What you'll learn

  • Core Python
  • Python Pandas
  • Python Numpy
  • Data Analysis
  • Statistics
  • Machine Learning
  • Natural Language Processing
  • Deep Learning
  • Database Interactions
  • Exploratory Data Analysis

Why Learn Data Science Masters At iNeuron?

  • iNeuron is an internationally recognized training institute for data science, machine learning and deep learning.
  • Our faculties consist of industry professionals who have worked as senior data scientists at reputed MNC’s like Verizon, IBM, Cognizant, etc, and are also into 50+ in-house product developments.
  • Our courses are aligned to match the requirements of both freshers as well as working professionals.
  • We not only provide conceptual knowledge but also pay equal attention to practical learning through exposure in live corporate projects via a contribution to our in-house projects.
  • Students:1400+
  • Duration:9 months
  • Major Projects:20
  • Minor Projects:10
  • Mode:Online & Offline
  • Job Assistence:100%
  • Certificate:Yes
  • Mock Interviews:Yes

Course Curriculum

We provide an introduction to data science and its various application in day to day life, along with its importance in the current as well as the future market scenario. Also as the candidate continues his/her journey into the course, we ensure that they have learned and mastered all the required knowledge such as analytical, computational and programming skills to pursue their bright future ahead.

  • Introduction of python and comparison with other programming languages
  • Installation of Anaconda Distribution and other python IDE
  • Python Objects, Number & Booleans, Strings, Container objects, Mutability of objects
  • Operators – Arithmetic, Bitwise, comparison and Assignment operators, Operators Precedence and associativity.
  • Conditions(If else,if-elif-else)
  • Loops(While ,for)
  • Break and Continue statements
  • Range functions

  • String object basics
  • String methods
  • Splitting and Joining Strings
  • String format functions
  • List object basics
  • List methods
  • List as stack and Queues
  • List comprehensions

  • Tuples,Sets,Dictionary Object basics,Dictionary
  • Object methods,Dictionary View Objects. Functions basics,Parameter passing,Iterators
  • Generator functions
  • Lambda functions
  • Map, Reduce, filter functions

  • Creating classes and Objects
  • Inheritance,Multiple Inheritance
  • Working with files
  • Reading and writing files
  • Buffered read and write
  • Other File methods

  • Using Standard Module
  • Creating new modules
  • Exceptions Handling with Try-except
  • Creating ,inserting and retrieving Table
  • Updating and deleting the data.

  • Matplotlib
  • seaborn
  • plotly
  • cufflinks

  • Flask introduction
  • Flask Application
  • Open link Flask
  • App Route Flask
  • URL Building Flask
  • HTTP Methods Flask
  • Templates Flask
  • Request Object

  • Mongo DB
  • SQL lite
  • python SQL

  • Web crawlers for image data sentiment analysis and product review sentiment analysis
  • Integration with web portal
  • Integration with rest api ,web portal and mongodb on Azure
  • Deployment on web portal on Azure
  • Text mining
  • Social media data churn

  • Python Pandas – Series
  • Python Pandas – DataFrame
  • Python Pandas – Panel
  • Python Pandas – Basic Functionality
  • Descriptive Statistics
  • Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Working with Text Data
  • Options & Customization
  • Indexing & Selecting Data
  • Statistical Functions
  • Python Pandas – Window Functions
  • Python Pandas – Date Functionality
  • Python Pandas – Timedelta
  • Python Pandas – Categorical Data
  • Python Pandas – Visualization
  • Python Pandas – IO Tools

  • NumPy – Ndarray Object
  • NumPy – Data Types
  • NumPy – Array Attributes
  • NumPy – Array Creation Routines
  • NumPy – Array from Existing Data
  • Array From Numerical Ranges
  • NumPy – Indexing & Slicing
  • NumPy – Advanced Indexing
  • NumPy – Broadcasting
  • NumPy – Iterating Over Array
  • NumPy – Array Manipulation
  • NumPy – Binary Operators
  • NumPy – String Functions
  • NumPy – Mathematical Functions
  • NumPy – Arithmetic Operations
  • NumPy – Statistical Functions
  • Sort, Search & Counting Functions
  • NumPy – Byte Swapping
  • NumPy – Copies & Views
  • NumPy – Matrix Library
  • NumPy – Linear Algebra

  • Feature_Engineering_and_Selection
  • Building_Tuning_and_Deploying_Models
  • Analyzing_Bike_Sharing_Trends
  • Analyzing_Movie_Reviews_Sentiment
  • Customer_Segmentation_and_Effective_Cross_Selling
  • Analyzing_Wine_Types_and_Quality
  • Analyzing_Music_Trends_and_Recommendations
  • Forecasting_Stock_and_Commodity_Prices

  • Descriptive Statistics
  • Sample vs Population statistics
  • Random Variables
  • Probability distribution function
  • Expected value
  • Binomial Distribution
  • Normal Distributions
  • Z-score
  • Central limit Theorem
  • Hypothesis testing
  • Z-Stats vs T-stats
  • Type 1 type 2 error
  • Confidence interval
  • Chi-Square test
  • ANOVA test
  • F-stats

  • Introduction
  • Supervised, Unsupervised, Semi-supervised, Reinforcement
  • Train, Test, Validation Split
  • Performance
  • Overfitting ,underfitting
  • OLS
  • Linear Regression
  • Assumptions
  • R square adjusted R square
  • Intro to Scikit learn
  • Training methodology
  • Hands on linear regression
  • Ridge Regression
  • Logistics regression
  • Precision Recall
  • ROC curve
  • F-Score

  • Decision Tree
  • Cross Validation
  • Bias vs Variance
  • Ensemble approach
  • Bagging Boosting
  • Randon Forest
  • Variable Importance

  • XGBoost
  • Hands on XgBoost
  • K Nearest Neighbour
  • Lazy learners
  • Curse of Dimensionality
  • KNN Issues
  • Hierarchical clustering
  • K-Means
  • Performance measurement
  • Principal Component analysis
  • Dimensionality reduction
  • Factor Analysis

  • SVR
  • SVM
  • polynomial
  • Regression
  • Ada boost
  • Gradient boost
  • Gaussian mixture
  • Anamoly detection
  • Novelty detection algorithm
  • Stacking
  • K-nn regressor
  • Decision tree regressor
  • DBSCAN

  • Text Analytics
  • Tokenizing, Chunking
  • Document term Matrix
  • TFIDF
  • Sentiment analysis hands on

  • Chatbot using Microsoft Luis
  • Chatbot using google Dialog flow
  • Chatbot using Amazon lex
  • Chatbot using Rasa NLU
  • Deployment of chatbot with web , Telegram , Whatsapp, skype

  • Basic of Neural Network
  • Type of NN
  • Cost Function
  • Gradient descent
  • Linear Algebra basics
  • Vanilla implementation of Neural Network in python
  • Tensorflow In depth
  • Hands on Simple NN with Tensorflow
  • Word Embedding
  • CBOW, Skip-gram
  • Word Relations
  • Hands on word2vec

  • Convolutional Neural Network
  • Maxpool, Window padding
  • Hands On
  • Image classification using Convolutional Neural Network
  • Recurrent Neural Network
  • Long Short Term Memory (LSTM) architecture
  • Building Story writer using character level RNN
  • Sentiment Analysis Hands on
  • Hands on embedding + RNN
  • Seq-to-Seq model
  • Hands on translation
  • Encoder Decoder
  • Hands on cleaning images

  • GAN
  • Generative Model Using GAN
  • BERT
  • Semi-supervised learning using GAN
  • Restricted Boltzmann Machine(RBM) and Autoencoders
  • CNN Architectures
  • LeNet-5
  • AlexNet
  • GoogLeNet
  • VGGNet
  • ResNet
  • SSD
  • SSD lite
  • Faster R CNN

  • SCNN
  • masked R-CNN
  • Xception
  • SENet
  • Facenet
  • Implementing a ResNet-34 CNN Using Keras
  • Using Pretrained Models From Keras
  • Pretrained Models for Transfer Learning
  • Classification and Localization
  • Tensorflow Object Detection
  • You Only Look Once (YOLO)
  • Semantic Segmentation

  • Case Study: Spam Detection
  • Case Study : Anomaly Detection
  • Case Study: Image Classification
  • Case Study : prediction of lungs Disease
  • Case Study: Generating scripts for TV Serials
  • Case Study : google and microsoft speech and vision api integration
  • Case Study: Translation model for languages

Projects

  • Autonomous Tagging of Stack Overflow Questions.
  • Keyword/Concept Identification.
  • Topics Identification.
  • Automated Essay Grading.
  • Sentence to Sentence Semantic Similarity.
  • Fight online abuse.
  • Open Domain Question Answering.
  • Automatic Text Summarization.
  • Copy-cat Bot.
  • Sentiment Analysis.
  • De-anonymization.
  • Univariate Time Series Forecasting.
  • Multivariate Time Series Forecasting.
  • Demand/load forecasting.
  • Predict Blood Donation.
  • Movie Recommender.
  • Search + Recommendation System.
  • Image Classification.
  • Bone X Ray Competition.
  • Image Captioning.
  • Image Segmentation/Object Detection.
  • Video Summarization.
  • Style Transfer.
  • Face Recognition.
  • Clinical Diagnostics: Image Identification classification & segmentation.
  • Satellite Imagery Processing for Socioeconomic Analysis.
  • Satellite Imagery Processing for Automated Tagging.
  • Music/Audio Recommendation Systems.
  • Music Genre recognition using neural networks.

  • Statistical project.
  • Traffic surveillance system.
  • Object identification.
  • Object tracking.
  • Object classification.
  • Tensorflow object detection.
  • Image to text processing.
  • Speech to speech analysis.

  • Vision based Attendance system.
  • Vision based sentiment analysis.
  • Raspberry pi integration.
  • Azure cloud Integration.
  • Deployment in ML devOps pipeline.
  • Autonomous vehicle.
  • Custom object training using TFOD.
  • Google coral deployment of Computer vision project.
  • Nvidia jetson nano deployment of computer vision project.
  • End to end cloud.
  • Deployment of computer vision , machine learning and NLP project.
  • Surveillance system for Warehouse.
  • Truck licence plate detection and its integration with IP camera.
  • Drone image analysis using DJI tello.

Batch Timings & Fee

Offline

Saturday & Sunday

12.30 PM - 2.30 PM

Fee Structure

₹40,000

+ GST
Online

Monday, Wednessday & Friday

10.00 PM - 12.00 AM

Data Science Masters from iNeuron

Complete your Data Science Masters program at iNeuron along with your internship and get your certificate.

  • 6 Months live session training
  • Placement Assistance
  • 3 Months in-house internship
Certificate

Our Features

6 Months Classroom Training

Once on-board our candidates will go through an intense classroom training session of 6 months conducted by our best team of experienced senior data scientists, who will provide all conceptual knowledge in an innovative as well as an interactive manner.

Career Counselling

After successful completion of our course, we train our candidates via mock interviews, personal interviews, and group discussions as well as provide them with professional mentoring and help them build an attractive resume which will enable them to fetch a lucrative job.

3 Months Of In-House Projects

What makes us different from other training institutes is that we are also into product development and that enables us to provide hands-on experience to our candidates to contribute in live projects, which will result in a deeper understanding of the course with industry-level knowledge.

Admission Process

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