FULL DATA SCIENCE AND AI PROGRAM

PRIORITISE TO IMPROVE YOUR SKILL WITH THE ADVANCED TASK.

Advanced Data Science Courses will increase your performance and enriches the standard with several project tasks

Why Advanced Data Science Course In Proitbridge?

The online courses in Advanced Data Science in Bangalore make your future with specialisation and make you master with enormous methodology. Enroll now in Proitbridge and automatically build your hands -on-projects. Each stage will explain successfully and review your skills before you come out with them. The courses will earn you life as a prospective Data scientist.

Data Science Advanced Courses In Online

Learn the advanced data science and improve your understanding with the best Academy of Proitbridge in Bangalore. It's a Parallel of knowledge in deep learning, data processing, data exploration, and learning mathematical basics for increasing your skills. It contains massive characteristics like frameworks, algorithms, technologies, and more.

Stages of Modules to Improve the Excellence

The courses include a total of 16 modules with entire parts and sessions. Each stage explains Advance Data Science and its advantages, mainly the Proitbridge Mentors are calculus your skills and provide caliber training. The courses run with your caliber and completing with the certification.

The course building and designing will undertake complete sessions.
Students can build end-to-end scalable processes of learning.
Obtaining details skills and hands-to-hand sources at your convenient time.
Wide usage of technologies.
It covers the massive points in module format so students can understand subjects deeply.
Balancing the courses with both theory and practical concepts.
Gain the impacts with the mentors and training.
Fetch the knowledge with the experts taught and communicate.

FUNDAMENTALS

Introduction to Types of Analytics
Project life cycle
An introduction to our E learning platform

Python Basics
Python Functions and Packages
Working with Data Structures, Arrays & Data Frames
Jupyter Notebook – Installation & Function
Working with OOPS

Good programming practices (testing, debugging, assertions, exception handling)
Get familiar with functional programming and its use cases
Understand the concepts of Object-Oriented Programming (Inheritance, encapsulation)

Pandas
NumPy
Matplotlib
Seaborn
Plotly

STATISTICS

Data Types
Measure Of Central Tendency
Measures of Dispersion
Graphical Techniques
Skewness & Kurtosis
Box Plot
Descriptive Statistics

Random Variable and Probability
Normal Distribution
Probability Distribution
Introduction to Hypothesis Testing
Statistical Tests Case Studies
A/B Testing

INTRODUCTION TO MACHINE LEARNING

Linear Regression
Multiple Variable Linear Regression
Logistic Regression
Decision Trees

K-means Clustering

Using Streamlit for Model Deployment

DEEP DIVE INTO MACHINE LEARNING

Linear Regression
Multiple Variable Linear Regression
Logistic Regression
Naive Bayes Classifiers
K-NN Classification
Support Vector Machines

Decision Trees
Bagging
Random Forests
Boosting

K-means Clustering
Hierarchical Clustering
DBSCAN
Dimension Reduction-PCA, t-SNE

Feature engineering
Model selection and tuning
Model performance measures
Regularizing Linear models
ML pipeline
Bootstrap sampling
Grid search CV
Randomized search CV
K fold cross-validation

Intro to Recommendation Systems
Popularity based model
Content based Recommendation System
Collaborative Filtering (User similarity & Item similarity)
Hybrid Models

What is Market Basket / Affinity Analysis
Measure of association
Support
Confidence
Lift Ratio
Apriori Algorithm

MACHINE LEARNING PROJECTS

Why streamlit?
Installation guide ,Hello Streamlit
Text Elements(title, header, sub-header, caption, text, code, write)
Data display elements like data frame, table, metrics
Media elements
Display chart elements
Widgets, SlideBar
Controlflow
Progress bar
Status messages and others
Data caching
Implementation of Simple Calculator app

From Business Understanding to Web Application Deployment Using Streamlit API

Basic of Flask
Paths of a website
Basic of HTML tags, input tag, buttons, forms, etc..
Render Template
Requests: Request..methods, Request..Form
User defined functions in Flask
Taking data from back and sending it to front

From Business Understanding to Web Application Deployment Using Flask API

DEEP LEARNING

Introduction to Perceptron & Neural Networks
Activation and Loss functions
Gradient Descent
Batch Normalization
TensorFlow & Keras for Neural Networks
Hyper Parameter Tuning

Introduction to Convolutional Neural Networks
Introduction to Images
Convolution, Pooling, Padding & its Mechanisms
Forward Propagation & Backpropagat on for CNNs
CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
Transfer Learning
Object Detection
YOLO, R-CNN, SSD
Semantic Segmentation
Instance Segmentation

DEEP DIVE INTO DEEP LEARNING

Advanced Computer Vision with OpenCV 4, Keras, and TensorFlow 2
Computer Vision for OCR and Object Detection
PyTorch for Deep Learning and Computer Vision

Encoder and Decoder Architecture

DEEP LEARNING PROJECTS

2 Real-time Projects End-to-End with Computer Vision Concepts

NATURAL LANGUAGE PROCESSING

Introduction to NLP
Stop Words
Tokenization
Stemming and Lemmatization
Bag of Words Model
Word Vectorizer
TF-IDF
POS Tagging
Text Mining, Cleaning, and Pre-processing
Text Classification
NLTK
Sentiment Analysis, etc...,

DEEP DIVE INTO NATURAL LANGUAGE PROCESSING

Named Entity Recognition
Emotion Mining
Introduction to Sequential data
RNNs and its Mechanisms
Vanishing & Exploding gradients in RNNs
LSTMs - Long short-term memory
GRUs - Gated Recurrent Unit
LSTMs Applications
LSTMs with Attention Mechanism
Neural Machine Translation
Advanced Language Models
Transformers, BERT

NLP PROJECTS

Introduction to Databases, Software Installation
Types of SQL Commands; Data Types in SQL
DDL and DML and TCL commands
Database Constraints
Operators in SQL
Grouping operations
Ranking functions, Analytical functions
Joining Tables
Views, Triggers
Introduction to subqueries, different types of subqueries
Indexing, Sequence Objects
Stored procedures

From Business Understanding to Web Application Deployment Using Flask API/ Streamlit API

TIME SERIES MODELLING

Steps of forecasting
Components of time series data
Scatter plot and Time Plot
Lag Plot
ACF – Auto-Correlation Function / Correlogram
Visualization principles
Naive forecast methods
Errors in forecast and its metrics

Linear Model
Exponential Model
Quadratic Model
Additive Seasonality
Multiplicative Seasonality
AR (Auto-Regressive) model PROITBRIDGE for errors
ARMA (Auto-Regressive Moving Average), Order p and q
ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q

Smoothing techniques
Moving Average
Simple Exponential Smoothing
Holts / Double Exponential Smoothing
Winters / HoltWinters
De-seasoning and de-trending

TIME SERIES FORECASTING PROJECTS

From Business Understanding to Web Application Deployment Using Flask API/ Streamlit API

ADDITIONAL TOPICS

GANS
Reinforcement Learning

Market awareness
Job Roles and Responsibility Awareness

Resume Preparation
Projects Presentation

1:1 Mentoring for Placements
Peers/Alumni Connect Sessions (Per hr. basis)
Experiential Learning Exposure (per hr. basis)

Business Presentations
Email Writing
Soft skills at the Job Front

Up to 3 Mock Interviews (Until Placed)

ADDITIONAL ASSISTANCE

Up to 3 Mock Interviews (Until Placed)

Tools Used

Career Options

Data Analyst

Data Scientist

ML Engineer

CV Engineer

Deep Learning Engineer

NLP Engineer

AI Engineer

OUR ALUMNI'S WORK AT

larsen-toubro-data-science-bangalore

Wipro-data-science-bangalore

Mindtree-data-science-bangalore

Bosch-data-science-bangalore

Zysus-data-science-bangalore

Cognictica-ai-data-science-bangalore

Bert-labs-company-data-science-bangalore

Damco-data-science-bangalore

Latentview-data-science-bangalore

Abb-data-science-bangalore

Dhruv-data-science-bangalore

Dhruv-data-science-bangalore

Dhruv-data-science-bangalore

Who can Apply for these Courses?

The people have Bachelor's degrees and are interested in Learning AL and ML.

Looking for an IT professional can apply.

Aiming people for a high Profession can apply.

Developers and Project Managers.

Freshers can apply for a standard professional career.

FAQ

Yes, the 16 modules cover online sessions, so as per flexible time can attend the class on weekdays or weekends.

It contains 70 days, accordingly, choosing the class sessions. (weekdays or weekends)

After completion, you can perform as a data scientist and related job roles with high packages.

It suits as financial and can help you with the customizable fee process. And also an asset for your future financial support.

Yes, we provide the certification after your respective course and support until your placement.