Computer Vision COURSE TRAINING IN HYDERABAD

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Upcoming Batches

Course	Branch	Training Type	Week	Day
Data Science	Dilsukhnagar	Hybrid	1st	Wednesday
Data Science	Dilsukhnagar	Hybrid	2nd	Monday
Data Science	Dilsukhnagar	Hybrid	3rd	Wednesday
Data Science	Dilsukhnagar	Hybrid	4th	Monday

What is Computer Vision?

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects

Why to learn Computer Vision?

Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images or videos) in the same way that humans do. The concept of computer vision is based on teaching computers to process an image at a pixel level and understand it.

Who can Pursue Computer Vision?

Any Degree pursuing or Graduated,
 Bachelor’s, Master’s, PhD, and
 Anyone interested in course

Enroll to Course Now

Services	Essential Course Package	Mid-Level Career Booster	Mastery Career Package
Members	>20	<15	<10
Duration	3 Months	7 Months	7 Months + 1 Year
Portal
Course Completion Certificate
One to One
Resume Preparation
Mock Interviews
Job Assistance
Internship
Job Guarantee

COURSE CURRICULUM

Python

Introduction
 Installation
 Fundamental of Python
 Variables
 Comments
 Print Statement
 Operators
 Mutable Data Types
Data Types
Special Data Types
Conditions
Loops
Functions
Break, Continue and Pass Statements
String Object and working
List
Tuple
Set
Dictionaries
Map
Reduce
Filter
Classes
Objects
Inheritance
Multiple Inheritance
Modules
Error Handling

Data Visualization

Matplotlib
 Seaborn
 Plotly
 Cuflinks
 Bokesh

Libraries

Numpy
Pandas
Random
Math
Scipy
sklearn
Keras
Tensorflow
OpenCV
NLTK
Spacy
Lot more…

Evaluation Metrics for Regression Models

R-Square
 Adjusted R Square
 Mean Square Error
Root Mean Square Error
Mean Absolute Error

Statistics

Basics of Statistics
Descriptive Statistics
Inferential Statistics
Qualitative Analysis
Quantitative Analysis
Hypothesis Testing
Data Distribution
Probability Distribution
Normal Distribution
Poison Distribution
Outlier Detection
Other Statistical Fundamentals

Machine Learning

Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforcement Learning

Supervised Learning

 Regression Models
Classification Models

Regression Models

Introduction to Regression Models
Linear Models
Linear Regression
Multiple Regression
Ordinary Least Square Method
Non-Linear Models
Support Vector Regressor
Random Forest Regressor
Decision Tree

Classification Models

Introduction to Classification Models
 Logistics Regression
 Naïve Bayes
 Support Vector Classifier
 K-NN Classifier
 Decision Tree Classifier
 Random Forest Classifier

Evaluation Metrics for Classification Models

Accuracy
Precision
Recall
F1 Score
ROC Curve

Unsupervised Learning

Introduction to Unsupervised Learning

Clustering Models

 Introduction to Clustering Models
 K- Mean Clustering
 Hierarchical Clustering
High Dimensional Clustering

Dimension Reduction

Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

Feature Extraction
Model Defects & Evaluation Metrics
Model Selection and Tuning
Comparison of machine learning models

Neural Networks

Introduction to Deep Learning
Fundamental of Neural Networks
TensorFlow and Keras
Artificial Neural Networks (ANN)
Convolution Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Evaluation of Deep Learning
Neural Networks Basics
Gradient Descent
Introduction to Perceptron & Neural Networks
Batch Normalization
Activation and Loss Functions
Hyper parameter tuning
SoftMax
Deep Neural Networks
Weights initialization

Image Pre-processing

Computer Vision
Open CV
Noise Detection
Noise Reduce
Low Pass Filters
Forward Propagation
Backward Propagation
Pooling & Padding

Job Roles

•	Business Analytics Professional
•	Business Intelligence Professional
•	Data Scientist
•	Data Analyst
•	Data Engineer
•	Big Data Analysts
•	HR Analytics Professionals
•	Marketing Analytics Professionals

Components of Computer Vision

•	Business Understanding
•	Understanding Problem Statement
•	Data Mining
•	Data Cleaning
•	Data Analysis
•	Data Analytics
•	Data Exploration
•	Stats and Mathematics
•	Feature Engineering
•	Feature Selection
•	Feature importance
•	Modeling
•	Predictive Modeling
•	Deploying Model