What you'll learn

  • Python Core
  • Modules, Exception Handling
  • Rest Api
  • Database
  • Python pandas & Python Numpy
  • Exploratory Data Analysis
  • Statistics
  • Machine Learning
  • Deployment

Why Learn Machine Learning and Deep Learning with Deployment At iNeuron?

Machine Learning and Deep Learning with Deployment at iNeuron take a candidate into the beautiful world of Machine Learning with interactive sessions, HOTS (High Order Thinking Skills) assignments as well as multiple live-projects which enable the candidate to learn and fully understand machine learning model creation as well as end to end deployment.

  • Students:1400+
  • Duration:8 months
  • Major Projects:20
  • Minor Projects:10
  • Mode:Online
  • Job Assistence:100%
  • Certificate:Yes
  • Mock Interviews:Yes

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 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 and 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

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

  • Flask introduction
  • Flask Application
  • Open linkFlask
  • App RoutingFlask
  • URLBuildingFlask
  • HTTP MethodsFlask
  • TemplatesFlask
  • Django end to end

  • 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

  • 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
  • Hands on Sentiment Analysis

  • 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 Baiyes
  • 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

  • Deployment of all the project In cloudfoundary, AWS, AZURE and Google cloud platform.
  • Expose API to web browser and mobile application Retraining approach of Machine learning model
  • Devops infrastructure for machine learning model
  • Database integration and scheduling of machine learning model and retraining.
  • Custom machine learning training approach
  • AUTO ML
  • Discussion on infra cost and Data volume
  • Prediction based on Streaming data

  • Discussion on project explanation in interview
  • Data scientist roles and responsibilities
  • Data scientist day to day work
  • Companies which hire a data scientist
  • Resume discussion with our team one to one

Projects

  • 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

  • Arima
  • Sarima
  • Auto Arima
  • Time series using RNN LSTM
  • Prediction of NIFTY stock price

  • Healthcare analytics prediction of medicines based on FIT BIT Band
  • Revenue forecasting for startups
  • Prediction of order cancellation at the time of ordering inventories
  • Anamoly 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 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 attendence system
  • Vehicle Number plate detection and recognition system

Self placed sessions

  • 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
  • At the End of this course

  • Project Sales
  • Financial Report
  • HealthCare
  • Procurement Spend Analysis
  • Human Resource

Batch Timings & Fee

Fee Structure

₹3,540

incl. GST
Online

Start Date : 6th June 2020

Sat & Sun(Live Class): 3.00 PM - 5.00 PM IST

Thu(Live Doubt Clearing Class): 10.00 PM - 11.59 PM IST

Machine Learning and Deep Learning with Deployment from iNeuron

Complete your Machine Learning and Deep Learning with Deployment at iNeuron and get your certificate.

  • 5 Months live session training
  • Placement Assistance
  • 3 Months remote internship
Certificate

Our Features

5 Months Classroom Training

Once on-board our candidates will go through an intense classroom training session of 5 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 Remote 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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