Data Science - Machine Learning & AI

Data Science – Machine Learning & AI Course In Pune

data science course in pune

Data science is a specialized field that focuses on understanding and imaging specific business, financial, manufacturing and medical research and forecasting. Data science is the process of analyzing, visualizing, extracting, managing, and storing data to gain insights from process analytics. These insights and information help companies analyze their marketing strategies, make powerful data-driven decisions, and create better ads.

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GOVERNMENT CERTIFICATION

  • Central Government Certification For Data Science Program.
  • BECIL – A Govt. of India Enterprise under Ministry of Information & Broadcasting
  • Certification Valid For Private And Government Jobs Also In More Than 82 Countries.
  • Applicable As Per State Vise Government Placements.

Why You Should Choose
Data Science?

INCREASING COMPUTATIONAL POWER

Computational power has been an issue. Managing how much data we have today is becoming a challenge. It is a constant effort to increase our computational power, Here this field helps us to manage the data

WORKING ACROSS VARIOUS SECTORS

With the presence and use of data in various industries and sectors, there is no substitute for data science. Therefore, people with the right skills can have a lot of opportunities.

EASY TO LEARN

Specific data science skills and concepts are relatively easy to learn with proper attention and tuition. So we're here for 1: 1 tuition so you can learn with practice, Our structure of study makes sure you understand everything.

GREAT JOB OPPORTUNITIES

This is a field that is in great demand. There are plenty of job opportunities for career aspirants. The fastest growing science on LinkedIn is currently estimating that 11.5 million jobs will be created in 2026!

HIGHLY IN DEMAND SKILL

Data science is in high demand for specialized professionals, and it is growing. Supply, however, is slow. According to IBM, digital science will employ 28% of all digital jobs.

GROWING PROGRESS

The widespread use of data science has led to growing advances in algorithms and theories developed by researchers. Unfortunately, there are vacancies for 45 days.

Who is a Data Scientist?

A data scientist is a researcher who has to develop large quantities of big data for analysis, develop complex quantitative algorithms to organize and synthesize information, and present results to senior management with a compelling concept. Is.

The data scientist enhances business decision making by introducing maximum speed and better direction throughout the process.

A data scientist must be someone who likes to play with numbers and statistics. A strong analytical mindset combined with strong industrial knowledge is the required set of skills in a data scientist.

He should have above average communication skills and be skilled in communicating technical ideas to non-technical people.

Benefits of Data Science Course

Opportunities:

opportunities for wider education.

Positions:

Abundance of different positions.

Career demand:

A high paying career &
In demand and high price

Work Convenience:

Provides the option to
work from home

Who can join this course?

Freshers
B.E. /BSc candidates
Working professionals
Any graduates for that matter
Interested in having a highly paid career.

Syllabus

DATA SCIENCE SYLLABUS

WHAT YOU WILL LEARN IN DATA SCIENCE – ML AND AI

  • Data Science Mathematics – Revising School Level Math
  • Python Programming Language
  • Python Data Science Libraries
  • Data Science Techniques
  • Basic Of Artificial Intelligence
  • Machine Learning
  • Data Visualization Tools
  • Deep Learning

DATA SCIENCE INTRODUCTION – MODULE I

 

  • Data Science and It’s Concepts
  • Scope Of Data Science
  • Data Science Business and Business Intelligence (BI) Use Cases
  • Data Science Field Discussions
  • Data Science Artificial Intelligence (AI) and AI Subset Machine Learning (ML) and ML Subset Deep Learning (DL) Involvements
  • Analytics – Introduction
  • Understanding Data, Types Of Data
  • Understanding Dataset – Structured, Unstructured and Semi Structured

DATA SCIENCE PROGRAMMING LANGUAGE (PYTHON)– MODULE II

 

  • Python – Introduction and installation
  • Python – Setup and Interpreter
  • Python – Keywords, Statements and Statements Syntax
  • Python – Variables, Literals, Data Types and Data Structure
  • Python – Operators
  • Python – Functions
  • Python – Input and Output (IO)
  • Python – Errors and Exceptions
  • Python – Modules
  • Python – classes
  • Python – Threading and Multi-threading
  • Python – Batteries
  • Python – Package Management Tools: pip and conda
  • Python – Virtual Environments

DATABASES MODULE- III

 

  • Structure Query Language (SQL)
  • SQL – Introduction
  • SQL – Data Definition Language(DDL)
  • SQL – DDL Operations – create tables or views, alter tables or views etc.
  • SQL – Data Manipulation Language(DML)
  • SQL – DML Operations – insert, update and delete etc.
  • SQL – Select
  • SQL – Constraints
  • SQL – Normalizations
  • SQL – Joins and indexes

DATA SCIENCE  LIBRARIES: Numpy, Pandas, Scipy, Scikit-learn, Matplotlib  MODULE  -IV

          Introduction to Anaconda/Jupyter Notebook

Numpy:

  • Introduction to numpy
  • Difference between Python Lists and Numpy
  • Creating arrays
  • Using arrays and Scalars
  • Indexing Arrays
  • Array Random functions
  • Array Search,Sort
  • Array Filter
  • Array Input and Output
  • Exercise on Numpy

Pandas:

  • Introduction to pandas?
  • Where it is used?
  • Index objects
  • Data Structure of Pandas
  • Reindex
  • Drop Entry
  • Selecting Entries
  • Data Alignment
  • Rank and Sort
  • Loc and iloc indexing
  • Summary Statics
  • Handling Missing Data
  • Index Hierarchy
  • Exercise on Pandas

Matplotlib: Data Visualization

  • Introduction of Data Visualization
  • Introduction to Matplotlib
  • Types of plots in Matplotlib
  • 3D plotting with Matplotlib
  • Basic and Specialized Visualization Tools

Scikit-learn

  • Machine learning Process Flow
  • Feature selection and extraction in machine learning

PROBABILITY AND STATISTICS  MODULE – V

  • Probability, Mean, Median, SD, Variance
  • Probability distributions, Poisson distribution, Binomial distribution.

ARTIFICAL INTELLIGENCE AND MACHINE LEARNING (ML) – MODULE VI

 

  • What is Machine Learning (ML)?
  • Introducing Supervised ML
  • Introducing Unsupervised ML
  • Introducing Reinforcement Or Semi Supervised ML
  • Supervised ML Algorithms (Regression and Classification)
  • Unsupervised ML Algorithms (Association and Clustering)
  • Reinforcement ML Algorithms

 

MACHINE LEARNING Algorithms – MODULE VII

  • Linear Regression
  • Polynomial Regression
  • Multinomial Regression
  • Train/Test method
  • KNN
  • K Means
  • Logistic Regression
  • Support Vector Machine 2.5.6
  • Decision Tree
  • Naïve Bayes
  • Ensemble Methods – Random Forest, Boosting and Optimization
  • Clustering and PCA
  • Recommendation system
  • Time Series Analysis

Deep Learning – MODULE VIII

  • Introduction to Deep Learning
  • Importance of Deep Learning
  • Types of Deep Learning
  • Introduction to Tensor flow & Keras
  • Artificial Neural Network
  • Convolutional Neural Network
  • Recurrent Neural Network
  • Introduction to Computer Vision, Open CV library
  •  Recurrent Neural Network
  • Introduction to Natural Language Processing
  • Using Regex in NLP
  • Category of Techniques for NLP
  • Spacy vs NLTK
  • Tokenization in Spacy
  • Stemming and Lemmatization
  • Name Entity Recognition

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