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 handson 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 64bit machine)
Machine Requirement:
Recommended – Machine with 4GB RAM, i3 or above quadcore 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 userdefined functions
 Objectoriented 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 assignmentsLinear 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
 ChiSquare 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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