Welcome to Huddlerise Academy
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$50.0
170 SEATS

Course Details

India is the second-largest country in the world to hire Data Scientists in which the number of vacancies has been increased by 53 percent. Due to huge demand for career opportunities in data, Data Scientist Salary package in India grown eventually. Our Data Science certification course in Bangalore will promote you to be a professional Data Scientist and also offering Data Science Course with 100% placements. If you are really interested to Learn Data Science course in Bangalore, then Huddle Rise is the Right place with real hands-on experience.

We have built careers of thousands of Data Science professionals with 100% job assistance in various MNCs. “Training to Job Placement” – is our vision.Data science is an essential part of any industry today, given the massive amounts of data that are produced. Data science is one of the most debated topics in the industries these days. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction.Our Data Science Course in Bangalore kick starts from statistics and insights of the large volume of Data to provide deep knowledge for the betterment of your future. During our Data Science classes, you will be practicing many real-world practical scenarios and assessments to strengthen you Data Science skills. At the end of the course, you will be transformed into a highly qualified Data Scientist which had promoted us as Best Data Science Training Institute in bangalore.

3 months Intensive Training By IITN's

Requirements

  • Model. Standard x86 (32-bit) or x86 (64-bit) compatible desktop or laptop computer.
  • Memory. At least 1GB of RAM.
  • Operating Systems. The following operating systems are supported: Windows 10, 32- or 64-bit versions.
  • Model. 64-bit Intel-based model.
  • Memory. At least 2GB of RAM.
  • Operating Systems. Mac OS X Mavericks (10.9.x)
  • Programming: Python, SQL, Scala, Java, R, MATLAB.
  • Machine Learning: Natural Language Processing, Classification, Clustering
  • Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries.
  • Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera
  • Supported web browsers -Google Chrome (recommended)

SECTION 1 : INTRODUCTION

Lessons 1: Introduction to R

  • Overview of R, R data types and object
  • Reading and writing data
  • Control structures, functions, scoping rules
  • Use of Python in Data Science
  • Where to use python in real time
  • Python Environment setup, operators
  • Simple if Statement, if-else Statement
  • if-elseif Statement.
  • Introduction To while Loops.
  • Introduction To for Loops, Using continue and break
  • Python Data Type
  • List, Tuples, Dictionaries
  • Python Lists, Tuples, Dictionaries
  • Accessing Values
  • Basic Operations
  • Indexing, Slicing
  • Built-in Functions & Methods
  • Defining Functions
  • Calling Functions
  • Anonymous Functions – Lambda
  • Numpy
  • Pandas
  • Matplot lib
  • Scikit learn
  • Numerical Analylsis
  • Categorical Analysis
  • Visualizing Data: Box, Scatter, Bar & Histogram
  • What is Machine Learning?
  • What is the Challenge?
  • Introduction to Supervised Learning,Unsupervised Learning.
  • Introduction to Linear Regression
  • Linear Regression with Multiple Variables
  • Disadvantage of Linear Models
  • Interpretation of Model Outputs
  • Understanding assumptions of linear regression
  • Describe or sumarise a set of data
  • The mean,median,mode, Kurtosis and skewness
  • Computing Standard deviation and Variance
  • Covariance, Correlation and Causation
  • Introduction to Logistic Regression.– Why Logistic Regression
  • Introduce the notion of classification.
  • Cost function for logistic regression
  • Confusion Matrix, Odd’s Ratio And ROC Curve
  • Advantages And Disadvantages of Logistic Regression
  • How to build decision tree?
  • Understanding Kart Model
  • Classification Rules- Overfitting Problem
  • Stopping Criteria And Pruning
  • Model A decision Tree, Bayes
  • Random Forests
  • Hierarchical Clustering
  • k-Means algorithm for clustering – groupings of unlabeled data points.
  • Principal Component Analysis(PCA)
  • Non-probabilistic Parsing, Probability
  • Information Theory, Language modeling and Naive Bayes
  • Viterbi Algorithm for Finding Most Likely HMM Path
  • Lexical Semantics, Sentiment Analysis
  • Production system, Ontology
  • Propositional logic,Pattern Recognition
  • Distance-Based Neural Networks
  • Population-based search
  • Introduction to deep learning
  • Deep L-layer neural network
  • Forward Propagation in a Deep Network.
  • Why deep representations, Forward and Backward Propagation
  • Parameters vs Hyperparameters
  • John Doe

    Professor

    There are many variations of passages of Lorem Ipsum available, but the majority have suffered altera tion in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum

    There are many variations of passages of Lorem Ipsum available, but the majority have suffered altera tion in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum

    Naila Naime

    Bachelor

    There are many variations of passages of Lorem Ipsum available, but the majority have suffered altera tion in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum

    Reviews

    Kota Mahesh

    Feb 22, 2019

    Excellent quality content! It's a great introductory course that really gets you interested in Data Science. I would highly recommend it to anyone curious in learning about what Data Science is about.

    Krishna M

    Feb 24, 2020

    Terrific introduction to the Data Science course. Never expected but was extremely excited with the quality of content, speakers and a very honest attempt to making this course interesting.

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