Recommender Systems

  • Classification

  • Regrression

  • The Perceptron

  • The Gradient Descent Algorithm

  • Multi-Layer Perceptrol

  • Deep Neural Networks

  • Convolutional Neural Networks

  • Recurrent Neural Networks

  • Problems Solved by Natural Language Processing

  • Text Preprocessing

  • Regular Expressions

  • Discrete Features

  • Word Embeddings

  • Part of Speech Tagging

  • Text Classification using Naïve Bayes

  • Text Classification using Neural Networks

  • Industrial Knowledge Representation using Decision Trees

  • Industrial Fault Diagnosis using Feedforward Neural Networks

  • Sound Classification using Feedforward Neural Networks

  • Image Classification using Convolutional Neural Networks

  • Machine Translation and Chatbots using Recurrent Neural Networks

  • Use of Flask

  • Integrating machine learning models with Flask

  • Deploying applications to a Web Server