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Data Science with ML

Techvolt Software Pvt. Ltd. offers Internship Training on Data Science with Machine Learning using Python for Freshers. Engineering and Non-Engineering students to enhance their skills as per the Industry standards Techvoll Software offers students internship in Coimbatore in the field of Full Stack development, Web Development, Data Science, Machine Learning, Internet of Things, Cyber Security, Human Resource and Digital Marketing Student's internships are available in offline and online mode. Techvoll software offers internship in Coimbatore for CSE Students, IT Students, ECE Students, EEE Students, Al Students, ML Students, MCA Students and Msc Students along with Hands on projects

Our main objective is to provide intense knowledge on Data Science with Machine Learning Algorithms and required programming knowledge so that an individual is made capable to develop any real-time Analytical project by themselves.

Skills Required: C , C++ with Good Communication.

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Data science combines domain expertise, programming, and statistics to extract insights from data. It uses Machine Learning, a subset of AI, enabling machines to learn without explicit programming. Growth in data, computing power, and statistical methods has made Machine Learning essential for AI systems that handle tasks requiring human intelligence.

Why Techvolt Software?

  • techvolt-internship-datascience3Live-Technical Training (Cost Applicable)-(60 hrs)
  • techvolt-internship-datascience3Learn from an Expert
  • techvolt-internship-datascienceStipend - 2nd Project
  • techvolt-internship-datascienceHands on Real-time Projects | Live Projects
  • techvolt-internship-datascienceTechnical & Internship Certificates
  • techvolt-internship-datascienceStipend - 2nd project
  • techvolt-internship-datasciencePre-Placement Offer

Benefits of Data Science

  • Data Science is in Great Demand
  • A Highly Paid Career
  • Multiple Job opportunities in Industries
  • Data science is accessible to all sectors
  • It provides Deep Analysis of Large Data
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Syllabus

  • An Overview of Python
    • Getting Started
    • Flow Control
    • Sequence Data
    • Defining Functions
    • Working with Files
    • Dictionaries and Sets
    • Errors and Exception Handling
    • Using Modules
    • Regular Expressions
    • Highlights of the standard Library
    • An Introduction to Python Classes
    • Data Wrangling
    • OS Services
    • Graphical Programming
    • Creating Modules
    • Testing and Debugging
    • Fun with Objects
    • Pythonic Programming
    • Network services
  • Unit-1: Starting python & Basic of Python Language
    • What is Python? Why Python for Data Science
    • Programming Model of Python. Python Installation, Simple I/O, Work with Numbers
    • Basic Data types, Variables, Data types, Control Structures, if Conditions, While Loop for Loop, break and continue, Arithmetic & Logical operators
  • Unit-2: Python Core Data Structures
    • Strings, Lists, Tuples, Dictionaries, Sets, List Comprehensions
      Lambda Functions - Maps, Filter, Reduce
  • Unit-3: Functions , Modules and Object Oriented Programming
    • Introduction to Functions, Function Syntax, Introduction to Modules, Create Modules
    • Importing Modules, Introduction to Object oriented concepts
  • Unit-4:Numpy
    • NumPy array creation, NumPy datatypes, NumPy indexing, slicing, Basic Reduction
    • statistical & Logical operations, Array shape manipulation, Array sorting, copies and views

  • Unit 1 : Introduction to Machine Learning and Data Science
    • Introduction to Machine learning
    • Broad classification -Supervised vs Un - supervised Learning
    • Overview of Regression
    • Use cases of Machine Learning
  • Unit 2 : Classification
    • Classification using Decision Trees, Random Forests, Ada Boost
    • Classification using Nearest Neighbours
    • Classification using Naïve Bayes
    • Classification using Logistic Regression
    • Classification using Support Vector Machines
    • Goodness measures such as confusion matrix
    • Ensemble Techniques (Bagging. Boosting)
  • Unit 3 : Validation Measures
    • ROC and AUC Curves comparison of distribution function/business measures
  • Unit 4 : Clustering
    • Introduction to Clustering
    • K - means, Hierarchical Clustering
    • Practical Issues in clustering
    • Validation
    • Applications discussed with case studies

  • Unit 4 : Clustering
    • Introduction to Clustering
    • K - means, Hierarchical Clustering
    • Practical Issues in clustering
    • Validation
    • Applications discussed with case studies
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