Workshop detalis

Machine Learning

Workshop: 5days
Engineering: CSE/AIML
Enrolled: 60 students
(30 Reviews)

Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.

Course description

Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.

What you'll learn from this course
  • Ready to begin working on real-world data modeling projects.
  • Expanded responsibilities as part of an existing role.
  • Manage your time so you'll get more done in less time.
  • Hone sharp leadership skills to manage your team.
  • Cut expenses without sacrificing quality.
Certification

At TechIn IT, we proudly assure that every Trainee who successfully completes our program will be awarded a certificate. We are officially associated with APSCHE, AICTE, MSME, Skill India, IAF, and NASSCOM. The certification will reflect the Trainees dedication and skill development, recognized under national-level standards.

  • Basics of AI & Introduction:

    Artificial Intelligence

    Environmental Constraints

    Various Agent Types

    PEAS Analysis of Problem

    CSP – Introduction

    Process flow for an AI agent

    Machine Learning Introduction

    Supervised & Unsupervised Learning

    Regression & Classification Problems

    Fuzzy Logic:

    Getting started with Fuzzy Logic

    Applications of Fuzzy Logic

    Problem Formulation, Defuzzification&Rulebase

    Membership Functions

    Defuzzification Methods

    Mamdani&Sugeno Methods

    Washing Machine Problem

    Tipping Problem Analysis

    Fuzzy Logic packages in Python

    Using Pyfuzzy with python

    Programming Fuzzy Logic Applications

    Practical Examples, Case Studies & Hands on session on Fuzzy Logic

    Linear Regression :

    Regression Problem Analysis

    Mathematical modelling of Regression Model

    Gradient Descent Algorithm

    Programming Process Flow

    Use cases

    Programming Using python

    Building simple Univariate Linear Regression Model

    Multivariate Regression Model

    Boston Housing Prizes Prediction

    Cancer Detection Predictive Analysis

    Best Fit Line and Linear Regression

    Decision Trees

    Forming a Decision Tree

    Components of Decision Tree

    Mathematics of Decision Tree

    Decision Tree Evaluation

    Practical Examples & Case Study

    Random Forest

  • Artificial Neural Networks

    Neurons, ANN & Working

    Single Layer Perceptron Model

    Multilayer Neural Network

    Feed Forward Neural Network

    Cost Function Formation

    Applying Gradient Descent Algorithm

    Backpropagation Algorithm & Mathematical Modelling

    Programming Flow for backpropagation algorithm

    Use Cases of ANN

    Programming SLNN using Python

    Programming MLNN using Python

    Digit Recognition using MLNN

    XOR Logic using MLNN & Backpropagation

    Diabetes Data Predictive Analysis using ANN

    Project – Banking Problem Analysis – When the customer will leave?

    Project – Medical Problem Analysis

    Support Vector Machine

    Concept and Working Principle

    Mathematical Modelling

    Optimization Function Formation

    The Kernel Method and Nonlinear Hyperplanes

    Use Cases

    Programming SVM using Python

    Character recognition using SVM

    Regression problem using

    Wisconsin Cancer Detection using SVM

    Image Processing with Opencv

    Image Acquisition and manipulation using opencv

    Video Processing

    Edge Detection

    Corner Detection

    Face Detection

    Image Scaling for ANN

    Training ANN with Images

    Character Recognition

    Clustering

    Hierarchical Clustering

    K Means Clustering

    Use Cases for K Means Clustering

    Programming for K Means using Python

    Image Color Quantization using K Means Clustering Technique

  • Deep Learning Networks

    Introduction to Tensor Flow

    The Programming Model

    Data Model

    Tensor Board

    Introducing Feed Forward Neural Nets

    Softmax Classifier

    ReLU Classifier

    Dropout Optimization

    Deep Learning Applications

    Convolutional Neural Networks :

    CNN Architecture

    Pooling

    Variants of the Basic Convolution Function

    Efficient Convolution Algorithms

    The Neuro-scientific Basis for Convolution Networks:

    Natural Language :

    Natural Language Processing & Generation

    Semantic Analysis

    Syntactic Analysis

    Language Translation

    Using NLTK

    Using Textblob

    Sentiment Analysis

    Project: Streaming live tweets and Sentiment Analysis

    Advice For applying Machine Learning:

    Machine Learning for System Design:

    Python Libraries used:

    Numpy

    Matplotlib

    Pandas

    Theano

    Scikit-learn

    Opencv

    TensorFlow

    Keras

    Scikit-Image

    Keras

    Quandl

    NLTK

    Textblob

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