DATA SCIENCE WITH MACHINE LEARNING

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.

Techvolt 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. Techvolt software offers internship in Coimbatore for CSE Students, IT Students, ECE Students, EEE Students, AI 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

Data Science

Data science is the field of study that combines Domain expertise, programming skills and knowledge of statistics and mathematics to extract meaningful insights from data. Data science practitioners apply Machine Learning algorithms to numbers, text, images, videos, audio and more to produce Artificial intelligence systems to perform tasks that ordinarily require human intelligence.

Machine Learning

Machine Learning is a subset of Artificial Intelligence which makes a machine learn without being explicitly programmed. Data is the key for Machine Learning, without a data machine will not be able to get trained in order to predict future. Today Machine Learning is so prominent because of three main factors - Availability of Data, Computational Power and Development in Statistical Methods

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Why Techvolt

  • Live - Technical Training (T & C Apply*)-( 60-100 hrs )
  • Learn from an Expert
  • ISO 9001:2015 Certified
  • Hands on Real-time Projects | Live Projects
  • Technical & Internship Certificates
  • Stipend - 2nd Project
  • Pre-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

Syllabus

Programming for Data Science
Unit-1 : Starting Python & Basics 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-5 : Introduction to Pandas, Operations in Pandas - Part I
  • Series creation, Operations on Series, DataFrames creation, Operations on Data frames
  • Basic Indexing using.loc, iloc, .ix, Multi Indexing, Boolean Indexing
Unit-6 : Operations in Pandas - Part II
  • Grouping of data, Merging and joining data, pivots and reshaping data
Data Analysis and Visualisation
Unit-1 : Exploratory Data Analysis
  • What is Exploratory Data Analysis?
Unit-2 : Data cleansing and transformation
  • Why Data cleansing is important?
  • Treating missing values
  • Data Normalization
Unit-3 : Data Virtualization
  • Types of plot - such as Scatter plot, Pie chart, Histogram, Boxplot
Machine Learning
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 Neighbors
  • 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
Natural Language Processing
Unit-1 : Introduction to Unstructured Data Analysis
  • Introduction to unstructured data
  • Differences in structured and unstructured data
  • Challenges posed due to lack of structure
  • Unstructured data encountered in various applications such as text, speech, multimedia (rich), web and social media data
  • Feature Extraction : text features, speech features, multimedia features, features in web and social media
  • • Document Term Matrix, Term Frequency, Inverse Term Frequency
  • • Count Vectorizer, TFIDF Vectorizer
  • • Text Classification Techniques using Vectorizers
  • Using Baye’s algorithm for text Classification

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