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Cinute Digital

Comprehensive Data Science & Artificial Intelligence - Master Program

This course is tailored to make you a highly skilled Data scientist with skills like Python, ML, R, Deep Learning, NLP and Generative Al.

Job Readiness | Career Guidance and Support | Industry Certifications | Flexible Learning Schedule
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Our Placements

Our Students Who Have landed Their Dream job In

Sessions

93 Lectures


Duration

185 Hrs


Placement

100% Assurance*


Job CTC

Upto 8 LPA*

Sessions

93 Lectures

Duration

185 Hrs

Placement

100% Assurance*

Job CTC

Upto 8 LPA*

Make You Industry-Ready

EXCLUSIVE CAREER

Why Join Us?

Hands-On Training

Experience our interactive, hands-on teaching approach through a free course demo.

Industry-Leading Mentors

Learn from seasoned professionals who have pioneered advancements in their industries.

Job Readiness

Receive personalized career guidance and placement assistance.

Tools and technologies

Expert-Designed Course Structure

Hands-on training with real-world projects.

Gain practical experience by working on industry-relevant projects under expert guidance.

Training From Industry Leading Mentors

Learn from seasoned professionals who have pioneered advancements in their respective fields.

Career guidance and placement assistance.

Receive personalized support to sharpen your job search skills and secure rewarding opportunities.

Flexible learning option

Choose from online or on-premise training modes to suit your preferences and lifestyle.

1:1 Live Sessions

Live one-on-one training assistants via video call, chat and on-site with problem and solution guidance.

Comprehensive Curriculum

Master a wide range of concepts and techniques through a meticulously designed and up-to-date curriculum.

OUR CURRICULUM

Our Interactive Course Content

Python Programming

  1. What Is Python?
  2. Benefits of Learning Python Programming Compared to Other Programming Languages?
  3. Interpreted vs compiled
  4. Dynamic programming
  5. Scripted vs GUI vs Interactive mode

  1. Installation of Anaconda
  2. Installation of PyCharm
  3. Installation of VS Code
  4. How to use VS Code, Jupyter & PyCharm.
  5. How to use google Collab and it’s benefits
     
     

  1. Variables
  2. Datatypes
  3. Operators

  1. Integer
  2. Float
  3. complex

  1. String Slicing
  2. Iterating Over String

  1. List
  2. Tuple
  3. Dictionary
  4. Sets

  1. If Condition
  2. EIif Condition
  3. Else Condition
  4. While Loop
  5. For Loop
  6. Break & Continue Statement

  1. List Unpacking
  2. Tuple Unpacking
  3. Dictionary Unpacking

  1. List Comprehension
  2. Dictionary Comprehension
     
     

  1. Basic Functions
  2. Lambda Functions/Expression
  3. Map() Function/Expression

  1. Palindrome, Fibonacci, factorial
  2. Prime number, divisibility test, count of substring
     
     

Classes (Simple to Advance)
  1. Inheritance
  2. Encapsulation
  3. Polymorphism
  4. Abstraction

Python Libraries for ML

  1. Introduction to NumPy
  2. NumPy Array
  3. Array Attributes
  4. Array Methods

  1. Introduction to Pandas
  2. Pandas Series
  3. Accessing Series Elements
  4. Pandas Data frame – Introduction
  5. Data frame Creation
  6. Reading Data from Various Files
  7. Accessing Data frame
  8. Data frame Sorting
  9. Data frame Concatenation
  10. Data frame Joins
  11. Data frame Merge
  12. Reshaping Data frame
  13. Data frame Operations
  14. Data frame methods - head(),tail, dType, shape, get_dummies
  15. Checking Duplicates
  16. Dropping Rows and Columns
  17. Replacing Values
  18. Missing Value Analysis & Treatment

  1. Plot Styles & Settings
  2. Line Plot
  3. Multiline Plot
  4. Matplotlib
  5. Subplots
  6. Histogram
  7. Boxplot
  8. Pie Chart
  9. Scatter Plot
     
     

  1. Strip plot
  2. Distribution plot
  3. Joint plot
  4. Violin plot
  5. Swarm plot
  6. Pair plot
  7. Count plot
  8. Heatmap

Machine Learning Algorithms

  1. What is DS/ AI/ Ml with examples
  2. ML Types
  3. Algorithm vs model
  4. Using Google colab

  1. Linear Regression with ordinary Least Square (OLS)
  2. Optimization Technique
  3. Linear Regression with Stochastic Gradient Descent (SGD)
  4. Logistic Regression
  5. Decision Trees
  6. Random Forests & Gradient Boosting
  7. Ensemble Techniques
  8. Support Vector Machines (SVM)
  9. Naïve Bayes
  10. KNN algorithm

  1. Principal Components Analysis (PCA)
  2. K-Means Clustering
  3. Implementation of K-Means
  4. Hierarchical Clustering
  5. Types of Hierarchical Clustering
  6. DBSCAN

Data Visualization, Statistics & ML using R programming

  1. Introduction to RStudio's,
  2. RObjects - vectors, list, factors, matrix, arrays and data frames

  1. Without using library
  2. Using GGplot2 library

  1. Mean/Median/Mode
  2. 1 2 3 Quartile
  3. Reading csv and excel file

  1. R project 1 using lm() (linear regression)
  2. R project 2 using glm() (logistic regression)

AI using Tableau

What Is Data Visualization?
 

  1. Data Connection
  2. Dimensions and measures

  1. Working with Metadata
  2. Calculated field
  3. bins and parameters
  4. Mapping
  5. Calculations
  6. AI in Tableau
  7. Dashboard and story
  8. Group
  9. clustering
  10. Forecasting

Deep Learning

  1. Convolutional Neural Network (CNN)
  2. Artificial Neural Network (ANN)

  1. Recurrent Neural Network(RNN)
  2. Long Short Term Memory(LSTM)
  3. Auto encoders

GenerativeAI

  1. Generative Learning foundation models
  2. fine tuning (transfer Learning)

  1. Generative Adversarial Networks (GAN’s)
  2. Variational Auto-encoders(VAE)
  3. Transformer

  1. Bing
  2. Bard
  3. ChatGPT

  1. Stable diffusion
  2. Leonardo.ai
  3. Adobe firefly

  1. veed.io
  2. PlayHT

  1. ICOC
  2. ZERO shot, ONE shot and Few Shot prompting PlayHT

NLP

  1. Introduction to Natural Language Processing
  2. Scope and real- word applications of NLP

  1. Tokenization
  2. Stop word removal
  3. Stemming and Lemmatization
  4. NLTK's POS tagging capabilities

  1. SpaCy's NER capabilities
  2. Understanding syntactic relationships
  3. Analyzing sentence structure with SpaCy

  1. Tensors
  2. NLP with TensorFlow Project

Image Processing

  1. Image Processing Basics

  1. What is OCR

  1. Pytesserect
  2. PyOCR

AAA Accreditation

American Accreditation Association (AAA) accredited Training and Education Provider

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Training Approach

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Real work experience
True, project-based learning
Live sessions & mentorship
Job-ready portfolio
Externship with top companies
Career guidance
Placement Assurance

Eligibility

Undergraduates

This course is structured for any undergraduate or job seeker who wants to start his career in Data Science & Machine Learning field.

Graduates

Any Fresh graduate or post-graduate looking to secure a career in the IT domain.

Professionals

Any working professional with experience in the non-IT domain and looking to enter the IT field.

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Kick-start Your Learning Journey with Our On-boarding Session

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Certification to Career: Let Us Guide Your Path to Success

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