Full Stack data Science with 1 year Internship

Job Guarantee Program

This is a data science full stack live mentor led certification program along with full time one-year internship provided by iNeuron intelligence private limited, where you will learn all the stack required to work in data science, data analytics and big data industry including ML ops and cloud infrastructure and real time industry project and product development along with iNeuron product development team and you will contribute on various level with iNeuron .

Start Date: 20th February 2021
Class Timings: 3 PM to 6 PM (IST) Saturday - Sunday
Doubt Clearing Session: 10 PM to 12 PM (IST) Wednesday
Price

USD 242

Course Features
  • Full stack Data Science master’s certification
  • Job guarantee otherwise refund
  • One year of internship
  • Online Instructor-led learning: Live teaching by instructors
  • 56 + hands-on industry real-time projects.
  • 400 hours live interactive classes.
  • Every week doubt clearing session after the live classes.
  • Lifetime Dashboard access.
  • Doubt clearing one to one
  • Doubt clearing through mail and skype support team
  • Assignment in all the module
  • Quiz in every module
  • A live project with real-time implementation
  • Resume building
  • Career guidance
  • Interview Preparation
  • Regular assessment

Course Overview

Complete stack of data science is covered in this unique program in live class along with this you will get doubt clearing session and you will be able to get 24/7 live support from iNeuron skype team. one-year internship is already included in this program and you will get one year of internship completion certificate and you will work along with iNeuron product development team in various domain according to your alignment and your time availability.
What you'll learn
  • Python
  • Stats
  • Machine learning
  • Deep learning
  • Computer vision
  • Natural language processing
  • Data analytics
  • Big data
  • Ml ops
  • Cloud
  • Data structure and algorithm
  • Architecture
  • Domain wise project
  • Databases
  • Negotiations skills
  • Mock interview
  • Interview preparation
  • Resume building after every module
Requirements
  • Dedication
  • Computer with i3 and above configuration

Course Curriculum


  • a. course overview and dashboard description
  • b. Introduction of data science and its application in day to day life
  • c. Programming language overview
  • d. Installation (tools: sublime, vscode, pycharm, anaconda, atom,jupyter notebook, kite)
  • e. Virtual environment
  • f. Why python
  • a. Introduction of python and comparison with other programming language
  • b. Installation of anaconda distribution and other python ide
  • c. Python objects, number & Booleans, strings.
  • d. Container objects, mutability of objects
  • e. Operators - arithmetic, bitwise, comparison and assignment operators, operator’s precedence and associativity
  • f. Conditions (if else, if-elif-else), loops (while, for)
  • g. Break and continue statement and range function
  • a. basic data structure in python
  • b. String object basics
  • c. String inbuilt methods
  • d. Splitting and joining strings
  • e. String format functions
  • a. List methods
  • b. List as stack and queues
  • c. List comprehensions
  • Dictionary object methods
  • Dictionary comprehensions
  • Dictionary view objects
  • Functions basics, parameter passing, iterators.
  • Generator functions
  • Lambda functions
  • Map, reduce, filter functions.
  • Multithreading
  • Multiprocessing
  • oops basic concepts.
  • Creating classes
  • Pillars of oops
  • Inheritance
  • Polymorphism
  • Encapsulation
  • Abstraction
  • Decorator
  • Class methods and static methods
  • Special (magic/dunder) methods
  • Property decorators - getters, setters, and deletes
  • Working with files
  • Reading and writing files
  • Buffered read and write
  • Other file methods.
  • Logging, debugger
  • Modules and import statements
  • Exceptions handling with try-except
  • Custom exception handling
  • List of general use exception
  • Best practice exception handling
  • What is desktop and standalone application
  • Use of desktop app
  • Examples of desktop app
  • Tinker
  • Kivy
  • SQLite
  • MySQL
  • Mongo dB
  • NoSQL - Cassandra
  • What is web API
  • Difference b/w API and web API
  • Rest and soap architecture
  • Restful services
  • Flask introduction
  • Flask application
  • Open link flask
  • App routing flask
  • Url building flask
  • Http methods flask
  • Templates flask
  • Flask project: food app
  • Postman
  • Swagger
  • Django introduction
  • Django project: weather app
  • Django project: memes generator
  • Django project: blog app
  • Django project in cloud
  • Stream lit introduction
  • Stream lit project structure
  • Stream lit project in cloud
  • Python pandas - series
  • Python pandas – data frame
  • Python pandas – panel
  • Python pandas - basic functionality
  • Reading data from different file system
  • Python pandas – re indexing 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 –time delta
  • Python pandas - categorical data
  • Python pandas – visualization
  • Python pandas - iotools
  • Dask Array
  • Dask Bag
  • Dask DataFrame
  • Dask Delayed
  • Dask Futures
  • Dask API
  • Dask SCHEDULING
  • Dask Understanding Performance
  • Dask Visualize task graphs
  • Dask Diagnostics (local)
  • Dask Diagnostics (distributed)
  • Dask Debugging
  • Dask Ordering
  • Numpy - ND array 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
  • Matplotlib
  • Seaborn
  • Cufflinks
  • Plotly
  • Bokeh
  • Introduction to basic statistics terms
  • Types of statistics
  • Types of data
  • Levels of measurement
  • Measures of central tendency
  • Measures of dispersion
  • Random variables
  • Set
  • Skewness
  • Covariance and correlation
  • Probability density/distribution function
  • Types of the probability distribution
  • Binomial distribution
  • Poisson distribution
  • Normal distribution (Gaussian distribution)
  • Probability density function and mass function
  • Cumulative density function
  • Examples of normal distribution
  • Bernoulli distribution
  • Uniform distribution
  • Z stats
  • Central limit theorem
  • Estimation
  • a Hypothesis
  • Hypothesis testing’s mechanism
  • P-value
  • T-stats
  • Student t distribution
  • T-stats vs. Z-stats: overview
  • When to use a t-tests vs. Z-tests
  • Type 1 & type 2 error
  • Bayes statistics (Bayes theorem)
  • Confidence interval(ci)
  • Confidence intervals and the margin of error
  • Interpreting confidence levels and confidence intervals
  • Chi-square test
  • Chi-square distribution using python
  • Chi-square for goodness of fit test
  • When to use which statistical distribution?
  • Analysis of variance (anova)
  • Assumptions to use anova
  • Anova three type
  • Partitioning of variance in the anova
  • Calculating using python
  • F-distribution
  • F-test (variance ratio test)
  • Determining the values of f
  • F distribution using python
  • linear algebra
  • Vector
  • Scaler
  • Matrix
  • Matrix operations and manipulations
  • Dot product of two vectors
  • Transpose of a matrix
  • Linear independence of vectors
  • Rank of a matrix
  • Identity matrix or operator
  • Determinant of a matrix
  • Inverse of a matrix
  • Norm of a vector
  • Eigenvalues and eigenvectors
  • Calculus
  • Ai vs ml vs dl vs ds
  • Supervised, unsupervised, semi-supervised, reinforcement learning
  • Train, test, validation split
  • Performance
  • Overfitting, under fitting
  • Bias vs variance
  • Handling missing data
  • Handling imbalanced data
  • Up-sampling
  • Down-sampling
  • Smote
  • Data interpolation
  • Handling outliers
  • Filter method
  • Wrapper method
  • Embedded methods
  • Feature scaling
  • Standardization
  • Mean normalization
  • Min-max scaling
  • Unit vector
  • Feature extraction
  • Pca (principle component analysis)
  • Data encoding
  • Nominal encoding
  • One hot encoding
  • One hot encoding with multiple categories
  • Mean encoding
  • Ordinal encoding
  • Label encoding
  • Target guided ordinal encoding
  • Covariance
  • Correlation check
  • Pearson correlation coefficient
  • Spearman’s rank correlation
  • Vif
  • Feature selection
  • Recursive feature elimination
  • Backward elimination
  • Forward elimination
  • Feature engineering and selection.
  • 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
  • Linear regression
  • Gradient descent
  • Multiple linear regression
  • Polynomial regression
  • R square and adjusted r square
  • Rmse , mse, mae comparison
  • Regularized linear models
  • Ridge regression
  • Lasso regression
  • Elastic net
  • Complete end-to-end project with deployment on cloud and ui
  • Logistics regression in-depth intuition
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Hyper parameter tuning
  • Grid search cv
  • Randomize search cv
  • Data leakage
  • Confusion matrix
  • Precision,recall,f1 score ,roc, auc
  • Best metric selection
  • Multiclass classification in lr
  • Complete end-to-end project with deployment in multi cloud platform
  • Decision tree classifier
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Confusion matrix
  • Precision, recall,f1 score ,roc, auc
  • Best metric selection
  • Decision tree repressor
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Performance metrics
  • Complete end-to-end project with deployment in multi cloud platform
  • Linear svm classification
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Soft margin classification
  • Nonlinear svm classification
  • Polynomial kernel
  • Gaussian, rbf kernel
  • Data leakage
  • Confusion matrix
  • precision, recall,f1 score ,roc, auc
  • Best metric selection
  • Svm regression
  • In-depth mathematical intuition
  • In-depth geometrical intuition
  • Complete end-to-end project with deployment
  • Bayes theorem
  • Multinomial naïve Bayes
  • Gaussian naïve Bayes
  • Various type of Bayes theorem and its intuition
  • Confusion matrix
  • precision ,recall,f1 score ,roc, auc
  • Best metric selection
  • Complete end-to-end project with deployment
  • Definition of ensemble techniques
  • Bagging technique
  • Bootstrap aggregation
  • Random forest (bagging technique)
  • Random forest repressor
  • Random forest classifier
  • Complete end-to-end project with deployment
  • Boosting technique
  • Ada boost
  • Gradient boost
  • Xgboost
  • Complete end-to-end project with deployment
  • Stacking technique
  • Complete end-to-end project with deployment
  • Knn classifier
  • Knn repressor
  • Variants of knn
  • Brute force knn
  • K-dimension tree
  • Ball tree
  • Complete end-to-end project with deployment
  • The curse of dimensionality
  • Dimensionality reduction technique
  • Pca (principle component analysis)
  • Mathematics behind pca
  • Scree plots
  • Eigen-decomposition approach
  • Clustering and their types
  • K-means clustering
  • K-means++
  • Batch k-means
  • Hierarchical clustering
  • Dbscan
  • Evaluation of clustering
  • Homogeneity, completeness and v-measure
  • Silhouette coefficient
  • Davies-bouldin index
  • Contingency matrix
  • Pair confusion matrix
  • Extrinsic measure
  • Intrinsic measure
  • Complete end-to-end project with deployment
  • Anomaly detection types
  • Anomaly detection applications
  • Isolation forest anomaly detection algorithm
  • Density-based anomaly detection (local outlier factor) algorithm
  • Support vector machine anomaly detection algorithm
  • Dbscan algorithm for anomaly detection
  • Complete end-to-end project with deployment
  • What is a time series?
  • Old techniques
  • Arima
  • Acf and pacf
  • Time-dependent seasonal components.
  • Autoregressive (ar),
  • Moving average (ma) and mixed arma- modeler.
  • The random walk model.
  • Box-jenkins methodology.
  • Forecasts with arima and var models.
  • Dynamic models with time-shifted explanatory variables.
  • The koyck transformation.
  • Partial adjustment and adaptive expectation models.
  • Granger's causality tests.
  • Stationarity, unit roots and integration
  • Time series model performance
  • Various approach to solve time series problem
  • Complete end-to-end project with deployment
  • Prediction of nifty stock price and deployment
  • Tokenization
  • Pos tags and chunking
  • Stop words
  • Stemming and lemmatization
  • Named entity recognition (ner)
  • Word vectorization (word embedding)
  • Tfidf
  • Complete end-to-end project with deployment
  • Aws segmaker
  • Aure ml studio
  • Ml flow
  • Kube flow
  • H2o
  • Pycaret
  • Auto sklearn
  • Auto time series
  • Auto viml
  • Auto gluon
  • Auto viz
  • Tpot
  • Auto neuro
  • Detail mathematical explanation
  • Neural network overview and its use case.
  • Various neural network architect overview.
  • Use case of neural network in nlp and computer vision.
  • Activation function -all name
  • Multilayer network.
  • Loss functions. - all 10
  • The learning mechanism.
  • Optimizers. - all 10
  • Forward and backward propagation.
  • Weight initialization technique
  • Vanishing gradient problem
  • Exploding gradient problem
  • Visualization of nn
  • Gpu introduction.
  • Various type of gpu configuration.
  • Gpu provider and its pricing.
  • Paper space gpu setup.
  • Running model in gpu
  • Colab pro setup
  • Tensor flow installation 2.0 .
  • Tensor flow installation 1.6 with virtual environment.
  • Tensor flow 2.0 function.
  • Tensor flow 2.0 neural network creation.
  • Tensor flow 1.6 functions.
  • Tensor flow 1.6 neural network and its functions.
  • Keras introduction.
  • Keras in-depth with neural network creation.
  • Mini project in tensorflow.
  • Tensorspace
  • Tensorboard integration
  • Tensorflow playground
  • Netron
  • pytorch installation.
  • Pytorch functional overview.
  • Pytorch neural network creation.
  • Mxnet installation
  • Mxnet in depth function overview
  • Mxnet model creation and training
  • Keras tuner installation and overview
  • Finding best parameter from keras tuner
  • Keras tuner application across various neural network
  • Cnn definition
  • Various cnn based architecture
  • Explanation end to end cnn network
  • Cnn explainer
  • Training cnn
  • Deployment in azure cloud
  • Performance tuning of cnn network
  • Various cnn architecture with research paper and mathematics
  • Lenet-5 variants with research paper and practical
  • Alexnet variants with research paper and practical
  • Googlenet variants with research paper and practical
  • Transfer learning
  • Vggnet variants with research paper and practical
  • Resnet variants with research paper and practical
  • Inception net variants with research paper and practical
  • Darknet variants with research paper and practical
  • Object detection in-depth
  • Transfer learning
  • Rcnn with research paper and practical
  • Fast rcnn with research paper and practical
  • Faster r cnn with research paper and practical
  • Ssd with research paper and practical
  • Ssd lite with research paper and practical
  • Tfod introduction
  • Environment setup wtih tfod
  • Gpu vs tpu vs cpu
  • Various gpu comparison
  • Yolo v1 with research paper and practical
  • Yolo v2 with research paper and practical
  • Yolo v3 with research paper and practical
  • Yolo v4 with research paper and practical
  • Yolo v5 with research paper and practical
  • Retina net
  • Face net
  • Detectron2 with practical and live testing
  • Semantic segmentation
  • Panoptic segmentation
  • Masked rcnn
  • Practical with detectron
  • Practical with tfod
  • Detail of object tracking
  • Kalman filtering
  • Sort
  • Deep sort
  • Object tracking live project with live camera testing
  • Introduction to ocr
  • Various framework and api for ocr
  • Practical implementation of ocr
  • Live project deployment for bill parsing
  • Image captioning overview
  • Image captioning project with deployment
  • Tensorflow js overview
  • Tfjs implementation
  • Tfjs
  • Tflite
  • Tfrt
  • Torch to tf model
  • Mxnet to tf conversion
  • Overview computational linguistic.
  • History of nlp.
  • Why nlp
  • Use of nlp
  • Web scrapping.
  • Text processing
  • Understanding regex.
  • Text normalization
  • Word count.
  • Frequency distribution.
  • Text annotation.
  • Use of annotator.
  • String tokenization
  • Annotator creation.
  • Sentence processing.
  • Lemmatization in text processing
  • Pos.
  • Named entity recognition
  • Dependency parsing in text.
  • Sentimental analysis
  • Spacy overview.
  • Spacy function
  • Spacy function implementation in text processing.
  • Pos tagging, challenges and accuracy.
  • Entities and named entry recognition
  • Interpolation, language models
  • Nltk
  • Text blob
  • Stanford nlp
  • Recurrent neural networks.
  • Long short term memory (lstm)
  • Bi lstm.
  • Stacked lstm
  • Gru implementation.
  • Building a story writer using character level rnn.
  • Word embedding
  • Co-occurrence vectors
  • Word2vec
  • Doc2vec
  • Seq 2 seq.
  • Encoders and decoders.
  • Attention mechanism.
  • Attention neural networks
  • Self-attention
  • Introduction to transformers.
  • Bert model.
  • Elmo model.
  • Gpt1 model
  • Gpt2 model.
  • Albert model.
  • Distilbert model
  • Deep learning model deployment strategies.
  • Deep learning project architecture
  • Deep learning model deployment phase.
  • Deep learning model retraining phase.
  • Deep learning model deployment in aws.
  • Deep learning model deployment in azure.
  • Deep learning model deployment in gcloud.
  • AWS
  • Azure
  • GCP
  • What is big data?
  • Big data application
  • Big data pipeline
  • Hadoop introduction
  • Hadoop setup and installation
  • Spark
  • Spark overview.
  • Spark installation.
  • Spark rdd.
  • Spark data frame.
  • Spark architecture.
  • Spark ml lib
  • Spark NLP
  • Spark linear regression
  • Spark logistic regression
  • Spark decision tree
  • Spark naive bayes
  • Spark xg boost.
  • Spark time series
  • Spark deployment in local server
  • Spark job automation with
  • Scheduler
  • Kafka introduction
  • Kafka installation
  • Spark streaming
  • Spark with Kafka
  • Jenkins
  • Kubernetes
  • Elaticsearch
  • Kibana
  • Git
  • Introduction
  • ER Daigram
  • Schema Design
  • Normalization
  • SQL SELECT Statement
  • SQL SELECT Using common functions
  • SQL JOIN Overview
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL JOIN
  • SQL Best Practice
  • INNER JOIN - Advanced
  • INNER JOIN & LEFT JOIN Combo
  • SELF JOIN
  • Joins & Aggregation - Subqueries
  • Sorting
  • Independent Subqueries
  • Correlated Subqueries
  • Analytic Function
  • Set Operations
  • SQL Views
  • Create a view
  • Create a view using DDL
  • SQL Insert - Advanced Technique
  • INSERT to create a table
  • INSERT new data to an existing table-1
  • INSERT new data to an existing table-2
  • INSERT new data to an existing table-3
  • INSERT new data to an existing table-4
  • SQL Update - Advanced Technique and TCL
  • SQL DELETE and TCL
  • SQL Constraints
  • SQL Aggregations
  • SQL Programmability
  • SQL Query Performance
  • SQL Xtras
  • Microsoft Excel Fundamentals
  • Entering and Editing Text and Formulas
  • Working with Basic Excel Functions
  • Modifying an Excel Worksheet
  • Formatting Data in an Excel Worksheet
  • Inserting Images and Shapes into an Excel Worksheet
  • Creating Basic Charts in Excel
  • Printing an Excel Worksheet
  • Working with Excel Templates
  • Working with an Excel List
  • Excel List Functions
  • Excel Data Validation
  • Importing and Exporting Data
  • Excel PivotTables
  • Working with Excel's PowerPivot Tools
  • Working with Large Sets of Excel Data
  • Conditional Functions
  • Lookup Functions
  • Text Based Functions
  • Auditing an Excel Worksheet
  • Protecting Excel Worksheets and Workbooks
  • Mastering Excel "What If?"Tools
  • Automating Repetitive Tasks in Excel with Macros
  • Macro Recorder Tool
  • Excel VBA Concepts
  • Advance VBA
  • Preparing and Cleaning Up Data with VBA
  • VBA to Automate Excel Formulas
  • Preparing Weekly Report
  • Working with Excel VBA User Forms
  • Importing Data from Text Files
  • Talking about Business Intelligence
  • Tools and Methodlogies used in BI
  • Why Visualization is getting more popular
  • Why Tableau?
  • Gartner Magic Quadrant of Market Leaders
  • Future buisness impact of BI
  • Tableau Products
  • Tableau Architecture
  • BI Project Excecution
  • Tableau Installation in local system
  • Introduction to Tableau Prep
  • Tableau Prep Builder User Interface
  • Data Preparation techniques using Tableau Prep Builder tool
  • How to connect Tableau with different data source
  • Visual Segments
  • Visual Analytics in depth
  • Filters, Parameters & Sets
  • Tableau Calculations using functions
  • Tableau Joins
  • Working with multiple data source (Data Blending)
  • Building Predictive Models
  • Dynamic Dashboards and Stories
  • Sharing your Reports
  • Tableau Server
  • User Security
  • Scheduling
  • PDF File
  • JSON File
  • Spatial File
  • Statistical File
  • Microsoft SQL Server
  • Salesforce
  • AWS
  • Azure
  • Google Analytics
  • R
  • Python
  • Hadoop
  • OneDrive
  • Microsoft Access
  • SAP HANA
  • SharePoint
  • Snowflake
  • Subject
  • Planning
  • Pen & Paper approach
  • Tools
  • Color theme
  • Shapes
  • Fonts
  • Image Selection
  • text position
  • visual placing
  • Story layout & design
  • Dashboard planning
  • Power BI introduction and overview
  • Key Benefits of Power BI
  • Power BI Architecture
  • Power BI Process
  • Components of Power BI
  • Power BI - Building Blocks
  • Power BI vs other BI tools
  • Power Installation
  • Overview of Power BI Desktop
  • Data Sources in Power BI Desktop
  • Connecting to a data Sources
  • Query Editor in Power BI
  • Views in Power BI
  • Field Pane
  • Visual Pane
  • Custom Visual Option
  • Filters
  • Introduction to using Excel data in Power BI
  • Exploring live connections to data with Power BI
  • Connecting directly to SQL Azure, HD Spark, SQL Server Analysis Services/ My SQL
  • Import Power View and Power Pivot to Power BI
  • Power BI Publisher for Excel
  • Content packs
  • Introducing Power BI Mobile
  • Power Query Introduction
  • Query Editor Interface
  • Clean and Transform your data with Query Editor
  • Data Type
  • Column Transformations vs Adding Colums
  • Text Transformations
  • Cleaning irregularly formatted data -Transpose
  • Date and Time Calculations
  • Advance editor: Use Case
  • Query Level Parameters
  • Combining Data – Merging and Appending
  • Data Modelling
  • Calculated Columns
  • Measures/New Quick Measures
  • Calculated Tables
  • Optimizing Data Models
  • Row Context vs Set Context
  • Cross Filter Direction
  • Manage Data Relationship
  • Why is DAX important?
  • Advanced calculations using Calculate functions
  • DAX queries
  • DAX Parameter Naming
  • Time Intelligence Functions
  • Types of visualization in a Power BI report
  • Custom visualization to a Power BI report
  • Matrixes and tables
  • Getting started with color formatting and axis properties
  • Change how a chart is sorted in a Power BI report
  • Move, resize, and pop out a visualization in a Power BI report
  • Drill down in a visualization in Power BI

Course Projects

  • Weeding script
  • Image resizing
  • Jupyter notebook merging, reading etc.
  • Sending emails
  • Weather app
  • Memes generator
  • Food log app
  • Web scrapping
  • Web crawlers for image data sentiment analysis and product review sentiment analysis.
  • Integration with web portal.
  • Integration with rest api, web portal and mongo db. on azure
  • Deployment on web portal on azure.
  • Text mining
  • Social media data churn
  • Mass copy, paste
  • Chatbot using Microsoft Luis
  • Chatbot using google dialog flow
  • Chatbot using amazon lex
  • Chatbot using rasa nlu
  • Deployment of Chabot with web , telegram , WhatsApp, skype
  • Healthcare analytics prediction of medicines based on Fitbit band.
  • Revenue forecasting for startups.
  • Prediction of order cancellation at the time of ordering inventories.
  • anomaly detection in inventory packaged material.
  • Fault detection in wafers based on sensor data.
  • Demand forecasting for fmcg product.
  • Threat identification in security system.
  • Defect detection in vehicle engine.
  • Food price forecasting with zomato dataset.
  • Fault detection in wafers based on sensor data.
  • Cement strength reg.
  • Credit card fraud.
  • Forest cover classification.
  • Fraud detection.
  • Income prediction.
  • Mushroom classifier.
  • phishing classifier
  • Thyroid detection.
  • Visibility climate
  • Traffic surveillance system.
  • Object identification.
  • Object tracking.
  • Object classification.
  • Tensorflow object detection.
  • Image to text processing.
  • Speech to speech analysis.
  • Vision based attendance system
  • Machine translation.
  • Abstractive text summarization.
  • Keyword spotting.
  • Language modelling.
  • Document summarization
  • Deployment and integration with UI machine translation.
  • Question answering (like chat – bot)
  • Sentiment analysis imdb.
  • Text search (with synonyms).
  • Text classifications.
  • Spelling corrector.
  • Entity (person, place or brand) recognition.
  • Text summarization.
  • Text similarity (paraphrase).
  • Topic detection.
  • Language identification.
  • Document ranking.
  • Fake news detection
  • Plagiarism checker
  • Text summarization extractive
  • Text summarization abstractive.
  • Movie review using bert
  • Ner using bert
  • Pos bert
  • Text generation gpt 2
  • Text summarization xlnet
  • Abstract bert
  • Machine translation
  • Nlp text summarization custom
  • Keras/tensorflow
  • Language identification
  • Text classification using fast bert
  • Neuralcore
  • Detecting fake text using gltr with bert and gpt2
  • Fake news detector using gpt2
  • Python plagiarism checker type a message
  • Question answering
  • Topic modeling.
  • Word sense disambiguation
  • Text to speech
  • Keyword spotting
  • Document ranking
  • Text search (with synonyms)
  • Language modeling
  • Spam detector
  • Image captioning
  • Ecommerce Analysis - Tableau Integration
  • Sales Data Analysis - Tableau Integration
  • E-Commerce Customer Analysis
  • Project Management Dashbaord
  • Sales Dashboard
  • Human Resource - Tableau
  • Supply Chain - Tableau
  • Sale Return - Tableau
  • Cost Insights - Power BI
  • Management Insights- Power BI
  • Retail Insights- Power BI
5.00 out of 5.0
1 Star 0.0%
2 Star 0.0%
3 Star 0.0%
4 Star 0.0%
5 Star 100.0%
Krish Naik & Sudhanshu kumar

Having 7+ years of experience in Big data, Data Science and Analytics with product architecture design and delivery. Worked in various product and service based Company. Having an experience of 5+ years in educating people and helping them to make a career transition.

Reviews

Shanthi Rama
February 27,2021
5.00

" Great work. Thanks to the whole team. "

Nikhil Telang
February 22,2021
5.00

" Awesome "

Garlapati Revanth Kumar
February 21,2021
5.00

" Excellent work and content..Thank you krish and sudhanshu for creating such an amazing course. Great work guys keep it up even support team is amazing. Only one suggestion is please help students by creating at least 1 project from each module in live class instead of executing all exisiting projects or pre recorded projects..This will boost confidence among students.Hope you take this as positive and improve in this part. Apart from this excellent support and teaching from you guys..keep rocking. "

Submit Reviews

You can not rate this course before login

Join Thousand of Happy Students!

Subscribe our newsletter & get latest news and updation!