Machine Learning Course Content

Description

The world has changed and setting up new process and method of doing their even more precisely we are changing our self into a machine and dependencies is increasing more than our imagination.

Overview

The Main purpose of training machine is to use their speed and capability. Most Importantly machine can think and perform task like humans

Goals
  • Python Programming for ML
  • Supervised based algo implementation
  • Tensor Flow and other frameworks learning
  • Learning pandas framework to handle data frames for machines
  • Matplotlib for graph plots with linear regression
  • Image recognition
  • ML with IoT for AI Systems
Introduction to Machine Learning
  • Understanding the need
  • Understanding Big data and machine learning
  • Running machine learning under Linux platform
  • Introduction to Red hat Enterprise Linux
  • Why Linux is important for machine learning with respect to future
  • Role of Python and R programming in this domain
  • Basic Introduction of Python syntax and programming logics
  • Deep dive with Supervised , Unsupervised and Reinforcement learning
  • Algo discussion with use case
  • Popular machine learning framework like tensor flow , scikit-learn

Advance Python programming and its use case

  • Basic of python and why python for machine learning
  • Installation of software and libraries on different OS.
  • Revising python concepts
  • Advance python programming
  • Hands on with Python standard libraries
  • GITHUB exposure
Data Science Libraries
  • Understanding & use of Various Open source libraries
  • Importing various modules with different methods
  • File handling with Python
  • Working with Numpy
  • Data types and its various Numerical operations
  • Exploring various use cases of Numpy
  • Hands on with huge data using numpy
Pandas & Matplotlib Libraries
  • Fundamentals of pandas
  • Dataframes and their operations
  • csv .xml and various files data import
  • Data extraction, uodation and export
  • Fundamentals of Matplotlib
  • Various 2D & 3D graphs
  • Data visualisation in various types of graphs
  • Basics of Data Analtics
Computer Vision & OpenCV Library
  • Fundamentals of Computer Vision
  • Image Processing using Python
  • OpenCV library for various data operatios
  • Working with live data
  • Computer Vision for various fileds like AR, VR , ML etc
  • Morphological operations and Image Filtering & ROI Extractions
  • Color Marker Detection

Project - Data Analytics using Python
Machine Learning algorithms & Mathematics

Introducing IoT
  • What’s an Algorithm and Machine Learning Implementation
  • Various ML algos and their mathematics
  • Categories of Machine Learning (Supervised & Unsupervised & Reinforcemenet)
  • Classification, Regression & Clustering in ML
  • Decision Tree, Naive Bayes,KNN,SVM,Haar Classifiers
  • Linear & Logistic Regression
  • K-Means Clustering
  • Neural Network – ANN, CNN & RNN
Scikit-Learn Library
  • Fundamentals of the library
  • Various commands and their usecase
Working with the Algos Classifiers
  • What is Decision Tree?
  • Implementation & its Prediction Experience
  • Applying algo on various data sets
  • ML with Decision Tree Results accuracy
Naive Bayes
  • Probability of Various Events
  • Bayes Theorem
  • Practise lab with DecisionTree algo and number of examples
  • Training data with python using Naive Bayes
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
ML Continued with Real Data set
  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with pandas
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree & Naive Bayes understanding & Results Comparisions
K Nearest Neighbours – KNN algo
  • Understanding the Mathematics and working of KNN
  • Implementing KNN by your Own
  • Apply your own designed KNN on real datasets
  • Comparing Designed KNN Results with Sklearn implementations
  • Applications of KNN
Regression (Linear Regression)
  • Understanding functioning of the Algo and Its Mathematics
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Stock exchange/GDP/Growth of the company analysis
  • Writing various codes upon various datasets
SVM (Supprt Vector Machine)
  • Support Vector Classifier and Regression
  • Understanding functioning of the Algo and Its Mathematics
  • Understanding Hyperplanes and its various internal parameters
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Writing various codes upon various datasets
Clustering (K-Means)
  • Unsupervised Learning
  • Features and data vectors visualization
  • Various steps of algo implementation
  • Understanding of Clusters and various types of Clustering
  • Applying K-Means on datasets and their practical use cases
  • Applications of Clustering and the algorithm
Neural Network (NN)
  • What’s Neural Network?
  • Various Structures of NN
  • Understanding Fundamentals and Various parameters of NN
  • ANN,CNN and RNN
  • Deep Dive with the Implementaion of NN on various datasets
  • Applying CNN on Images
  • Applications and its complexities over other algorithms
Project:- Smart Machine Learning System
Objects Detections
  • Image processing and it’s various features for detection
  • Haar Classifier and its functioning behind
  • Cascading of features in Algorithm
  • Implementation of Haar Classifier on different image datasets
  • Real time Object Detection
Project:- Object Detection System
Tensorflow
  • Fundamentals of tensorflow
  • What’s tensor and its flow graphs
  • Datatypes and Data Optimizers
  • Understanding Tensorflow from basics
  • Implementing usecase using tensorflow
  • Working on Realtime problem with Tensorflow and writing code for that
Obejcts Recognitions
  • Understanding of Features of Objects for Recognitions
  • Working with Face Recognition Library
  • Recognition encodings
  • Various matching techniques for Recognition
  • Working on improving Efficiency of the code

Project 3 :Face Recognition System

Project 4 :Biometric Advance Attendance System

Project 5 :Building Security System Communication Protocol using Python

  • Various communication protocols for networking
  • Networking with MQTT
  • MQTT implementation with python over internet
  • World wide data communication and analysis
Auto Chat Bot using ML
API Integration with Python
  • What is an API?
  • What is Cloud & its Connections with Python?
  • Google Python Libraries
  • Speech Recognition
  • Text to Speech Conversion
  • Speech Recognition Exceptions
  • Various API’s Integration for ML
Design & Development of your Personal Assistant
ML over Cloud
  • Various cloud platforms for ML
  • Open Source Cloud for Features engineering
  • Various features for a person analysis
  • Registration and deletion of data over cloud
  • Recognising images over cloud
Gender & Expressions recognition system over cloud
Weather & Other API’s
  • Various weather API’s
  • Data extration from the raw weather information
  • Other API’s for data extraction from Web
  • Web Scrapping using network libraries in python
  • Data extraction from Zomato/Ola/Amazon or other such big online platform
Smart Weather App using ML & Python
Natural Language Processing
  • Concepts of Natural Language processing
  • NLP libraries in Python
  • Working with NLTK
  • Words Extrations from the text
  • Sentiment Analysis concepts
Smart Talking System using ML
Keras Library and Its Implementation
  • Understanding wide range of Keras library
  • Keras and its various structures for images
  • Backend tensorflow mechanism for patern recognitions
  • Deep learning models and their formations
  • Deep learning models use case with ML for Expression Recognition
Facial Expression Recognition System
Deep Learning Concepts
  • Understanding Deep Learning
  • Various Concepts of Deep Learning
  • How does these model work?
  • How to prepare your own Models?
  • Various problems to work with Deep Learning
Smart Weather App using ML & Python
Natural Language Processing
  • Concepts of Natural Language processing
  • NLP libraries in Python
  • Working with NLTK
  • Words Extrations from the text
  • Sentiment Analysis concepts

Project 3 :Preparation of Self Deep Learning Models using Custom datasets

Project 4 : Completion & presentations Query Session Certificate Distribution

Biometric Advance Attendance System

Courses Features

  • Language
    English
  • Lectures
    02
  • Certification
    Yes
  • Project
    2 Minor + 2 Major
  • Duration
    35 hrs
  • Max-Students
    20
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