DATA SCIENCE COURSE TRAINING IN HYDERABAD

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Learnai.co.in offers a comprehensive range of professional IT and Non-IT courses that are  designed to cater to an aspiring group of professionals who went a tailored program on  making them career ready. Our programs are driven by a constant need to be job achieving by following industry-oriented course curriculum and stimulating, taking into consideration  the dynamic nature of technology with practical knowledge work on different modules,  tools and are taught by world-class professionals with specific domain expertise. Simply one stop learning for all trending technologies.

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What is Data Science?

Data science is the study of data. It involves developing methods of recording, storing, and  analyzing data to effectively extract useful information. The goal of data science is to gain  insights and knowledge from any type of data — both structured and unstructured.

Why to learn Data Science?

Data science can be defined as a blend of mathematics, business understanding, tools,  algorithms and machine learning techniques, all of which help us in finding out the hidden  insights or patterns from raw data which can be of major use in the formation of big  business decisions

Who can Pursue Data Science?

  • Any Degree pursuing or Graduated,
  • Bachelor’s, Master’s, PhD, and
  • Anyone interested in course
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ServicesBasicIntermediateAdvanced ExpertLead
Members>20<15<10<10 <5
Duration45+ Days2+ Months3+ Months6+ Months1+ Year
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COURSE CURRICULUM

Python

  • Introduction
  • Installation
  • Fundamental of Python
  • Variables
  • Comments
  • Print Statement
  • Operators
  • Mutable Data Types
  • Data Types
  • Special Data Types
  • Conditions
  • Loops
  • Functions
  • Break, Continue and Pass Statements
  • String Object and working
  • List
  • Tuple
  • Set
  • Dictionaries
  • Map
  • Reduce
  • Filter
  • Classes
  • Objects
  • Inheritance
  • Multiple Inheritance
  • Modules
  • Error Handling

Data Visualization

  • Matplotlib
  • Seaborn
  • Plotly
  • Cuflinks
  • Bokesh

Libraries

  • Numpy
  • Pandas
  • Random
  • Math
  • Scipy
  • sklearn
  • Keras
  • Tensorflow
  • OpenCV
  • NLTK
  • Spacy
  • Lot more…

Tableau

  • Introduction to Data Visualization
  • Introduction to Tableau
  • Basics charts and dashboards
  • Special Char Types
  • Dashboard design and principles
  • Connections with servers
  • Local file access
  • Hands on experience with worksheet

Web Scraping

  • Url
  • Beautiful Soup

Data base

  • SQL
  • MongoDB

Statistics

  • Basics of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Qualitative Analysis
  • Quantitative Analysis
  • Hypothesis Testing
  • Data Distribution
  • Probability Distribution
  • Normal Distribution
  • Poison Distribution
  • Outlier Detection
  • Other Statistical Fundamentals

Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Semi Supervised Learning
  • Reinforcement Learning

Supervised Learning

  1. Regression Models
  2. Classification Models

Regression Models

  • Introduction to Regression Models
  • Linear Models
  • Linear Regression
  • Multiple Regression
  • Ordinary Least Square Method
  • Non-Linear Models
  • Support Vector Regressor
  • Random Forest Regressor
  • Decision Tree

Evaluation Metrics for Regression Models

  • R-Square
  • Adjusted R Square
  • Mean Square Error
  • Root Mean Square Error
  • Mean Absolute Error

Classification Models

  • Introduction to Classification Models
  • Logistics Regression
  • Naïve Bayes
  • Support Vector Classifier
  • K-NN Classifier
  • Decision Tree Classifier
  • Random Forest Classifier

Evaluation Metrics for Classification Models

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • ROC Curve

Unsupervised Learning

  • Introduction to Unsupervised Learning

Clustering Models

  • Introduction to Clustering Models
  • K- Mean Clustering
  • Hierarchical Clustering
  • High Dimensional Clustering

Dimension Reduction

  • Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

  • Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

  • Feature Extraction
  • Model Defects & Evaluation Metrics
  • Model Selection and Tuning
  • Comparison of machine learning models

Neural Networks

  • Introduction to Deep Learning
  • Fundamental of Neural Networks
  • TensorFlow and Keras
  • Artificial Neural Networks (ANN)
  • Convolution Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Evaluation of Deep Learning
  • Neural Networks Basics
  • Gradient Descent
  • Introduction to Perceptron & Neural Networks
  • Batch Normalization
  • Activation and Loss Functions
  • Hyper parameter tuning
  • SoftMax
  • Deep Neural Networks
  • Weights initialization

Image Pre-processing

  • Computer Vision
  • Open CV
  • Noise Detection
  • Noise Reduce
  • Low Pass Filters
  • Forward Propagation
  • Backward Propagation
  • Pooling & Padding

Natural Language Processing

  • Introduction to NLP
  • Corpus
  • Natural Language Understanding (NLU)
  • Natural Language Generator (NLG)
  • Tokenization (Word, Sentence, Blank, RegEx)
  • Frequency Distribution
  • Filtering
  • Stemming
  • Lemmatization
  • Stop Words
  • Regular Expressions
  • POS Tagging
  • Syntax Tree
  • Chunking
  • Lemmas
  • Hypernyms
  • Hyponyms
  • Synonyms
  • Antonyms
  • Distance Between words
  • Wordnet
  • Name Entity Recognition
  • Bag of words or Document Matrix
  • Count Vectorization
  • Term Frequency
  • Inverse Document Frequency
  • Sentimental Analysis

Job Roles

  • Business Analytics Professional
  • Business Intelligence Professional
  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Big Data Analysts
  • HR Analytics Professionals
  • Marketing Analytics Professionals

Components of Data Science

  • Business Understanding
  • Understanding Problem Statement
  • Data Mining
  • Data Cleaning
  • Data Analysis
  • Data Analytics
  • Data Exploration
  • Stats and Mathematics
  • Feature Engineering
  • Feature Selection
  • Feature importance
  • Modeling
  • Predictive Modeling
  • Deploying Model

What We Offer

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Industrial Standard Course Structure
Job Oriented Programs
Recording Sessions
Assignments on Real time Scenarios
Internships
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Job Support
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Working on Real Time Projects
Course Completion Certifications

We Provide Higher Quality Services

AND YOU’LL GET SOLUTIONS FOR EVERYTHING
Best Artificial Intelligence Training in Hyderabad with 100% placement Assistance. Learn Data Science with Python, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Statistics and Tableau.

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