Data Science Training in Gurgaon - Job Oriented

• Statistical learning vs. Machine learning
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)

• Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
• Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
• Iteration and Model Evaluation

• Python Numpy (Data Manipulation)
• Python Pandas(Data Extraction & Cleansing)
• Python Matplot (Data Visualization)
• Python Scikit-Learn (Data Modelling)
• EDA – Quantitative Technique

• Data Exploration Techniques
• Sea-born | Matplotlib
• Correlation Analysis
• Data Wrangling
• Outliers Values in a DataSet
• Data Manipulation
• Missing & Categorical Data
• Splitting the Data into Training Set & Test Set
• Feature Scaling
• Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
• Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)

• Basic Data Structure & Data Types in Python language.
• Working with data frames and Data handling packages.
• Importing Data from various file sources like csv, txt, Excel, HDFS and other files types.
• Reading and Analysis of data and various operations for data analysis.
• Exporting files into different formats.
• Data Visualization and concept of tidy data.
• Handling Missing Information.

• Calls Data Capstone Project
• Finance Project : Perform EDA of stock prices. We will focus on BANK Stocks(JPMorgan, Bank Of America, Goldman Sachs, Morgan Stanley, Wells Fargo) & see how they progressed throughout the financial crisis all the way to early 2016.

• Fundamental of descriptive Statistics and Hypothesis testing (t-test, z-test).
• Probability Distribution and analysis of Variance.
• Correlation and Regression.
• Linear Modeling.
• Advance Analytics.
• Poisson and logistic Regression

• Feature Selection
• Principal Component Analysis(PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA
• Feature Reduction

• Simple Linear Regression
• Multiple Linear Regression
• Perceptron Algorithm
• Regularization
• Recursive Partitioning (Decision Trees)
• Ensemble Models (Random Forest , Bagging & Boosting (ADA, GBM)
• Ensemble Learning Methods
• Working of Ada Boost
• AdaBoost Algorithm & Flowchart
• Gradient Boosting
• XGBoost
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Evaluating Regression Models Performance

• Logistic Regression
• K-Nearest Neighbours(K-NN)
• Support Vector Machine(SVM)
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification
• Evaluating Classification Models Performance

• K-Means Clustering
• Challenges of Unsupervised Learning and beyond K-Means
• Hierarchical Clustering

• Purpose of Recommender Systems
• Collaborative Filtering

• Market Basket Analysis
• Collaborative Filtering
• Content-Based Recommendation Engine
• Popularity Based Recommendation Engine
• Anomaly Detection and Time Series Analysis

• Upper Confidence Bound (UCB)
• Thompson Sampling

• Spacy Basics
• Tokenization
• Stemming
• Lemmatization
• Stop-Words
• Vocabulary-and-Matching
• NLP-Basics Assessment
• TF-IDF

• Understanding Word Vectors
• Training the Word2Vec Model
• Exploring Pre-trained Models

• POS-Basics
• Visualizing POS
• NER-Named-Entity-Recognition
• Visualizing NER
• Sentence Segmentation

Course -1: TensorFlow for Deep Learning with MLOps

Course -2: Keras for Deep Learning with MLOps

Course -3: Deep learning Computer Vision - CNN, OpenCV, YOLO, SSD & GANs

• Environmental Setup using YAML
• Using Conda Packages

• Introduction to Neural Network
• Neural Network Activation Functions
  • RELU
  • TANH
  • Signoid
  • Hyperbolic
• Cost Functions
• Gradient Descent Back propagation
• Tensorflow Playground (playground.tensorflow.org)
• Manually Creating Neural Network
• Manually creation of Neural Network – Operations
• Manually creation of Placeholders & variables
• Manually creation of Session
• Manual Neural Network – Classification point of View

• Introduction to tensor flow
• Tensorflow – graphs
• Tensorflow – Variables & Placeholders
• Neural Network using Tensorflow
• Regression using Tensorflow
• Classification using Tensorflow
• Saving & Restoring models using Tensorflow

• Introduction to CNN
• Manual Creation of CNN
• CNN Implementation using Tensorflow
• Cnn Project Solution using Tensorflow

• Introduction to RNN
• Manual creation of RNN
• Vanishing Gradients
• LSTM & RGRU
• Introduction to RNN with Tensorflow API
• RNN with Tensorflow
• RNN Plotting
• Time Series App using RNN
• Word2Vec using RNN

• Deep Nets with TensorFlow Abstraction API
• Deep Nets with TensorFlow Abstraction API – Keras
• Deep Nets with TensorFlow Abstraction API – Layers
• Tensorboard

• Autoencoder Basics
• Dimensionality reduction with Linear AutoEncoder
• Linear Autoencoder PCA
• Stacked Autoencoder

• Introduction to Reinforcement Learning with OpenAI gym
• Introduction to OpenAI gym
• OpenAI Gym setup
• OpenAI gym Environment Basics
• OpenAI gym observations
• OpenAI gym Actions
• Simple Neural Network game
• Policy gradient theory Implementation & core base

• Introduction to GANs
• GAN Implementation
• Face Aging GAN

• ML Deployment Introduction
• ML Pipeline – Research Environment
• ML System Architecture
• Building a reproducible Machine Learning Pipeline
• Creating a ML Pipeline Application
• Serving the models via REST API
• Continuous Integration & Deployment Pipelines
• Deploying to IAAS (AWS ECS)
• Machine learning Projects
• Differential Testing
• Deploying to PAAS (Heroku) without containers
• Running Apps with Containers (Docker)
• Packaging of a Deep Learning Model (Deep Learning Model with Big Data)

• ANN Introduction
• The Neuron
• The Activation Function
• How do Neural Networks work?
• How do Neural Network Learn?
• Gradient Descent
• Stochastic Gradient Descent
• Backpropagation
• Building an ANN

• Evaluating the ANN
• Improving the ANN
• Tuning the ANN

• Convolutional Networks Introduction
• What are Convolutional Neural Networks (CNN)?
• Convolution Operation
• Various Layers in CNN
• Relu Layer
• Pooling Layer
• Flatening Layer
• Fully Connected Layer in CNN
• Softmax & Cross-Entropy
• Building a CNN
• Evaluating, Improving and Tuning the CNN

• RNN Introduction
• Idea behind Recurrent Neural Networks
• Vanishing Gradient Problem
• LSTM's (Long Short Term Memory)
• LSTM Variations
• Building an RNN
• Evaluating, Improving, and Tuning the RNN

• SOM's Introduction
• How do Self Organizing Maps work?
• Live SOM Example
• Reading an Advanced SOM
• Building a SOM
• Mega Case Study

• Introduction to Boltzmann Machines
• Energy-Based Modesl (EBM)
• Restricted Boltzmann Machines(RBM's)
• Contrastive Divergence
• Deep Belief Networks (DBN)
• Deep Boltzmann Machines (DBM)
• Building a Boltzmann Machine

• AutoEncoders Introduction
• Note on Baises
• Training an Auto Encoder
• Overcomplete Hidden Layers
• Sparse Autoencoders
• Denoising Autoencoders
• Contractive Autoencoders
• Stacked Autoencoders
• Deep Autoencoders
• Building an Autoencoder

• ML Deployment Introduction
• ML Pipeline – Research Environment
• ML System Architecture
• Building a reproducible Machine Learning Pipeline
• Creating a ML Pipeline Application
• Serving the models via REST API
• Continuous Integration & Deployment Pipelines
• Deploying to IAAS (AWS ECS)
• Machine learning Projects
• Differential Testing
• Deploying to PAAS (Heroku) without containers
• Running Apps with Containers (Docker)
• Packaging of a Deep Learning Model (Deep Learning Model with Big Data)

• Introduction to Computer Vision & Deep Learning
• What is Computer Vision?
• Intro to OpenCV, OpenVINO & their Limitations

• Setting Up Deep Learning Virtual Machines
• Manual Setup on Ubuntu Virtual Machine

• Handwriting Recognition, Simple Object Classification OpenCV Demo
• Experiment with a Handwriting Classifier
• Experiment with a Image Classifier
• OpenCV Demo Live Sketch with Webcam

• Setup OpenCV
• Storing Images on Computers
• Getting Started with OpenCV
• Grayscaling - Converting Color Images to Shades of Gray
• Understanding Color Spaces - Thw Many Ways Color Images Are Stored Digitally
• Histogram Representation of Images - Visualizing the Component Of Images
• Creating Images & Drawing on Images - Make Squares , Circles, Polygons & Add Text
• Transformations, Affine and Non-Affinne
• Image Translations- Moving Images Up, Down, Left And Right
• Rotations - How to Spin Our Image Around and Do Horizontal Flipping
• Scaling, Resizing and Iterpolations - Understanding How Re-sizing Affects Quality
• Image Pyramids - Another way of Resizing
• Arithmetic Operations - Brightening & Darkening Images
• Bitwise Operations - How Image Masking Works
• Blurring - The Many ways We can Blur images
• Sharpening - Reverse Your Images Blurs
• Thresholding (Binarization) - Making Certain Images Areas Black or White
• Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines
• Edge Detection using Image Gradients & Canny Edge Detection
• Perspective & Affine Transforms - Take An Off Angle Shot & MakeIt Look Top Down

• Segmentation & Contours - Extract Defined Shaped in our images
• Sorting Counters - Sort Those Shapes By Size
• Approximate Contours & Finding Their Convex Hull - Clean Up Messy Counters
• Matching Contour Shapes - Match Shapes in Images Even when distorted

• Line Detection - Detect Straight Lines Eg : The Lines On A Sudoku Game
• Circle Detection
• Blob Detection - Detect The Centre Of Flowers

• Object Detection Overview

• Feature Description Theory - How we digitally Represent Objects
• Finding CORNERS - Why Corners In Images are important to Object Detection
• Histogram Of Oriented Gradients - Another Novel Way Of Representing Images
• HAAR Cascade Classifiers - Learn How Classifiers Work And Why They are amazing
• Face & Eye Detection - Detect Human Faces & Eyes in Any Image

• Neural Network Introduction
• Forward Propagation
• Activation Functions
• LOSS Functions
• Backpropagation & Gradient Descent
• Backpropagation & Learning Rates
• Regularization, Overfitting, Generalization
• Epochs, Iterations & Batch Sizes
• Measuring Performance & the Confusion Matrix

CNNs in Detail

• CNN Introduction
• Convolution & Images Features
• Depth, Stride & Padding
• ReLU
• Pooling
• Fully Connected Layer
• Training CNNs
• Designing our own CNN

• Build Handwriting Recognition CNN
• Plotting LOSS & Accuracy Charts
• Displaying Model Visually
• Building Simple Image Classifier CIFAR10

• Introduction to Visualizing What CNNs 'see' & Filter Visualizations
• Saliency Maps & Class Activation Maps
• Filter Visualizations
• Heat Map Visualizations of Class Activations

• Data Augmentation Introduction
• Train Cats Vs Dogs Classifier
• Boosting Accuracy with Data Augmentation
• Types of Data Augmentation

• Data Augmentation Introduction
• Train Cats Vs Dogs Classifier
• Boosting Accuracy with Data Augmentation
• Types of Data Augmentation

• Introduction to the types of Optimizers, Learning Rates & Callbacks
• Types Optimizers & Adaptive Learning Rates Methods
• KERAS Callbacks & Checkpoint, Early Stopping & Adjust Learning Rates that PI
• Build a Fruit Classifier

• Intro to Building Lenet, AlexNet in KERAS & Understand Batch Normalization
• Build LeNet & test on MNIST
• Build AlexNet & test on CIFAR10
• Batch Normalization
• Build a Clothing & Apparel Classifier (Fashion MNIST)

• Introduction to ImageNet
• ImageNet - Experimenting with pre-trained Models in KERAS (VGG16,ResNet50, Mobi)
• Understanding VGG16 & VGG19
• Understanding ResNet50
• Understanding InceptionV3

• Transfer Learning & Fine Tuning
• Build a Monkey Breed Classifier with MobileNet using Transfer Learning
• Build a Flower Classifier with VGG16 using Transfer Learning

• LittleVGG Introduction
• Simpsons Character Recognition using LittleVGG

• Advanced Activation Functions
• Dying ReLU Problem & Introduction to Leaky ReLU , ELU & PReLUs
• Advanced Initializations

• Deep Surveillance Introduction
• Build an Emotion, Facial Expression Detector
• Build Emotion/Age/Gender Recognition in our Deep Surveillance Monitor

• Overview of Image Segmentation & Medical Imaging in U-Net
• What’s is Segmentation? And Applications in Medical Imaging
• U-Net: Image Segmentation with CNNs
• Intersection over Union (IoU) Metric
• Finding the Nuclei in Divergent Images

• Object Detection Introduction - Sliding Windows with HOGs
• R-CNN, Fast R-CNN, FasterR-CNN, and Mask R-CNN
• Single Shot Detectors (SSDs)
• YOLO to YOLOv3

• TFOD API Install & Setup
• Experiment with a ResNet SSD on images, webcam, and videos
• How to train a TFOD model

• Setting up & Install YOLO DarNet & DarkFlow
• Experiment with YOLO on still images, webcam, and videos
• Build our own YOLO Object Detector - Detecting London Underground Signs

• DeepDream - How AI-Generated Art All Started
• Neural Style Transfer

• GAN Overview
• Mathematics of GAN
• Mathematics of GAN
• Face Aging GAN

• Basic Face Recognition using LittleVGG CNN
• Face Matching with VGGFace
• Face Recognition using WebCAM & Identifying Friends TV Show Characters in Video

• CV Introduction
• Alternative Frameworks: PyTorch, MXNet, Caffe, Theano & OpenVNO
• Popular APIs Google, Microsoft, Clarifai, Amazon Rekognition & others
• Popular Computer Vision Conferences & Finding Datasets
• Building Deep Learning Machines & Cloud GPUs

• Training our model
• Extracting A Credit Card from the Background
• Use Model to identify the Digits & Display it onto our Credit Card

• Why use Cloud GPUs & How to Setup a PaperSpace Gradient Notebook
• Train a AlexNet on PaperSpace

• Install & Run Flask
• Running our Computer Vision Web App on Flask Locally
• Running our Computer Vision API
• Setting up AWS EC2 instances & Installing KERAS, Tensorflow, OpenCV & Flask
• Changing our EC2 Security Group
• Running our CV WebApp on EC2
• Running our CV API on EC2

Deep Science training will build your expertise in both basic & advanced level concepts like writing python scripts, sequence & file operations in python, Machine Learning, Data Analytics, Web application development & widely used packages like NumPy, Matplot, Scikit, Pandas & many more. The training will build your skills in real time scenarios related to analytics such as Prediction Analysis, Linear Regression, Image Processing, Audio & Video Analytics, Text Data Processing, IOT and various Machine Learning & Deep Learning Algorithms. 

After the completion of Data Science Course, you will be able to:

• Develop and Implement various Machine Learning Algorithms in daily practices & Live Environment.
• Build Real time Machine Learning and Deep Learning Applications
• Implement Data Analytics models on various Data Sets
• Data Mining across various file formats using Machine Learning and Deep Learning models
• Building Recommendation systems and Classifiers
• Perform various type of Analysis (Prediction & Regression)
• Implement plotting & graphs using various Machine Learning and Deep Learning Libraries (Tensor Flow & Keras)
• Implement Data Analytics models (CNN & RNN) on various Data Sets
• Building Image & Video Classifiers, Speech Analytics using Deep Learning models
• Perform various type of Analysis (Time Series, Image, Video, Audio, Face Detection & Recognition)
• Perform Big Data Analytics using Deep Learning & other frameworks
• Building different Neural networks using TensorFlow, Keras, PyTorch & other Deep Learning  Libraries.

We at Gyansetu understand that teaching any course is not difficult but to make someone job-ready is the essential task. That's why we have prepared capstone projects which will drive your learning through real-time industry scenarios and help you clearing interviews.

All the advanced level topics will be covered at Gyansetu in a classroom/online Instructor-led mode with recordings.

No prerequisites. This course is for beginners.

Gyansetu is providing complimentary placement service to all students. Gyansetu Placement Team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training.

• Our placement team will add Data Science skills & projects to your CV and update your profile on Job search engines like Naukri, Indeed, Monster, etc. This will increase your profile visibility in top recruiter search and ultimately increase interview calls by 5x.
• Our faculty offers extended support to students by clearing doubts faced during the interview and preparing them for the upcoming interviews.
• Gyansetu’s Students are currently working in Companies like Sapient, Capgemini, TCS, Sopra, HCL, Birlasoft, Wipro, Accenture, Zomato, Ola Cabs, Oyo Rooms, etc.

• Gyansetu trainers are well known in Industry; who are highly qualified and currently working in top MNCs.
• We provide interaction with faculty before the course starts.
• Our experts help students in learning Technology from basics, even if you are not good at basic programming skills, don’t worry! We will help you.
• Faculties will help you in preparing project reports & presentations.
• Students will be provided Mentoring sessions by Experts.