Machine Learning And Deep Learning Masters

This is Machine Learning masters and Deep Learning, where you will learn various things from beginning like python , API , deployment in Aws , azure , GCP , Heroku , database , various modules in statistics ,all machine learning algorithm , four mode of Chabot live Dialog flow , Amazon Lex , Azure Luis and RASA NLU , and 15+ live project all together in live instructor led class along with various mode of support and services and doubt clearing session.

Start Date: 10th April 2021
Class Timings: 08:00 AM to 10:00 AM (IST) Saturday - Sunday
Doubt Clearing Session: 10:00 PM to 12:00 AM (IST) Thursday

INR 3540

Course Features
  • Machine Learning in depth from beginning to advance discussion and implementation with Deployment.
  • Deep learning in-depth topic wise discussion and implementation with the project.
  • Docker and Kubernetes end to end with CI/CD pipeline for machine learning.
  • End to End Model Deployment in Azure, GCP, AWS, and Pivotal Cloud.
  • Python spark implementation with the project.
  • Time Series end to end implementation in machine learning and deep learning.
  • 26 + hands-on industry real-time projects.
  • Power BI and Tableau self-placed course.
  • Machine Learning Deep Learning Masters Certificate
  • 200 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 support team
  • Assignment in all the module
  • 20+ use case of Machine learning
  • A live project with real-time implementation
  • Resume building
  • career guidance
  • interview Preparation
  • Regular assessment
  • Job alerts
  • Online Instructor-led learning: Live teaching by instructors
  • Product Demo

Course Overview

This is Machine Learning masters and  Deep Learning, where you will learn various things from beginning like python , API , deployment in Aws , azure , GCP , Heroku  , database , various modules in statistics ,all machine learning algorithm , four mode of Chabot live Dialog flow , Amazon Lex , Azure Luis and RASA NLU , and 15+ live project all together in live instructor led class along with various mode of support and services and doubt clearing session.

What you'll learn
  • Python
  • Stats
  • Machine learning
  • Deep learning
  • Data analytics
  • Mock interview
  • Interview preparation
  • Resume building
  • Dedication
  • Laptop with internet connectivity

Course Curriculum

  • Introduction of Data science and its application in Day to Day life
  • Course overview and Dashboard description
  • Introduction of python and compari s on with other
  • Programming language
  • Installation of Anaconda Distribution and other python
  • IDE Python Objects, Number & Booleans, Strings
  • Container objects, Mutability of objects
  • Operators Arithmetic, Bitwise, C omparison and Assignment o perators, Operators Precedence and associativity
  • Conditions(If else,if elif else) Loops(While ,for)
  • Break and Continue statement and Range Function.
  • String object basics
  • String methods
  • Splitting and Joining Strings
  • String format functions
  • List object basics
  • 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
  • OOPS basic concepts
  • Creating classes and Objects Inheritance
  • Multiple Inheritance
  • Working with files
  • Reading and writing files
  • Buffered read and write
  • Other File methods
  • Exceptions Handling with Try except
  • Flask introduction
  • Flask Application
  • Open linkFlask
  • App RoutingFlask
  • URL BuildingFlask
  • HTTP MethodsFlask
  • Mongo DB SQL
  • Lite python SQL
  • Python Pandas Series
  • Python Pandas DataFrame
  • Python Pandas Panel
  • Python Pandas Basic functionality
  • 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 Distribution 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 assumption.
  • 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
  • K NN Issues
  • Hierarchical clustering K Means
  • Performance measurement
  • Principal Component analysis
  • Dimensionality reduction
  • Factor Analysis
  • SVR
  • S V M
  • Polynomial Regression
  • Ada boost
  • Gradient boost
  • Gaussian mixture
  • Anamoly detection
  • Novelty detection algorithm Stacking
  • K NN regressor
  • Decisson tree regressor DBSCAN
  • Text Ananlytics
  • Tokenizing , Chunking
  • Document term
  • Matrix TFIDF
  • Sentiment analysis hands on
  • Spark overview.
  • Spark installation.
  • Spark RDD.
  • Spark dataframe .
  • 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.
  • Deep Learning Introduction.
  • Neural Network Architecture.
  • Loss Function.
  • Cost Function.
  • Optimizers.
  • CNN architecture.
  • Build First Classifier in CNN.
  • Deploy Classifier over cloud.
  • RNN overview.
  • GRU.
  • LSTM.
  • Time Series using RNN LSTM.
  • Customer Feedback analysis using RNN LSTM.
  • Arima
  • Sarima .
  • Auto Arima
  • Time series using RNN LSTM .
  • Prediction of NIFTY stock price.
  • Deployment of all the project In cloudfoundary , AWS AZURE and Google cloud platform
  • Expose api to web browser and mobile application retraining a pproach of Machine learning model
  • Devops infrastructure for machine learning model
  • Data base integration and scheduling of machine learning model and retraining c ustom machine learning training approach.
  • Discussion on infra cost and data volume
  • P rediction based on streaming data
  • Discussion on project explanation in interview
  • Data scientist roles and responsiblities
  • Data scientist day to day work
  • Companies which hire a data scientist
  • Resume discussion with our team one to one
  • Business Intelligence (BI) Concepts.
  • Microsoft Power BI (MSPBI) introduction.
  • Connecting Power BI with Different Data sources.
  • Power Query for Data Transformation.
  • Data Modelling in Power BI.
  • Reports in Power BI Reports and Visualisation types in Power BI.
  • Dashboards in Power BI.
  • Data Refresh in Power BI.
  • Traditional Visualisation(Excel) vs Tableau.
  • About Tableau.
  • Tableau vs Other BI Tool Pricing.

Course Projects

  • Web crawlers for image data sentiment analysis and product review sentiment analysis
  • Integration with web portal
  • Integration with rest a A pi W eb portal and Mongo DB on Azure
  • Deployment on web portal on Azure
  • Text mining
  • Social media data churn
  • Chatbot using Microsoft Luis
  • Chatbot using google Dialog flow
  • Chatbot using Amazon Lex
  • Chatbot using Rasa NLU
  • Deployemnt of chatbot with web , Telegram , Whatsapp , Skype
  • Healthcare analytics prediction of medicines based on FIT BITband
  • Revenue forecasting for startups
  • Prediction of order cancellation at the time of ordering inventories.
  • Anamoly detection in inventory packaged material.
  • Fault detection in wafferes based on sensordata
  • Demand forecasting for FMCG product.
  • Threat identification in security system.
  • Defect detection in vehicle engine.
  • Food price forecasting with Zomato dataset.
  • Fault detection in wafferes based on sensor data.
  • Cement_Strength _ reg.
  • Credit Card Fraud.
  • Forest_Cover_Classification .
  • Fraud Detection.
  • Income Prediction.
  • Mushroom classifier., Phising Classifier , Thyroid_Detection .
  • Visibility climate.
  • Customer Feedback analysis using RNN LSTM.
  • Family member detection.
  • Industry financial growth prediction.
  • Speech recognization based attendance system.
  • Vehicle Number plate detection and recognition system.
  • Project 1. Project Sales.
  • Project 2. Financial Report.
  • Project 3. HealthCare.
  • Project 4. Procurement Spend Analysis.
  • Project 5. Human Resource Tableau
4.87 out of 5.0
1 Star 3.3%
2 Star 0.0%
3 Star 0.0%
4 Star 0.0%
5 Star 96.7%
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.


Bhagyesh Thakur
August 22,2021

" Best place to learn Data Science, ML "

Ashish Purohit
August 12,2021

" Sudhanshu sir have great teaching ability. "

Nikhil Singh
August 02,2021

" The support team is superb they will train you to resolve errors by yourself but still, you are not able to solve it they will make it happen to end. In my case, I learned to resolve errors by myself with some guidance. Thanks, INeuron Team "

Submit Reviews

You can not rate this course before login

Join Thousand of Happy Students!

Subscribe our newsletter & get latest news and updation!