Learn Data Science and AI Online: Artificial Intelligence Certification - Digicrome

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DATA SCIENCE
GO COUNTRIES
PG - Program in
ARTIFICIAL INTELLIGENCE Most In-Demand Course
in 2024 - 2025
Powered By- Digicrome Digicrome's flexible
learning option will help you access the course
at your convenience.
01203113765
www.digicrome.com
_at_digicrome_official
www.digicrome.com
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About Digicrome Digicrome is the worlds 1
online bootcamp provider that enables learners
through rigorous and highly specialized training.
We focus on emerging technologies and processes
that are transforming the digital world, at a
fraction of the cost and time of traditional
approaches. Over one million professionals and
2000 corporate training organizations have
harnessed our award-winning programs to achieve
their career and business goals
DS AI ADMISSION Open For Registration! Period
2024 - 2025
The Best program Kickstart Your Career With New
Skills
Read More www.digicrome.com
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About The Program
Since our inception, we have been focused on
developing cutting-edge learning methodologies by
involving learners and experienced faculty, along
with providing individuals and corporations with
high- quality training materials that aid
professionals in accomplishing their career
objectives and furthering their careers. We work
with some of the world's finest institutions and
certifying authorities and we aspire to provide
high- quality training to professionals all
across the globe. We have a proven track record
of effectively training thousands of
professionals in both classroom and online
training. Come join us and let us transform your
professional lives via digital skills.
40k Trusted Learners
20k Students Secured jobs
Our mission is to offer affordable and
industry-relevant education that enables the
advancement and development of India's workforce.
500 All courses till now batch
Google Rating
4.6/5
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Why Learn Data Science?
Placement Report
40k 20k 25k
Trusted Learners Successfully Placed Job Interviews Cracked
Book a free consultation with expert
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Career in Data Science
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9-12 Yrs of Experience
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4-9 Yrs of Experience
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1-4 Yrs of Experience 0-1 Yrs of Experience
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4 5 Years of Experience
Some of the job roles associated with Data
Science include Data Analyst, Data Science
Generalist, Data Scientist, ML Analyst, ML
Engineer, ML Scientist, AI Analyst, AI Engineer,
AI/ML Developer, Business Intelligence Analyst,
Associate Data Scientist, Data Architect,
Business Intelligence Developer, Deep Learning
Engineer, Decision Scientist, Data Visualization
Specialist, and many others.
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Who can apply? A professional with Anyone with a
B. Tech / M. Tech / MCA / M.Sc / M.A (Economics)
/ MBA / BCA / B.Sc / B.com/ BA degree from an
accredited institution. Must have studied in 12th
standard Additional Scholarships Bring your
friend along and avail a discount of up to 5.
For more details, look at the program Term
Condition How can apply? Submit Application
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Apply for the Program by filling up the 1 min
Application form.
Join the Prestigious Program The admissions
office will send the acceptance letter. You can
secure your seat by depositing the registration
fee.
Admission You can secure admission by accepting
the offer letter and completing the payment.
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Data science is the skill set used to harness the
magnitude of these tectonic shifts in our world.
The data scientists toolkit enables her to solve
complex and intractable problems. Data science is
one of the most highly paid and in-demand skills
of the 21st century. Harvard Business Review
rated the job data scientist the sexiest
profession of the 21st century. Duration Executi
ve Program - 11 Months Project Internship - 12
Months Mode Online Online Class LMS
Key Features 100 Job Guarantee Aid
Topic wise Case study provide
Weekly doubt Session
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International Certified Certificates
25projects
Real time
4Capstone project
20 In demand skills and tools
Easy payment option available
12
Month Internship Provide
1
Life - time access to LMS
Personal mentorship
Month mock interview resume preparation
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Learning Path Career Services
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Step 1
Fundamentals of Python Programming
Step 2
Exploratory Data Analysis using Advanced Python
Libraries
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Step 3
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Practical Statistics for Data Scientists
Step 4
Data Analytics Tools (Power Bi, Tableau, Google
Data Studio, and SQL)
4
Step 5
Machine Learning Part 1- Supervised
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Step 6
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Machine Learning Part 2- Unsupervised Learning
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Deep Learning and Aritificial Intelligence
Step 7
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Artificial Neural Networks and Computer Vision
Step 8
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Time Series, Generative AI using Autoencoders,
and Reinforcement Learning
Step 9
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Step 10
Natural Language Processing
Step 11
11 Professional Soft Skills and Final Capstone
Project Resume Preparation Profile
Building Program Certificate
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Syllabus Overview
TIME DURATION 11 MONTHS
43 Weeks 200 HOURS 6 Months of Practical
Module Training 3 Months Advance AI ML
Training 1 Month Deep Learning using Keras and
TensorFlow Training 1 Month Interview Resume
Building Preparation Note 12 Months Internship (
Internship will start from Second month of the
course ) PROGRAM CURRICULUM
M o d u l e
C o u r s e
10 Modules
Total 86 Class
D i g i c r o m e
Introduction to Data Science - Orientation
Class Fundamentals of Python Programming Month 1

1. Basics Of Python
2. Data Structures in Python
3. Control Structure And Functions
4. OOP in Python

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• Exploratory Data Analysisusing Advanced
  PythonLibraries
• Month 2
• Python NumPy - functions
• Data Wrangling using Pandas
• Exploratory Data Analysis Using Matplotlib
• Exploratory Data Analysis Using Seaborn
• Data Visualization using Plots
• Web Scraping

M o d u l e
Practical Statistics for Data Scientists
C o u r s e

• Month 3
• Introduction to Statistics And Understanding the
  data
• Descriptive Statistics, Measures of central
  tendency, and dispersion
• Inferential Statistics
• Probability Distribution, Confidence intervals,
  and hypothesis testing
• Sampling Techniques
• Statistical significance using p-values
• Regression Analysis And Correlation analysis
• Introduction to Bayesian statistics

D i g i c r o m e
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• Data Analytics Tools (Power Bi, Tableau, Google
  Data Studio, and SQL)
• Month 4
• Tableau Overview and its implementation
• Power BI-Overview and Its implementation
• Google Data Studio and Its implementation
• Data Analysis using SQL
• Machine Learning
• Month 5
• Introduction to Machine Learning
• What is ML
• Why ML
• Types of ML
• Main Challenges - Overfitting, Underfitting,
  Poor Quality data, Irrelevant Features etc
• What are Hyperparameters
• How to Select ML model

M o d u l e
C o u r s e
D i g i c r o m e
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• Classification Metrics
• Accuracy
• Recall
• Precision
• F1 Score
• Confusion Matrix
• Classification Report
• Precision/Recall Tradeoff
• ROC Curve
• AOC Curve
• Binary and Multilabel Classification
• Feature Engineering and Feature
  Importance/Selection
• ClassifcationModels
• Gradient Descent and Stochastic
• Logistic Regression
• K Nearest Neighbors
• Naive Bayes
• Support Vector Machines

M o d u l e
C o u r s e
D i g i c r o m e
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• Ensemble Techniques
• Bagging - Eg Voting Classifiers
• Boosting - XG Boost, Adaboost, etc
• Cross Validation
• Random Forest Classifier
• XG Boost Classifier
• Stacking
• Hyper parameter Tuning

M o d u l e

• Month 6
• Regression Techniques
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Cost Function and Gradient Descent
• Performance Metrics - MSE, RMSE, MAE etc
• Heteroskedasticity, Non Normality and Correlated
  Errors
• Hyper parameter Tuning
• Regression Models
• Decision Tree Regressor
• Support Vector Machines
• K Nearest Neighbors
• Random Forest
• Boosting
• HyperparameterTuning

C o u r s e
D i g i c r o m e
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• UnsupervisedLearning
• Introduction to Unsupervised Learning
• K Means Clustering
• Hierarchical Clustering
• Model Based Clustering
• DBSCAN
• Anamoly Detection using Gaussian Mixtures
• DimensionalityReduction - Principal Component
  Analysis 9.RecommendationSystems

M o d u l e

• Deep Learning and Aritificial Intelligence
• Month 7
• Deep Learning using Keras and Tensorflow
• Introduction to Artificial Neural Networks
• Biological to Artificial Neurons
• The perceptron
• Multi-layer Perceptrons (MLPs)
• Input Layer, Hidden Layers and Output layers
• Weights and Biases
• Regression MLPs
• Classification MLPs
• Activation functions and Optimizers

C o u r s e
D i g i c r o m e
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• Implementationusing Tensorflow and Keras
• Building a Neural Network using Sequential API
• Building a Neural Network using Functional API
• Building a Neural Network using Subclassing API
• Saving and Restoring a Model
• Callbacks
• Training Deep Neural Networks
• Vanishing/Exploding Gradients
• Batch Normalization
• Gradient Clipping

M o d u l e
C o u r s e
D i g i c r o m e
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Month 8 ii. Artificial Neural Networksand
Computer Vision

• Introduction to Computer Vision
• The Architecture of Visual Cortex
  1.2ConvolutionalLayers
• Feature Maps
• Pooling
• Padding
• Stacking Multiple feature Maps
• Handson Experience - Building an Image Classifier
  using CNN
• Object Detection, ImageSegmentation, and
  SemanticSegmentation

M o d u l e
C o u r s e

• 4.CNN Architectures
• Learning Predefined Architectures -
  LeNet,AlexNet, GoogleLeNet, ResNet,VGGNet,
  Xception, SENet
• Transfer Learning - UsingPretrained Models from
  Keras
• Classificationand Localization

D i g i c r o m e
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Month 9 iii. Time Series, Generative AI using
Autoencoders, and Reinforcement
Learning 1.Processing Sequences using Recurrent
Neural Networks

• Introduction to Recurrent Neurons and Layers
• Memory Cells
• Implementation and Training of Recurrent Neural
  Networks
• Time Series using Recurrent Neural Networks
• Deep RNNs for Time Series
• Forecasting Several Time Steps Ahead
• Handling Long Sequences using LSTM and GRU cells
• Autoencoders
• Introduction to Autoencoders
• Encoder Decoder Networks

M o d u l e
C o u r s e
D i g i c r o m e
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• Reinforcement Learning
• What is Reinforcement Learning?
• Learning to Optimize Rewards
• Policy Search
• Hands on Experience using Open AI Gym
• The Credit Assignment Problem
• Q Learning and Deep Q Learning
• Implementing Deep Q Learning using keras

M o d u l e
Month 10 iv. Natural Language Processing
C o u r s e

1. Introduction to Natural Language Processing
2. Overview of NLP and its Applications
3. Data Preprocessing for NLP
4. Key Components - Tokenization, Stemmingand
   Lemmatization
5. Hands on Experience - Generating AI Text
6. Sentiment Analysis in NLP using Keras

D i g i c r o m e

• Neural Machine Translation (NMT)
• Bidirectional Recurrent Neural Networks
• Beam Search
• Sequence to Sequence Model
• Building a basic Encoder Decoder Network for NMT

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• Attention Mechanism
• Introduction to Attention Mechanisms
• Visual Attention
• The Transformer Architecture
• Fine Tuning NLP Models for NLP Tasks
• Hands on Experience - Building a Basic Chatbot
• Natural Language Processing -
• Building a Basic Chatbot like Chat GPT
• How Chat GPT work?
• Perfect execution of Chat GPT using Prompt
  Engineering
• Professional Soft Skills and Final Capstone
  Project
• Month 11
• Introduction to Natural Language Processing

M o d u l e
C o u r s e
D i g i c r o m e
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Module Course Internship
11 Months Course Overview 6 Months of Practical
Module Training 3 Months of Advance AI ML
Training 1 Month Deep Learning using Keras and
TensorFlow Training 1 Month Interview Resume
Building Preparation Note- We have 12 month
internship in this course simultaneously
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Working Tools
COURSE TOOLS MORE
Matplotlib
Chatbot
Stats
CAPSTONE PROJECT
EXPERIENTIAL LEARNING
CASE STUDIES
HACKATHONS
ASSIGNMENTS
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Data Science AI Capstone Project
This Data Science and AI Capstone project will
allow you to implement the skills you learned
throughout this program. Youll learn how to
solve a real-world, industry- aligned Data
Science problem through dedicated mentoring
sessions, from data processing and model building
to reporting your business results and insights.
The project is the final step in the learning
path and will enable you to showcase your
expertise in Data Science to future
employers. Key Learning Objectives Digicromes
online Data Science AI Capstone course will
bring you through the Data Science decision
cycle, including data processing, building a
model and representing results. The project
milestones are as follows Data Processing In
this step, you will apply various data processing
techniques to make raw data meaningful. Model
Building You will leverage techniques such as
regression and decision trees to build
machine-learning models that enable accurate and
intelligent predictions. You may explore Python
to build your model. You will follow the complete
model-building exercise from data splitting, to
testing, training, and validating data using the
K-Fold Cross Validation process. Model
Fine-tuning You will apply various techniques to
improve the accuracy of your model and select the
champion model that provides the best
accuracy. Dashboarding and Representing
Results As the last step, you will be required to
export your results into a dashboard with
meaningful insights using Tableau.
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CAPSTONE PROJECT
Test your skills and mettle with a capstone
project
Web Social Media
Retail Actionable insights for improving sales of
consumer durables Retailers using POS data
analytics Techniques used Market Basket
Analysis, RFM (Recency-Frequency Monetary)
Analysis, Time Series Forecasting E-commerce Tec
hniques used Text Mining, Kmeans Clustering,
Regression Trees, XGBoost, Neural Network
Trapping Social Media exchanges on Twitter-A case
study of the 2015 Floods Techniques used Topic
Modeling using 9 Latent Dirichlet Allocation.
K-Means Hierarchical Clustering
Banking Techniques used Linear Discriminant
Analysis, Logistic Regression, Neural Network,
Boosting, Random Forest, CART
Insurance Personal insurance digital
assistant Techniques used NLP (Natural Language
Processing), Vector Space Model, Latent Semantic
Analysis
Supply Chain Developing a demand forecasting
model for optimizing the supply chain Techniques
used Text Mining, Kmeans Clustering, Regression
Trees, XGBoost, Neural Network Retail
Consumers Market basket analysis for consumer
durables Techniques used Market Basket
Analysis, Brand Loyalty Analysis
Entrepreneurship /Start-Ups Start-up insights
through data analysis Techniques used
Univariate and Bivariate Analysis, Multinomial
Logistic Regression, Random Forest
Healthcare Prediction of user's mood using
smartphone data Techniques used Logistic
Regression, Random Tree, ADA Boost, Random
Forest, KSVM
Finance Accounts Vendor invoicing grief
project Techniques used Conditional Inference
Tree, Logistic Regression, CART and Random Forest
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Project Overview
Every live class focus on practical so Digicrome
ensures you that every topic is done in live
class with proper implementation, Because you get
hire for practical knowledge not for your
theory. LEARN THROUGH REAL-LIFE INDUSTRY
PROJECTS ACROSS INDUSTRIES 12 Projects in Live
Class 25 Practice Project 4 Capstone Project
(Duration 1 month each) 2 Minor Project (Duration
1-2 months) 3 Major Project (Duration 2-3
months) TOTAL 46 PROJECTS, 5 Big Integrated
Projects Dedicated Projects for each domain
Data Science Data Analytics Machine Learning Deep
Learning Computer Vision CNN Text and Time series
RNN Tabular Data ANN Natural Language
Processing Feel confident with a proper exposure
to all domain in AI.
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Course Timeline

• 11 Months -gt 4 Weeks -gt 2 Days 2 Hours Per Day
  ( 200 Hours of Live Training)
• 4 Weeks -gt 2 Days -gt 2 Hours Per Day 16 Hours
  of Live Training Monthly
• 5th Month Onward We Start Mock Interview and Soft
  skill Training
• Capstone 4 Project
• Minor 2 Project
• Major 3 Project Practice Projects 30 Test
• Assignment Exercises
• Major Project (2-3 Month Long) Project-Based
  Interview Prep Mock Interview
• Research and Development Advance Deep Learning
  Advance NLP
• Time Series By Deep Learning
• Mock Interview
• HR Round Preparation Resume Preparation Linkedin
  Profile Review Package Discussion Training 500
  Interview Questions

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Math work at the back end you don't have to do it
manually Just like calculator you give input see
output.
1
Step
How much maths is involved?
Stats is back bone of Data Science and AI but its
simple and you know already most of it like
taking average , line charts ,bar graph etc
2
Step
How much Statistics is involved?
3
Step
No, any one from any back ground can learn it
Is there any Entry Barrier?
We teach all technologies from scratch with baby
steps so it will never be an issue
Step 4
Can Non Technical Enter?
Why Learn AI Today
Top Paying IT Jobs Most in Demand Great
Scope Still Growing Future-Proofing Career Global
Demand
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Program Details
Qualification BE / B.Tech (from any branch), BBA
/ MBA, MCA / M.Tech, B.Com, B.Sc, BA (in any
branch) Note- Must have studied in 12th standard
Course Duration 200 Hours Weekend Batch 11
Months Saturday / Sunday 2 hours/day
Total Class 86
Total Week 43
About Instructors
Experienced software development educators impart
valuable real-world expertise and efficient
strategies, equipping students for achievement in
the field.
TOTAL FEES ? 2,99,000/- Included18 GST
EASY EMI Registration Fee ? 5000/- Financing
Partners
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Main Point
Point
Most Dominating Field in IT right now is
Artificial Intelligence and Data Science
1
Point Highest Paying job from last 5 years 2
Point
More than 30 profiles that you can applay after
doing this training
3
TYPE OF DATA WE CAN USE
Image 1
Video 2 Visual data in the form of Videos.
Visual data in the form of images.
3
Text 4
Audio
Data represented in the form of sound or speech.
Unstructured data in the form of text.
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Tabular
Sequential
The data has a well-defined structure with a
consistent format.
Data representing a sequence of events occurring
in a particular order.
7
8
Rows and Columns
Time Series
Data collected over a period of time at regular
intervals.
Data is arranged in a tabular format with rows
and columns.
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Data Science Salary Trends 2023
Data science roles are undergoing a
transformative surge in demand, becoming
increasingly pivotal for businesses worldwide in
optimizing quality and achieving financial
success. Industries now actively seek
professionals equipped with the right skills and
experience, offering attractive packages to those
who can harness the power of data effectively.
Data Science AI Job Roles Salary Trends
2022-2023 Salary By Experience The average median
salary of a data scientist in India is around INR
16,80,000
Salary By Job roles Over the past 8 years, there
has been a remarkable 60 increase in the salary
bracket for Data Science and Analytics
professionals, highlighting the significant
growth and demand in this dynamic field. The
median salary for Data Science professionals
witnessed a 10 decrease from 2022 to 2023,
reflecting a shift in compensation trends within
the field.
Data Science Salary Groth
By Year 2015 - 2023
16,80,000
15,20,000
14,60,000
12,60,000
12,20,000
11,50,000
10,50,000
9,00,000
7,50,000
2015
2016
2017
2018
2019
2020
2021
2022
2023
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Salary By Cities in INDIA
Salary in Lakhs( Indian Rupees)
30,20,000
30,50,000
26,80,000
25,30,000
25,90,000
27,00,000
24,50,000
25,50,000
19,40,000
20,80,000
18,50,000
14,30,000
16,10,000
17,50,000
13,40,000
14,00,000
14,60,000
13,00,000
8,40,000
7,90,000
7,50,000
6,50,000
5,80,000
5,90,000
5,60,000
5,60,000
5,00,000
New Delhi Gurugram Bengaluru Chennai
Pune
Mumbai Hyderabad
Kolkatta
Gujrat
Average
Min Max Salary By Industry (INR in Lakhs)
10-12 Years
12 Years
Average
0-3 Years
3-6 Years
6-10 Years
35
33.5

30
27.5
26.4
25
21.8
21
21
20
18
16.5
15.7
15 14
14
14.9
12.8
12.8
11.2
11.5
9.2
7.5 9.8
8.8
10
7
7.3
6.5
5
5
0
Travel
Internet / E-Commerce
BFSI
Retail
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10-12 Years
12 Years
Average
0-3 Years
3-6 Years
6-10 Years
35
30.7
30
27.5
28.4
25
25
22.9
21
21
18
20
17.8
16.5
17.8
16.5
16.4
15.2
15
14.5
15
12.5
11
9.8
9
10
7.5
6.9
5.5
5.2
5 0
Pharma
Finance
Telecom
Media
Salary By Job Roles
In the field of data science, professionals have
the opportunity to explore various roles, and
salary trends differ based on job descriptions
and responsibilities. Here is a more
professionally phrased version.
Starting 6,00,000 LPA 6,80,000 LPA 4,80,000
LPA 5,50,000 LPA 7,00,000 LPA 5,00,000
LPA 6,50,000 LPA 5,30,000 LPA 5,00,000
LPA 12,00,000 LPA 6,00,000 LPA 8,00,000
LPA 5,20,000 LPA 5,40,000 LPA 5,90,000
LPA 8,00,000 LPA
Highest 25,00,000 LPA 28,00,000 LPA 12,80,000
LPA 15,90,000 LPA 24,00,000 LPA 18,40,000
LPA 21,80,000 LPA 21,00,000 LPA 15,50,000
LPA 30,50,000 LPA 18,00,000 LPA 24,30,000
LPA 18,00,000 LPA 20,00,000 LPA 17,90,000
LPA 24,70,000 LPA
Data Scientist Data Architect Data Analyst
Business Analyst Machine Learning Engineer
Database Administrator AI Engineer Data Engineer
Marketing Analyst Data Science Manager Data
Visualization Engineer NLP Processing
Engineer Financial Analyst Risk Analyst
Healthcare Analyst Deep Learning Engineer
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Course Main Topic
Artificial Intelligence
Advance Tools
Data Science
Deep Learning Convolutional Neural Network
Computer Vision Image Processing Recurrent Neural
Network Text Modeling Time Series
Modeling Natural Language Processing
Data Analytics Machine Learning Supervised
Regression Time Series Classification
Unsupervised Cluster
Stats My SQL Tableau Google Data Studio Power
BI Excel Python And More...
Certificates

• After the Completion of the Course, You'll get 7
  Professional Certificates
• Course Completion Certificate - Post- Graduate
  Programme in Data Science and Artificial
  Intelligence
• Data Science - Machine Learning
• Fundamentals of Python Programming
• Statistics and Probability
• Data Science - Computer Vision Professional
• Data Science - Natural Language Processing
  Professional
• 12 Month Internship Certificate with one of our
  partner organisations.

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Digicrome
USA Office (USA) 30 N Gould St Ste R Sheridan,
Wyoming 82801 Contact- 013015292014
INDIA Office (IND) C-20 Suite No 02, Sector 2,
Noida, Uttar Pradesh 201301 Contact- 01203131297
More Information
Phone [phone]
www.digicrome.com
Mail info_at_digicrome.com