MLDL

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: 28th August 2021
Class Timings: 8PM to 10PM IST Sat-Sun
Doubt Clearing Session: 10:00 PM to 12:00 AM (IST) Thursday

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
Requirements
  • 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 comparison 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.
  • AUTO ML
  • 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
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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

Sai Kumar Kanna
September 21,2021
5.00

" Very Good course ! Highly Recommended "

Manik Paira
September 18,2021
5.00

" The teaching method is very good, anyone can learn even if the candidate doesn\'t have much idea about the course also. "

someet singh
September 08,2021
5.00

" just joined this Course Excited to be Part of it. "

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