iNeuron Full Stack Data Science BootCamp Live Classes and Tasks

Name : Mohammad Wasiq

Batch Number : DSB177

Here I have uploaded the Live Classes Codes the Tasks

1. Python
2. OOPs
3. MySQL
4. MongoDB
5. Pandas
6. NumPy
7. Data Visualization
8. API
9. Web Scrapping
10. Statistics
11. Exploratory Data Analysis and Feature Engineering-I
12. Hypothesis Testing Using Python
13. Exploratory Data Analysis and Feature Engineering-II
14. Machine Learning

• 1. Linear Regression
  • Linear Regression
  • Ridge Regression
  • Lasso Regression
  • Elastic-Net Regression
• 2. Logistic Regression
• 3. Support Vector Machine (Classification)
• 4. Support Vector Machine (Regressor)
• 5. Decision Tree Classifier
• 6. Decision Tree Regressor
• 7. Random Forest
• 8. Adaboost
• 8.1 Gradience & Xgboosst
• 9. Clustering
• 10. Naive Bayes
• 11. Principal Component Analysis (PCA)

15. Time Series
16. Deep Learning and Computer Vision
    • Download the flower folder

• Datasets1
• Datasets2

Course Features

• Full stack Data Science master’s certification
• Job guarantee otherwise refund
• One year of internship Anytime
• 1:1 Personalized Mentorship
• Revision Classes
• Online Instructor-led learning: Live teaching by instructors
• 56 + hands-on industry real-time projects
• 500 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
• A live project with real-time implementation
• Resume building Anytime
• Career guidance Anytime
• Interview Preparation Anytime
• Regular assessment
• Job Fair and Internal Hiring
• Mock Interview Anytime

Subjects

1. Python

2. Statistics

3. Machine Learning

4. Deep Learning

5. Computer Vision

6. Natural Language Processing

7. Data Analytics

8. Big Data

9. MLOPs

10. Cloud

11. Architecture

12. Domain wise Project

13. Databases

14. Negotiations Skills

15. Mock Interview

16. Interview Preparation

17. Resume Building After Every Module

18. Industry Grade Projects

Course Curriculum

1. Python Basics

• Python Introduction, Installation and Setup
• Python Basics & Conditionals
• Conditionals & Loops
• Working with Loops
• Working with Strings & Lists
• List manipulation
• Tuple, Set & Dictionary
• Working with Functions
• Functions, Generators & File Handling
• Logging and Debugging
• Modules and Exception

2. OOPS

• OOPs, Classes & Objects
• OOPS, Abstraction & Inheritance
• Inheritance, Polymorphism & Intro to Databases

3. Databases

• Working with SQL & Python
• SQL Continued, MongoDB Installation & Working with MongoDB

4. Pandas

• Introduction to Pandas
• Pandas Basics
• Pandas Data Manipulation
• Working with Pandas

5. Numpy

• Introduction to Numpy

6. Matplotlib

• Working with Pandas & Matplotlib

7. Plotly

• Working with Plotly

8. Seaborn

• Working with Seaborn

9. EDA

• Expolartory Data Analysis

10. Web Frameworks

• Rest API, Flask & Working with Postman
• Working with Flask & Debugging Calculator Application

11. Python Projects with Deployment

• Project Discussion Review Scraper with Deployment on Heroku, AWS and Azure
• Project Discussion Advance Review Scraper

12. Statistics

• Different types of Statistics
• Population vs Sample
• Mean, Median and Mode
• Variance, Standard Deviation
• Sample Variance why n-1
• Standard Deviation
• Variables
• Random Variables
• Percentiles & Quartiles
• 5 Number Summary
• Histograms
• Gaussian - Normal Distribution
• Standard Normal Distribution
• Application Of Z-Score
• Basics Of Probability
• Addition Rule In Probability
• Multiplication Rule in Probability
• Permutation
• Combination
• Log Normal Distribution
• Central Limit Theorem
• Statistics - Left Skewed And Right Skewed Distribution And Relation With Mean, Median And Mode
• Covariance
• Pearson And Spearman Rank Correlation
• What is P Value
• What is Confidence Intervals
• How To Perform Hypothesis Testing - Confidence IntervalZ Test Statistics Derive Conclusion
• Hypothesis testing part 2
• Hypothesis testing part 3
• Finalizing Statistics

13. Introduction to Machine learning

• Linear Regression
• Lasso Regression
• Ridge Regression
• Elastic Net Regression
• Logistic Regression
• Decision Tree (Regression)
• Decision Tree (Classification)
• Ensemble Technique
• Random Forest (Regression)
• Random Forest (Classification)
• Boosting
• XG Boost
• K-Nearest Neighbour (KNN)
• Support Vector Machine (Regression)
• Support Vector Machine (Classification)
• Bagging Classifier
• Stacking
• Clustering
• PCA
• DBSCAN
• Naive Bayes

14. Time Series

• Arima, Sarima, Auto Arima
• Time Series using RNN LSTM, Prediction of NIFTY Stock Price

15. Deep Learning

• Introduction to Deep Learning
• Importance of Deep learning
• Why you should study Deep Learning? (Motivation)

DL ANN - Introduction

• ANN vs BNN
• The first Artificial Neuron

DL ANN - Perceptron

• Overview of Perceptron
• More about Perceptron
• Perceptron implementation using python - 1
• Perceptron implementation using python - 2
• Perceptron implementation using python - 3
• Perceptron implementation using python - 4
• Perceptron implementation using python - 5
• Perceptron implementation using python - 6
• Perceptron implementation using python - 7
• Python scripting & modular coding for Perceptron
• Python logging basics and docstrings

DL ANN -1

• Multilayer Perceptron
• Forward propagation
• Why we need Activation function?
• ANN implementation using tf.keras - 1
• ANN implementation using tf.keras - 2
• ANN implementation using tf.keras - 3
• ANN implementation using tf.keras - 4
• ANN with Callbacks | Tensorboard | Early Stopping | Model Checkpointing

DL ANN - 2

• Vector
• Differentiation
• Partial Differentiation
• Maxima and Minima Concept
• Gradient Descent Basics
• In-depth understanding of Gradient descent with mathematical proof

DL ANN - 3

• Chain Rule
• Back Propagation

DL ANN - 4

• General problems in training Neural Networks
• Vanishing and Exploding gradients
• Activation Function Basics
• Weight initialization
• Activation Functions - 1
• Activation functions - 2
• Activation functions - 3
• Transfer learning
• Batch normalization -1
• Batch normalization -2
• Batch normalization -3

DL ANN - 5

• Introduction to fast optimizers
• Momentum optimization
• NAG
• Loss functions
• Regularization
• Dropout

16. Computer Vision

Introduction to Course

• Course Overview
• Installing Anaconda, Pycharm & Postman
• Working with Conda Envs
• Pycharm Introduction
• Pycharm with Conda
• Pycharm with venv
• Pycharm with Pipenv

CNN Foundations

• Why CNN? Building an Intution for CNN
• CNN, Kernels, Channels, Feature Maps, Stride, Padding
• Receptive Fields, Image Output Dimensationality Calculations, MNIST Dataset Explorations with CNN
• MNIST CNN Intutiton, Tensorspace.js, CNN Explained, CIFAR 10 Dataset Explorations with CNN
• Dropout & Custom Image Classification Dog Cat Dataset
• Deployment in Heroku, AWS, Azure

CNN Architectures

• LeNet-5
• LeNet-5 Practical
• AlexNet
• AlexNet Practical
• VGGNet
• VGG16 Practical
• Inception
• Inception Practical
• ResNet
• Resnet Practical

Data Augmentation

• What is Data Augmentation?
• Benefits of Data Augmentation
• Exploring Papers like RICAP, Random Erasing, Cutout
• Exploring Augmentor
• Exploring Roboflow

bject Detection Basics

• What is Object Detection?
• Competitions for Object Detection
• Bounding Boxes
• Bounding Box Regression
• Intersection over Union (IoU)
• Precision & Recall
• What is Average Precision?

Object Detection Architectures

• Object Detection Family
• RCNN
• RCNN Network Architecture
• Cons of RCNN
• FAST RCNN
• FAST RCNN Network Architecture
• Cons of FAST RCNN
• FASTER RCNN
• FASTER RCNN Network Architecture
• YOLO
• YOLO Architecture
• YOLO Limitations

Practicals Object Detection using Tensorflow 1.x

• Introduction to TFOD1.x
• Using Google Colab with Google Drive
• Installation of Libraries in Colab
• TFOD1.x Setup in Colab
• Visiting the Model Zoo
• Inferencing in Colab
• Inferencing in Local
• Important Configurations Files
• Webcam Testing

Practicals Training a Custom Cards Detector using Tensorflow1.x

• Custom Model Training in TFOD1.x
• Our Custom Dataset
• Doing Annotations or labeling data
• Selection of Pretrained Model from Model Zoo
• Files Setup for Training
• Let's start Training in Colab
• Export Frozen Inference Graph
• Inferencing with our trained model in Colab
• Training in Local
• Inferencing with our trained model in Local

Practicals Creating an Cards Detector Web App with TFOD1

• Code Understanding
• WebApp Workflow
• Code Understanding
• Prediction with Postman
• Debugging our Application

Practicals Object Detection using Tensorflow 2.x

• Introduction to TFOD2.x
• Using the Default Colab Notebook
• Google Colab & Drive Setup
• Visting TFOD2.x Model Garden
• Inference using Pretrained Model
• Inferencing in Local with a pretrained model

Practicals Training a Custom Chess Piece Detector using Tensorflow2

• Custom Model training in TFOD2.x
• Our Custom Dataset TF2
• File Setup for Training
• Let's start Training
• Let's start Training
• Stop Training or resume Training
• Evaluating the trained model
• Convert CKPT to Saved Model
• Inferencing using the Custom Trained Model in Colab
• Inferencing using the Custom Trained Model in Local PC

Practicals Creating an Chess Piece Detector Web App with TFOD2

• Creating a Pycharm project & Environment Setup TF2
• Application Workflow
• Code understanding
• Testing our App with Postman
• Debugging our Application

Practicals Object Detection using Detectron2

• Introduction to Detectron2
• Detectron2 Colab Setup

Practicals Training a Custom Detector using Detectron2

• Detectron2 Custom Training
• Exploring the Dataset
• Registering Dataset for Training
• Let's start Training
• Inferencing using the Custom Trained Model in Colab
• Evaluating the Model

Practicals Creating an Custom Detector Web App with Detectron2

• Creating a Pycharm project & Environment Setup Detectron2
• Application Workflow
• Code understanding
• Testing our App with Postman
• Debugging our Application

Practicals Object Detection using YoloV5

• Introduction to YoloV5
• YoloV5 Colab Setup
• Inferencing using Pre Trained Model

Practicals Training a Custom Warehouse Apparel Detector using YoloV5

• Custom Training with YoloV5
• Exploring the Dataset
• Doing Annotations or labeling data
• Setting up Google Colab & Drive
• Let's start Training
• Inferencing using the Custom Trained Model in Colab

Practicals Creating an Warehouse Apparel Detector Web App with YOLOV5

• Creating a Pycharm project & Environment Setup Yolo
• Application Workflow
• Code understanding
• Testing our App with Postman
• Debugging our Application

Image Segmentation

• Segmentation Introduction
• From Bounding Box to Polygon Masks
• What is Image Segmentation?
• Types of Segmentation
• MASKRCNN
• MASK RCNN Architecture

MASK RCNN Practicals with TFOD

• Segmentation with TFOD1.x
• Local Setup MASKRCNN
• Exploring the Dataset
• Data Annotation
• Model Selection
• Files Setup for Training
• Model Training
• Export Frozen Inference Graph
• Model Prediction

MASKRCNN practical with Detectron2

• Introduction to Detectron2
• Data Preparation
• Setup for Training
• Let's start Training
• Inferencing using the Custom Trained Model in Colab
• Evaluating the Model

Face Recognition Project

• Introduction to Project
• Requirement Gathering
• Techstack Selection
• Project Installation
• Project Demo
• Project Workflow
• Core Components of the Application
• Data Collection Module
• Generate Face Embeddings
• Training Face Recognition Module
• Prediction Pipeline Entry point of the Application
• Application Workflow
• Debugging our Application

Object Tracking Project

• Object Tracking project
• Project Installation Tracking
• Project Demo
• Code Understanding

GANS

• Introduction to GANS
• GAN Architecture
• GAN PRACTICALS Implementation

Fashion Apparel Detection

• Introduction to Fashion Apparel Detection project
• Requirement Gathering
• Techstack Selection
• Detailed Project Workflow
• Data Collection
• Data Preparation
• Data Augmentation
• Data Annotations

Image TO Text OCR

• Introduction to Project
• Project Installation OCR
• Project Demo

Shredder System

• Introduction to Shredder Systems
• Requirement Gathering
• Techstack Selection
• Data Collection
• Data Augmentation
• Data Preparation
• Data Annotation
• Model Selection from Zoo
• Model Training

Automatic Number plate Recognition with TFOD1.x

• Introduction to ANPR Project
• Requirement Gathering
• Tech Stack Selection
• Data Collection
• Data Augmentation
• Data Preparation
• Data Annotation

16. Natural Language Processing

NLP Overview

• NLP Overview
• NLP very basic

NLP Word Embeddings

• TFIDF
• Word Embeddings Part-1
• Word Embeddings Part-2

NLP RNN

• RNN Basic
• RNN Implementation

NLP Project:- Text to Speech

• Introduction
• Project Setup Text to Speech
• Project Demo

Speech To Text

• Introduction
• Project Setup Speech To Text
• Project Demo

Spell Corrector

• Introduction
• Project Setup Spell Corrector
• Project Demo

17. Big Data

Introduction to Distributed Systems Hadoop and MapReduce

• Big Data Engineering Introduction

Hive

• Apache Hive

NoSQL and Hbase

• Big Data HBase
• Hbase hands On

Spark

• Spark - Introduction
• Big Data Engineering using PySpark- RDDs
• Spark hands on - RDD
• Big Data Engineering using PySpark- Shared Vars , Coalesce Repartition
• Spark hands on - Dataframe

Spark ML

• Big Data Engineering using PySpark- MLLib
• Spark hands On - Spark ML Lib

Spark Streaming

• Big Data Engineering using PySpark- Streaming Part 1
• Big Data Engineering using PySpark- Streaming Part 2
• Spark hands On - Spark Streaming

Kafka

• Big Data Kafka
• Big Data Kafka Hands on

18. Power BI

Basic Charts in Power BI

• Basic Charts in Power BI Desktop
• Column Chart in Power BI
• Stacked Column Chart in Power BI
• Pie Chart in Power BI

Working with Maps

• Creating a Map in Power BI
• Filled Map
• Map with Pie Chart
• Formatting in Map

Tables and Matrix in Power BI

• Table and Matrix in Power BI
• Creating a Table in Power BI
• Formatting a Table

19. Tableau

Introduction to tableau

• Tableau Introduction
• Download and Install Tableau
• Tableau Vs Excel

20. SQL

• Database Architecture
• Introduction to SQL
• Constraints
• Joins
• Import Export
• Aggregate Functions
• Order by, Having & Limit Clause
• String Functions
• Datetime functions
• Nested Queries
• Views

21. Excel

• Introduction to Excel
• Pre-defined functions
• Datetime Funtions
• String functions
• Mathematical functions
• Lookup

22. Chatbot - Google Dialog Flow

• What is Chatbot?
• Why Chatbot?
• Types of Chatbot
• Use of Chatbot
• Examples of chatbot
• Dialogflow - Inline editor
• Create Intent and Entities
• Food order Intent

23. Interview Preparation

Interview Questions Discussion

• Interview Question Discussion
• Resume Discussion

24. Interview Preparation

Project Discussion

• Vision-Based Attendance System

25. Interview Preparation

Interview Questions Discussions

• Interview Question Discussion - 1

Interview Preparation - General Discussion

• Discussion Session - 1
• Discussion Session - 2

Educator

Instructor

Sudhanshu Kumar

Having 8+ 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..

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Krish Naik

Having 10+ years of experience in 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.

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Sunny Savita

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Sourangshu Pal

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Boktiar Ahmed Bappy

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Sunny Bhaveen Chandra

Sr. Data Scientist and lecturer at iNeuron.ai with working experience in computer vision, natural language processing and embedded systems. Hands-on experience leveraging machine learning, deep learning, transfer learning models to solve challenging business problems. Also, he has a vast interest in Robotics.

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