Artificial Intelligence & Machine Learning

Upcoming Batch:

18th December  -7:00 AM – 9:00 AM, IST, ( Monday to Friday)

21st December  -8:00 PM – 10:00 PM, IST, ( Monday to Friday)

Mode of Training: Online Live Led Class

Duration: 45 hours

Fee: INR 18,500/- + GST Enroll now get 20% off on fee,  Pay Only INR 14,800/- + GST

About Course

If you are a computer geek, if you love coding and wish to explore dimensionless world of programming, TechTrunk brings you the core Artificial Intelligence Training which will take you through core development and programming experience and will make you expert in writing algorithms for AI applications, you will learn Machine Learning, Fuzzy Logic, NLP, SVM and much more.

Learning Objectives for this Course:

  • This course covers foundational concepts and hands-on learning of leading machine learning tools, such as Python and TensorFlow.
  • Over the course of the 45 Hours, candidates will not only gain theoretical knowledge of machine learning tools, but also gain exposure to business perspectives and industry best practices through lectures, Practice sessions, Assignments and project submissions.

Software to be installed –

Anaconda – https://www.anaconda.com/download/ (According to bit version 32 bit for 32 Machine and 64 bit for 64-bit machine)

Machine Requirement:

Recommended – Machine with 4GB RAM, i3 or above quad-core processor

Requirement: Working Internet Connection throughout the training for participants.

Python Libraries used (Most of them are already available in Anaconda, others we will install during the training)

Course Content

  • Basics of AI & Introduction

    • Artificial Intelligence
    • Environmental Constraints
    • Various Agent Types
    • PEAS Analysis of Problem
    • Process flow for an AI agent
    • Machine Learning Introduction
    • Supervised & Unsupervised Learning
    • Regression & Classification Problems
    • What makes a Machine Learning Expert?
    • Advantages & Disadvantages of Naïve Bayes Models

    Math for Machine Learning – Statistic & Linear Algebra

    • Matrices and Vectors
    • Basic Linear Algebra
    • Differentiation and Integration
    • Differential Equations
    • Inverse, Transpose, Eigen Vectors and Eigen Values
    • Single Value Decomposition
    • LU, QR Decomposition
    • Orthogonalization

  • Introduction to Python Programming

    • What is Python?
    • Installing Anaconda
    • Understanding the Spyder Integrated Development Environment (IDE)
    • Python basics and string manipulation
    • lists, tuples, dictionaries, variables
    • Control Structure – If loop, For loop and while Loop
    • Single line loops
    • Writing user-defined functions
    • Object-oriented programming
    • Working with Class & Inheritance

    Data Structure & Data Manipulation in Python

    • Intro to Numpy Arrays
    • Creating arrays
    • Indexing, Data Processing using Arrays
    • Mathematical computing basics
    • Basic statistics
    • File Input and Output
    • Getting Started with Pandas
    • Data Acquisition (Import & Export)
    • Selection and Filtering
    • Combining and Merging Data Frames
    • Removing Duplicates & String Manipulation

    Visualization in python

    • Introduction to Visualization
    • Visualization Importance
    • Working with Python visualization libraries
    • Matplotlib
    • Creating Line Plots, Bar Charts, Pie Charts, Histograms, Scatter Plots


  • Learning Objective: Working with Machine Learning Techniques like Linear Regression, Logistic Regression, Working with Projects and assignments

    Linear Regression

    • Regression Problem Analysis
    • Mathematical modelling of Regression Model
    • Gradient Descent Algorithm
    • Use cases
    • Regression Table
    • Model Specification
    • L1 & L2 Regularization

    Linear Regression – Case Study & Project

    • Programming Using Python
    • Building simple Univariate Linear Regression Model
    • Multivariate Regression Model
    • Apply Data Transformations
    • Identify Multicollinearity in Data Treatment on Data
    • Identify Heteroscedasticity
    • Modelling of Data
    • Variable Significance Identification
    • Model Significance Test
    • Bifurcate Data into Training / Testing Dataset
    • Build Model of Training Data Set
    • Predict using Testing Data Set
    • Validate the Model Performance
    • Project: Boston Housing Prizes Prediction
    • Project: Marketing Predictive Analysis
    • Best Fit Line and Linear Regression

    Logistic Regression

    • Assumptions
    • Reason for the Logit Transform
    • Logit Transformation
    • Hypothesis
    • Variable and Model Significance
    • Maximum Likelihood Concept
    • Log Odds and Interpretation
    • Null Vs Residual Deviance
    • Chi-Square Test
    • ROC Curve
    • Model Specification

    Case for Prediction Probe

    • Model Parameter Significance Evaluation
    • Drawing the ROC Curve
    • Estimating the Classification Model Hit Ratio
    • Isolating the Classifier for Optimum Results

  • Learning Objective: Working with Ensemble techniques, Decision Trees, Random Forests, Naïve Bayes, Projects, Examples. Getting expertise in Neural Networks, Projects and Case Studies

    Artificial Neural Networks with case study

    • 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 – Miscellaneous (Industry relevant Project)
    • Project – Miscellaneous (Industry relevant Project)

  • Learning Objective of this week – Support Vector Machines, Examples and Case Studies, Unsupervised Learning Techniques, Descriptive Analysis, Naïve Bayes Method

    Support Vector Machine

    • Concept and Working Principle
    • Mathematical Modelling
    • Optimization Function Formation
    • The Kernel Method and Nonlinear Hyperplanes
    • Use Cases & Programming SVM using Python
    • Project – Character recognition using SVM
    • Project – Regression problem using SVM
    • Project – Wisconsin Cancer Detection using SVM

    Clustering

    • Hierarchical Clustering
    • K Means Clustering
    • Use Cases for K Means Clustering
    • Programming for K Means using Python
    • Cluster Size Optimization vs Definition Optimization

    Projects & Case Studies

  •  

    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

  • Principle Component Analysis

    • Dimensionality Reduction, Data Compression
    • Concept and Mathematical modelling
    • Use Cases
    • Programming using Python

    Deep Learning

    Introduction to TensorFlow & keras

    • The Programming Model
    • Data Model, Tensor Board
    • Introducing Feed Forward Neural Nets
    • Softmax Classifier & ReLU Classifier
    • Dropout Optimization
    • Deep Learning Applications
    • Working with Keras
    • Building Neural Network with keras
    • Examples and use cases

 

 

 

 

 

 

 

 

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