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EDUCATION FOR EVERYONE

People likes our Training.
No joking - here’s the proof!

viharatech review
Kiran Kumar
Software Engineer

Vihara Tech is an amazing place to learn data science where the curriculum and trainers are incredibly supportive and helped me solve all my course-related issues


viharatech review
Sandeep GuthiKonda
NLP GEN AI Engineer

Great Place to Learn all the Advanced concepts in AI They have all realtime working Employess so it becomes more easy for me to understand all concepts


viharatech review
Durga Bhavani
Junior Cloud Engineer

I joined Vihara Tech to learn AWS DevOps and found the curriculum and trainers to be extremely supportive After Course I secured at job at HCL Tech as a junior Cloud engineer


viharatech review
Mani Kanta
Python Developer

I Enroll Vihara Tech to study AI and was impressed by the trainers Within six months I secured an internship and later a job at AMD as a Python Developer


viharatech review
Tharun
DS Freelancer

I completed a course at Vihara Tech and received offers from two startup companies Due to my interest in pursuing a masters degree I continued my education


viharatech review
VishwaTeja
Computer Vision Intern

I learned Artificial Intelligence course at Vihara Tech when I was at 4th Year In Btech know I am working as an Computer Vision Intern at Zestiot


viharatech review
Venkata Jaghadeesh
data scientist intern

Attended all the classess to learn data science and after course as viharatech promised given me interview calls and I cleared one of them at ZestIot


viharatech review
Srinivas Reddy
System Engineer

I Enrolled Python with Data analysis course and got placed at TCS in 5 Months me and my family were really happy on new Job I recommend everyone to learn at Vihara Tech


viharatech review
Jayanth
Masters In USA

Before going to Master in USA I completed Data science course in vihara tech since I got many offers But I continued to Pursuing Masters


viharatech review
Naresh Mohana
Embedding Engineer

Secured Job as Embedding Engineer after Learning c programming and computer vision course to Integrate AI Models into Small Devices like Jetson Nano


viharatech review
Balaji Bhutham
Computer Vision Engineer

time is very precious for everyone with just 7 months of time and hard work with great team and mentors I got placed in dream Job thank you vihara tech for supporting


viharatech review
Sai Varma Chilakamari
Cloud Engineer

Due to Interest in cloud related techniques AWS Azure and salesforce I enrolled viharatech for devops and salesforce course it is amazing place to adopt new skills


viharatech review
Revanth Kumar Narra
Pursuing Masters

When I was in Btech I enrolled the GEN AI course In viharatech and got 4 offers in hand but due to higher education plans moved to USA It is an great place to adopt skills


viharatech review
Revanth Kumar Narra
Pursuing Masters

When I was in Btech I enrolled the GEN AI course In viharatech and got 4 offers in hand but due to higher education plans moved to USA It is an great place to adopt skills


viharatech review
Sai Varma Chilakamari
Cloud Engineer

Due to Interest in cloud related techniques AWS Azure and salesforce I enrolled viharatech for devops and salesforce course it is amazing place to adopt new skills


viharatech review
Balaji Bhutham
Computer Vision Engineer

time is very precious for everyone with just 7 months of time and hard work with great team and mentors I got placed in dream Job thank you vihara tech for supporting


viharatech review
Naresh Mohana
Embedding Engineer

Secured Job as Embedding Engineer after Learning c programming and computer vision course to Integrate AI Models into Small Devices like Jetson Nano


viharatech review
Jayanth
Masters In USA

Before going to Master in USA I completed Data science course in vihara tech since I got many offers But I continued to Pursuing Masters


viharatech review
Srinivas Reddy
System Engineer

I Enrolled Python with Data analysis course and got placed at TCS in 5 Months me and my family were really happy on new Job I recommend everyone to learn at Vihara Tech


viharatech review
Venkata Jaghadeesh
data scientist intern

Attended all the classess to learn data science and after course as viharatech promised given me interview calls and I cleared one of them at ZestIot


viharatech review
VishwaTeja
Computer Vision Intern

I learned Artificial Intelligence course at Vihara Tech when I was at 4th Year In Btech know I am working as an Computer Vision Intern at Zestiot


viharatech review
Tharun
DS Freelancer

I completed a course at Vihara Tech and received offers from two startup companies Due to my interest in pursuing a masters degree I continued my education


viharatech review
Mani Kanta
Python Developer

I Enroll Vihara Tech to study AI and was impressed by the trainers Within six months I secured an internship and later a job at AMD as a Python Developer


viharatech review
Durga Bhavani
Junior Cloud Engineer

I joined Vihara Tech to learn AWS DevOps and found the curriculum and trainers to be extremely supportive After Course I secured at job at HCL Tech as a junior Cloud engineer


viharatech review
Sandeep GuthiKonda
NLP GEN AI Engineer

Great Place to Learn all the Advanced concepts in AI They have all realtime working Employess so it becomes more easy for me to understand all concepts


viharatech review
Kiran Kumar
Software Engineer

Vihara Tech is an amazing place to learn data science where the curriculum and trainers are incredibly supportive and helped me solve all my course-related issues


Why Vihara Tech

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You can attend for classes through live or classroom by visitng our institute. We can provide recorded classes if you miss or recap the class

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Course Curriculum

Course Content

  • Introduction to Data Science
    Data Science with real-world Examples
    Applications using Data Science
    Applications using Machine Learning
    Applications using Deep learning
    Applications using computer vision
    Applications using Gen AI

  • Python

    SQL

    PowerBI

    Tableau

    Excel

    Machine Learning

    Deep Learning

    NLP

    Gen AI

    OpenCV

    Computer Vision

    Web Scrapping

    AWS

    DevOps

    Deployment

  • Data types

    varibales

    operators

    print

    precision and fied width

    Python Installation and Running

    Installation and Running

    Jupyter Notebook

    .py file from terminal

    Google Colab

    String

    list

    tuple

    sets

    dictionary

  • Forloops

    while loops

    if

    else

    elif

    break

    continue

    functions

    lambda function

    comprehensions

  • Error / Exception Handling

    File Handling

    JSON module

    OS Module

    Pickle Module

    Datetime Module

    Copy Module

    oops concept

    class

    object

    constructor

    inheritance

    polymorphisam

    abstraction

    excapsulation

  • what is process

    what is multiprocessing

    what is multithreading

    start

    join

    kill

    terminate

  • Matrix Algebra

    Vector Matrix

    multiplication Matrix

    Regression lines etc

  • Numpy

    Pandas

    Data Visualization Library: Matplotlib

    Seaborn

  • Descriptive Statistics

    Measure of central tendency

    Measure of dispersion

    outliers

    covariance

    correlation

    testing

    hypothesis testing

    mean

    median

    mode etc..

  • Conditional Probability

    Bayes Rule

    Probability Distribution: Discrete and Continuous

    Normal Distribution etc..

  • Types of Machine Learning Methods

    Supervised Machine learning

    Unsupervised Machine learning

    Reinforcement Machine learning

  • Linear Regression

    Simple Linear Regression

    Multiple Linear Regression

    Polynomial Linear Regression

    Loss Function

    MSE

    MAE

    RMSE

    Optimizers

    Gradient Descent

    Regularization Parameters

    L1 and L2 Regularization

    Mini Project - 1 Developing Regression Model to find car cost status

    Mini Project -2 finding best Regresson Line using Gradient Descent Optimizers

  • KNN

    Logistic Regression

    Naive Bayes

    Decision Tree

    Ensemble: Bagging Random Forest Classifier

    Ensemble: Boosting Gradient Boosting

    AdaBoost

    XG Boosting

    Support Vector Machine (SVM)

  • Creation of new features from existing data

    Selection of relevant features for the model

    Handling missing values

    Handling categorical data

    Normalization and scaling

    Encoding categorical variables

    Polynomial features

    Log transformations

    Date-time feature extraction

    Dimensionality reduction techniques

    Text feature extraction

    Image feature extraction

    Feature crossing

    Feature splitting

    Handling outliers

    Feature standardization

    Feature normalization

    Filter methods

    Wrapper methods

    Feature importance from models

    L1 regularization (Lasso)

    L2 regularization (Ridge)

    Variance thresholding

    Chi-square test

    Correlation matrix

    Principal component analysis (PCA)

  • k_means_clustering

    Hierarchal Clustering

  • Neurons

    Synapses

    Weights

    Biases

    Activation functions

    Input layer

    Hidden layers

    Output layer

    Feedforward networks

    Backpropagation

    Gradient descent

    Learning rate

    Loss function

    Epochs

    Batch size

    Training data

    Validation data

    Test data

    Overfitting

    Underfitting

    Regularization

    Dropout

    Weight initialization

    Normalization

    Standardization

    Hyperparameters

    Model evaluation

    Tanh

    ReLU (Rectified Linear Unit)

    Leaky ReLU

    Softmax

    Thresholded ReLU

    Mean Squared Error (MSE)

    Mean Absolute Error (MAE)

    Binary Cross-Entropy

    Categorical Cross-Entropy

    Sparse Categorical Cross-Entropy

    Stochastic Gradient Descent (SGD)

    Mini-batch Gradient Descent

    Adagrad

    Adadelta

    RMSprop

    Adam

    L1 regularization

    L2 regularization

    Dropout

    Data augmentation

    Batch normalization

    Layer normalization

    Accuracy

    Precision

    Recall

    F1-score

    ROC-AUC

    Confusion matrix

    Precision-Recall curve

    Mean Average Precision (mAP)

    R-squared.

  • Convolutional layers

    Filters (kernels)

    Feature maps

    Stride

    Padding

    Pooling layers

    Max pooling

    Average pooling

    Flattening

    Fully connected layers

    Softmax activation

    Cross-entropy loss

    Backpropagation

    Gradient descent

    Batch normalization

    Layer normalization

    Dropout

    VGG 16

    VGG19

    ResNet

    TensorFlow

    Keras

  • Image reading and writing

    Image resizing

    Image rotation

    Image translation

    Image filtering

    Edge detection

    Contour detection

    Color space conversion

    Image thresholding

    Geometric transformations

    Feature detection

    Face detection

    Video processing

  • Viola Jones

    HOG

    Yolo v1

    Yolo v2

    Yolo v3

    Yolo v4

    Yolo v7

    Yolo v8

    Unet Segmentation

    v7 Segmentation

    POS Estimation

  • Tokenization

    Lowercasing

    Stop word removal

    Stemming

    Lemmatization

    Text normalization

    Removing punctuation

    Removing special characters

    Removing numbers

    Part-of-speech tagging

    Named entity recognition (NER)

    NLTK

  • Recurrent Neural Networks (RNNs)

    Hidden state

    Sequence modeling

    Backpropagation through time (BPTT)

    Vanishing gradient problem

    Exploding gradient problem

    Long Short-Term Memory (LSTM)

    Gated Recurrent Unit (GRU)

    Bidirectional RNN

    Sequence-to-sequence models

    Encoders and Decoders

    Attention all you need

    Transformers

    Bert

    Distilled Bert

    RobertA

    Hugging Face

    GPT Models

    Diffusion Models

    Stable Difusion Models

    Lang chains

    RAG

  • Zero Shot Learning

    Single Short learning

    Few Short Learning

  • Deployment on AWS

  • PowerBI Introduction

    PowerBI Installations

    Data Explanation

    Visualizations

    Bar chart

    Line chart

    Pie chart

    Area chart

    Scatter plot

    Bubble chart

    Treemap

    Funnel

    Card

    Geographic map

    Filled map

    Line chart

    Area chart

    Histogram

    Donut chat

    Model View Explanation

    DAX Formulas

    Power Query

  • Tableau Introduction

    Tableau Installation

    What is Data Source

    What is sheet

    What is DashBoard

    What is Story telling

    Tableau visualizations

    Tableau alignments

    Tableau Parameters

    Tableau sets

    Tableau GroupBy

    Include Exclude Bar Chart

    Line Chart

    Pie Chart

    Map

    Scatter Plot

    Histogram

    Gantt Chart

    Bubble Chart

    Heat Map

    Tree Map

    Box Plot

    Area Chart

    Dual-Axis Chart

    Waterfall Chart

    Highlight Table

    Packed Bubbles

    Word Cloud

    Funnel Chart

    Ascending order

    Descending order

    Transform

    Publish Data Reports

  • SELECT statement

    FROM clause

    WHERE clause

    JOINs INNER JOIN

    LEFT JOIN

    RIGHT JOIN

    FULL JOIN

    GROUP BY clause

    HAVING clause

    ORDER BY clause

    Aggregate functions (SUM AVG COUNT MIN MAX)

    Subqueries (Nested queries)

    UNION and UNION ALL

    Views (CREATE VIEW ALTER VIEW DROP VIEW)

    Indexes (CREATE INDEX DROP INDEX)

    Transactions BEGIN TRANSACTION

    COMMIT

    ROLLBACK

    Constraints PRIMARY KEY

    FOREIGN KEY

    UNIQUE

    CHECK

    Data manipulation INSERT

    UPDATE

    DELETE

  • Formulas and Functions

    math formulas

    LOOKUP

    INDEX-MATCH

    PivotTables (Creating Filtering Grouping)

    Charts and Graphs

    Column chart

    Line chart

    Pie chart etc

    Conditional Formatting

    Data Validation

    Named Ranges

    Excel Tables

    Functions for Text Manipulation

    LEFT

    RIGHT

    CONCATENATE etc

    Functions for Date and Time

    DATE

    TODAY

    MONTH etc

    Data Import and Export

Get Certified

Our Official Certificates
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