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Data Science Training in Gurgaon - Job Oriented

Data Science, Machine Learning, Python are termed as the bread & butter of the future analytics industry. Data Science course will build your skills in python, data visualization, predictive analytics, machine learning. These skills will help you to start a successful career in data science. Building a strong understanding of the topics, and job-focused project will help you demonstrate your abilities to the interviewer, thus achieving the goals of your career.

Instructor from Microsoft  |  Instructor-Led Training  |  Free Course Repeat  |  Placement Assistance  |  Job Focused Projects  |  Interview Preparation Sessions

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Curriculum

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    The Machine Learning Introduction chapter of the data science course provides an overview of the basic concepts and principles of machine learning. It covers the different types of learning algorithms, phases of predictive modeling, and evaluation techniques. The chapter also introduces the concept of overfitting and underfitting and highlights the importance of performance metrics in evaluating model performance.

    • 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 Data Analysis Libraries (EDA) teach how to use Python libraries like Pandas, Numpy, and Matplotlib for exploratory data analysis. It covers the basics of data manipulation, cleaning, and visualization and teaches how to perform statistical analysis on data using these libraries. The course also covers advanced data analysis techniques like time series analysis and machine learning.

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

    This teaches how to prepare data for machine learning models. It covers techniques such as data wrangling, handling missing values, feature scaling, etc. The course also discusses how to perform data normalization and standardization and handle outliers in data.

    • 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)

    Here, you will learn how to work with various data sources in Python. We will cover popular data formats such as CSV, Excel, and JSON and show you how to connect to databases like MySQL and SQLite. You'll learn how to import and export data, preprocess and clean it, and prepare it for analysis.

    • 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.

    A Machine Learning Capstone Project is a final project demonstrating a student's machine learning proficiency. The project usually involves solving a real-world problem using machine learning techniques. Examples of projects include predicting customer behavior, analyzing sentiment in social media, detecting fraud, and diagnosing diseases. The project is an opportunity to apply machine learning concepts to real-world scenarios and showcase the skills acquired during the course.

    • 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.

    Machine learning is a subfield of artificial intelligence that uses statistical inference to develop algorithms that improve performance on a specific task. This involves analyzing large datasets and identifying patterns that can be used to make predictions or decisions. Statistical analysis is a key machine learning component, providing a framework for modeling and testing hypotheses.

    • 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

    Dimensionality reduction is a critical step in machine learning that helps reduce the number of features in a dataset while retaining as much relevant information as possible. This process is commonly known as feature engineering. Techniques like principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are widely used in dimensionality reduction

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

    Supervised learning is a machine learning technique that involves training a model using a labeled dataset to predict continuous output values. Regression is a type of supervised learning that aims to predict a numerical value based on input variables. Linear regression, polynomial regression, and multiple regression are some of the popular regression algorithms.

    • 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

    Classification is another type of supervised learning that involves predicting a categorical variable or class label based on input variables. Popular algorithms for classification include logistic regression, decision trees, random forests, and support vector machines (SVMs).

    • 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

    Unsupervised learning is a machine learning technique that involves training a model using an unlabeled dataset to identify patterns and relationships in the data. Clustering is a type of unsupervised learning that involves grouping similar data points together into clusters. K-means, hierarchical clustering, and DBSCAN are popular clustering algorithms

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

    Recommender systems are an important application of machine learning that helps suggest relevant products, services, or content to users based on their past behavior and preferences. Collaborative filtering, content-based filtering, and hybrid filtering are some of the common techniques used in recommender systems.

    • Purpose of Recommender Systems
    • Collaborative Filtering

    To design a recommendation engine, start by defining the problem statement and the type of recommendation system required. Then, collect and preprocess data, select appropriate features, and choose a suitable machine learning algorithm. Finally, evaluate the model's performance and fine-tune the parameters as required. Deploying the model and monitoring its performance are also crucial steps in the design process

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

    Reinforcement Learning is a type of machine learning where an agent learns to take optimal actions in an environment to maximize a reward signal. The agent learns from experience through trial and error. Reinforcement Learning has applications in gaming, robotics, and finance.

    • Upper Confidence Bound (UCB)
    • Thompson Sampling

    Text Mining and NLP are subfields of machine learning that deal with analyzing and understanding human language. These techniques are used for sentiment analysis, topic modeling, and text classification, among other things. In addition, NLP has applications in chatbots, customer service, and virtual assistants

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

    Word2Vec is a machine learning technique that learns word embeddings. Word embeddings are dense vector representations of words that capture semantic relationships between them. Word2Vec is used for natural language processing tasks such as language translation, sentiment analysis, and speech recognition

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

    POS tagging is a technique used in NLP to assign grammatical tags to words in a text corpus. These tags indicate the part of speech of each word, such as noun, verb, or adjective. POS tagging is used for text classification, information extraction, and machine translation.

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

    Deep Learning is a subset of machine learning that uses artificial neural networks to learn from data. This specialization in Python covers the basics of deep learning, including building and training neural networks, image classification, and natural language processing.

    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

     

    TensorFlow is an open-source machine learning framework developed by Google. It is widely used for deep learning applications like image and speech recognition. MLOps (Machine Learning Operations) is a set of best practices for deploying and managing machine learning models. This course covers the installation and setup of TensorFlow along with MLOps best practices

    • Environmental Setup using YAML
    • Using Conda Packages

    Neural Networks are a class of machine-learning models inspired by the human brain. They consist of layers of artificial neurons that process input data and produce output. In regression, neural networks are used to predict continuous values. This course covers the manual creation of neural networks for regression problems, including architecture design and hyperparameter tuning.

    • 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

    TensorFlow is a popular open-source machine learning framework that was developed by Google. It provides various tools and libraries for building and deploying machine learning models. TensorFlow allows developers to create deep learning models using its flexible and efficient programming interface. It is widely used for developing applications in fields such as image recognition, speech recognition, natural language processing, and more.

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

    Convolutional Neural Networks (CNNs) are widely used in image and video recognition tasks. TensorFlow provides powerful tools for building CNN models, including convolutional layers, pooling layers, and more. These layers can be easily combined to create complex CNN architectures that can achieve state-of-the-art performance on image and video classification tasks.

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

    Recurrent Neural Networks (RNNs) are a type of neural network that can handle sequential data such as time series or natural language data. TensorFlow provides various tools for building RNN models, including recurrent layers, bidirectional layers, and more. These layers can create complex RNN architectures that can achieve state-of-the-art performance on various sequential data tasks.

    • 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

     

    TensorFlow provides additional tools and libraries for building and deploying machine learning models. These include TensorFlow Probability, TensorFlow Datasets, TensorFlow Federated, and TensorFlow Lite. These libraries can be used for probabilistic modeling, data preprocessing, distributed training, and deployment on mobile and embedded devices

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

    Autoencoders are a type of neural network that can be used for unsupervised learning tasks such as data compression and feature extraction. TensorFlow provides tools for building autoencoder models, including dense layers, convolutional layers, and more. These layers can be combined to create complex autoencoder architectures that can be used for a wide range of unsupervised learning tasks

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

    Reinforcement learning is machine learning that involves training agents to act in an environment to maximize a reward. OpenAI gym is a toolkit for developing and comparing reinforcement learning algorithms. It provides a variety of environments that can be used for training agents, including classic control problems, Atari games, and robotics tasks

    • 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

    Generative Adversarial Networks (GANs) are a type of neural network that can be used for generative modeling tasks such as image synthesis and text generation. GANs consist of two neural networks: a generator and a discriminator. The generator network is trained to generate realistic samples, while the discriminator network is trained to distinguish between real and fake samples. GANs can learn to generate highly realistic samples by training these networks adversarially

    • Introduction to GANs
    • GAN Implementation
    • Face Aging GAN
     
    Machine Learning Operations (MLOps) is a set of practices and tools for managing the entire lifecycle of machine learning models, from development to deployment and maintenance. MLOps includes data management, model training, model deployment, and monitoring. Some popular MLOps tools include Kubeflow, MLflow, and TFX. Proper MLOps practices can help ensure that machine learning models are deployed in a reliable, scalable, and maintainable manner???????
     
    • 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)

    Keras is an open-source deep learning library that simplifies the process of building artificial neural networks (ANNs). Keras can be integrated with MLOps tools for managing the entire lifecycle of machine learning models, from development to deployment and maintenance. In addition, Keras provides a high-level API that makes building complex deep learning models easy, including convolutional neural networks, recurrent neural networks, and more

    • 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, improving, and tuning artificial neural networks (ANNs) is essential to building effective deep-learning models. Various techniques, such as cross-validation, regularization, and hyperparameter tuning, can evaluate, improve, and fine-tune ANNs. As a result, these techniques can help improve deep learning models' performance, accuracy, and generalization.

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

    Convolutional Neural Networks (CNNs) are a type of neural network commonly used in computer vision tasks such as image classification and object detection. CNNs consist of multiple convolutional layers that extract features from input images, followed by pooling layers that reduce the spatial dimensions of the feature maps. As a result, CNNs can achieve state-of-the-art performance on a wide range of computer vision tasks.

    • 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

    Recurrent Neural Networks (RNNs) are a type of neural network that can handle sequential data such as time series or natural language data. RNNs are designed to capture the temporal dependencies in sequential data. They consist of recurrent layers that maintain a hidden state over time, allowing them to handle sequential data with variable-length input and output sequences

    • 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

    Self-Organizing Maps (SOMs) are a type of unsupervised learning algorithm that can be used for dimensionality reduction and data visualization tasks. SOMs use a grid of nodes to map high-dimensional input data onto a low-dimensional space. The grid nodes are organized so that similar input data points are mapped to nearby nodes while dissimilar input data points are mapped to distant nodes

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

    Boltzmann Machines (BMs) are a type of neural network that can be used for unsupervised learning tasks such as data compression and feature extraction. BMs consist of a network of binary nodes connected by weighted edges. These connections are adjusted based on the input data to learn a compressed representation of the input data

    • 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 are a type of neural network that can be used for unsupervised learning tasks such as data compression and feature extraction. Autoencoders consist of an encoder network that maps the input data to a compressed representation, followed by a decoder network that maps the compressed representation back to the input data. The network can learn an effective compression and feature extraction scheme by training the autoencoder to minimize the reconstruction error between the input data and the reconstructed data.

    • 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

    Deep Learning models can be used for both classification and regression tasks in machine learning. With MLOps tools, developers can build, train, and deploy these models at scale. Classification tasks predict a class label for input data, while regression tasks predict a continuous value. As a result, deep Learning models can achieve high accuracy and performance on various classification and regression tasks

    • 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)

    Deep Learning is particularly useful for computer vision tasks such as object detection, image classification, and facial recognition. Convolutional Neural Networks (CNNs) are commonly used in computer vision tasks, as they are designed to extract features from images. With deep learning, computer vision tasks can be automated and performed at scale, making it possible to analyze large amounts of visual data quickly and accurately

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

    Setting up a deep learning development environment involves installing the necessary software and hardware, including a powerful GPU, to run deep learning models. Popular deep learning frameworks like TensorFlow, Keras, and PyTorch require compatible hardware and software environments to run effectively. An optimized development environment can significantly improve the efficiency of deep learning workflows, allowing developers to build and deploy models more quickly.

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

    Deep Learning models can be used for tasks such as handwriting recognition and simple object classification using OpenCV, a popular computer vision library. In addition, deep Learning models such as Convolutional Neural Networks (CNNs) can be used to recognize image patterns, making it possible to perform complex tasks such as object classification and handwriting recognition

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

    OpenCV is a popular computer vision library with deep learning models for various tasks, such as live sketches, shape identification, and face detection. Deep Learning models can improve the accuracy and performance of these tasks, allowing for real-time analysis and detection of visual data. OpenCV3 tutorials provide step-by-step guidance on how to use this library with deep learning models

    • 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

    This mini-project involves building a live sketch app using OpenCV and deep learning techniques. The app takes a webcam feed and transforms it into a real-time pencil sketch. The deep learning model detects edges and lines in the image and applies a filter to produce the sketch effect. This mini-project is a great introduction to OpenCV and deep learning for beginners

    • 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

    This mini-project involves building a deep-learning model to identify different image shapes. The model is trained on a dataset of labeled shapes and uses techniques such as Convolutional Neural Networks (CNNs) to identify the different shapes. This mini-project is a good introduction to deep learning for image recognition tasks

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

    This mini-project involves building a deep-learning model that can count the number of circles and ellipses in an image. The model is trained on a labeled image dataset and uses blob detection and contour analysis techniques to identify the circles and ellipses. This mini-project is a good introduction to deep learning for image analysis tasks

    • Object Detection Overview

    This mini-project involves building a deep learning model to quickly find a specific pattern in an image, such as the character Waldo from the popular book series "Where's Waldo?". The model is trained on a dataset of labeled images and uses techniques such as feature detection and matching to quickly locate the pattern in the image. This mini-project is a good introduction to deep learning for object detection tasks.

    • 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

    This mini-project involves building a deep-learning model to detect cars and pedestrians in videos. The model is trained on a labeled video frame dataset and uses techniques such as Motion Detection and Haar Cascades to detect the objects of interest. This mini-project is a good introduction to deep learning for video analysis tasks

    Neural Networks are the foundation of deep learning, and this mini-project introduces their architecture and workings. The project involves building a simple neural network from scratch and training it on a small dataset to perform a classification task. This mini-project is a good introduction to deep learning for beginners

    • 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

    This mini-project involves building a Convolutional Neural Network (CNN) using KERAS to recognize handwritten digits from the MNIST dataset. The project includes pre-processing the data, building the CNN model, and training it on the dataset. This mini-project is a good introduction to deep learning using KERAS.

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

    This mini-project explores how CNNs 'see' images by visualizing the filters, heatmaps, and salience maps generated by the model. These visualizations help us understand how the CNN model makes its predictions and can help us improve its performance

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

    This mini-project involves building a Cats Vs Dogs classifier using data augmentation techniques. The project includes pre-processing the data, building the CNN model, and training it on the dataset using various data augmentation techniques to improve its accuracy.

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

    This project aims to provide a thorough understanding of different optimization techniques, learning rates, and callbacks. Additionally, the project introduces the concept of data augmentation, a technique used to artificially increase the size of training data. The project also explores the various data augmentation methods and their effectiveness in improving model accuracy. With this guide, learners can acquire the necessary skills and knowledge to build a fruit classifier using KERAS.

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

    This project is designed to help learners thoroughly understand the different types of optimizers, learning rates, and callbacks used in deep learning. By the end of the project, learners will have acquired the necessary skills to build a fruit classifier using KERAS. The guide also introduces the different types of optimizers and adaptive learning rate methods. It also explores KERAS callbacks, including checkpoint, early stopping, and adjusting learning rates, to improve model performance

    • 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

     

    Batch Normalization is a technique to improve the training of deep neural networks. Building LeNet and AlexNet models to classify clothes using deep learning is fascinating. Using Convolutional Neural Networks (CNN), we can achieve high accuracy. The project involves downloading and pre-processing the data set, building the CNN models, training and evaluating them, and finally using them for predictions.

    • 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)

    ImageNet is a large dataset of labeled images that have revolutionized the field of computer vision. In Keras, we can use pre-trained models like VGG16/19, InceptionV3, and ResNet50 to build advanced image classifiers. These models use convolutional layers to extract features from the images and then use fully connected layers to classify them. We can fine-tune these pre-trained models on our dataset for even better accuracy.

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

    Transfer learning is using a pre-trained model to solve a new task. This project uses transfer learning to build a flower and monkey breed classifier. First, we use a pre-trained model like VGG16 to extract features from the images. Then, we add a few more layers to the pre-trained model and fine-tune it on our own dataset. Finally, we evaluate the model on a test set and see how well it performs

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

    In this project, we will design our CNN architecture called LittleVGG to classify Simpsons characters. First, we pre-process the dataset and then build the CNN model using Keras. The LittleVGG model consists of convolutional, pooling, and fully connected layers. Finally, we train the model on the Simpsons dataset and evaluate its performance.

    • LittleVGG Introduction
    • Simpsons Character Recognition using LittleVGG

    Activation functions and weight initialization are critical components of deep neural networks. This project explores advanced activation functions like LeakyReLU and ELU and initialization techniques like He and Xavier. We implement these techniques in Keras and evaluate their performance on various datasets

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

    Deep surveillance involves using deep learning to analyze images or videos to recognize faces and their attributes. In this project, we build a facial emotion, age, and gender recognition system using deep learning. First, we use pre-trained models like VGG16 and InceptionV3 to extract features from the face images and then train a multi-task model to predict the attributes

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

    Image segmentation is dividing an image into multiple segments to analyze it more effectively. In this project, we use the U-Net model to perform image segmentation on medical images and find nuclei in them. We use the Kaggle Data Science Bowl 2018 dataset and pre-process it before building and training the U-Net model. Finally, we evaluate the model's performance on the test set.

    • 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 is a technique in computer vision that involves locating and classifying objects within images or videos. This project focuses on object detection principles, including using bounding boxes, anchor boxes, and non-maximum suppression. In addition, we explore different object detection models, such as Faster R-CNN, YOLO, and SSD, and evaluate their performance

    • 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

    TensorFlow Object Detection API is a powerful framework for object detection tasks. In this project, we use the API to build an object detection model to recognize and locate different objects in images or videos. We start by pre-processing the dataset, training the model using transfer learning, and evaluating its performance on a test set

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

    YOLO (You Only Look Once) is a real-time object detection system that is fast and accurate. In this project, we use YOLO and Darkflow to build a London Underground Sign Detector. We start by pre-processing the dataset, building and training the YOLO model, and evaluating its performance on test images

    • 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 and Neural Style Transfer are techniques in deep learning that generate unique and creative images. In this project, we use these techniques to create AI-generated art. We start by building and training a deep neural network using Keras and then use the trained model to apply DeepDream and Neural Style Transfer on images

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

    Generative Adversarial Networks (GANs) are deep learning models that generate new data from existing data. In this project, we use GANs to age faces to 60+ using our Age-cGAN. We start by pre-processing the dataset, building, and training the Age-cGAN model, and evaluating its performance on test images

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

     

    VGGFace is a pre-trained deep-learning model that can recognize faces. In this project, we use VGGFace to build a face recognition system. We start by pre-processing the dataset, building and fine-tuning the VGGFace model, and evaluating its performance on test images.

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

    Computer vision is an exciting field that is rapidly growing, and this project explores the latest trends and technologies in computer vision. We cover topics such as object detection, image segmentation, facial recognition, and generative models and their applications in various industries

    • 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

    We build a credit card number reader using deep learning in this project. We use pre-processing techniques to extract the credit card number from the image, build and train a deep neural network to recognize the numbers and evaluate the model's performance on test images

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

    Training deep learning models can be time-consuming, and cloud GPUs can significantly speed up the process. In this project, we use PaperSpace to train deep-learning models using cloud GPUs. We start by setting up an account, launching a GPU instance, and finally, building and training a deep learning model

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

    We create a computer vision API and web app using Flask and AWS in this project. We build and train a deep learning model to recognize images and then use Flask to create a RESTful API to interact with the model. Finally, we deploy the API to AWS and create a web app to showcase its capabilities

    • 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

     

Course Description

The Data Science course is designed to provide learners with hands-on experience in various data science tools and techniques. The course covers data analysis, machine learning, artificial intelligence, data visualization, and more. The course is taught

    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.

    Upon finishing the Data Science course training, participants are required to take an online examination facilitated by the academy. To obtain certification, they must score 60% or higher.

    The skills and knowledge you gain through working on projects, simulations, and case studies will set you apart from the competition, giving you a Course Certificate to differentiate yourself.

     

    • This module helps you take your first steps towards becoming a successful Data Scientist.
    • The faculties provide a detailed understanding of the modules provided in the sequence Python, SQL, & Power BI.
    • You will be introduced to the fundamentals of data science, algorithms, structures, python programming, statistical foundations, machine learning, and more.
    • The faculties provide you one-by-one online and offline doubt-clearing offers; for offline discussion, you can contact the team and enjoy the services.
    • The training empowers students to proficiently figure out tremendous information from various sources and infer significant bits of knowledge to go with more astute information-driven choices.
       

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Data Science Training in Gurgaon - Job Oriented Features

Frequently Asked Questions

    We have seen getting a relevant interview call is not a big challenge in your case. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. We help you prepare your CV by adding relevant projects and skills once 80% of the course is completed. Our placement team will update your profile on Job Portals, this increases relevant interview calls by 5x.

    Interview selection depends on your knowledge and learning. As per the past trend, initial 5 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    We have seen getting a technical interview call is a challenge at times. Most of the time you receive sales job calls/ backend job calls/ BPO job calls. No Worries!! Our Placement team will prepare your CV in such a way that you will have a good number of technical interview calls. We will provide you interview preparation sessions and make you job ready. Our 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 update your profile on Job Portals, this increases relevant interview call by 3x

    Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    We have seen getting a technical interview call is hardly possible. Gyansetu provides internship opportunities to the non-working students so they have some industry exposure before they appear in interviews. Internship experience adds a lot of value to your CV and our placement team will prepare your CV in such a way that you will have a good number of interview calls. We will provide you interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training and we will update your profile on Job Portals, this increases relevant interview call by 3x

    Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    Yes, a one-to-one faculty discussion and demo session will be provided before admission. We understand the importance of trust between you and the trainer. We will be happy if you clear all your queries before you start classes with us.

    We understand the importance of every session. Sessions recording will be shared with you and in case of any query, faculty will give you extra time to answer your queries.

    Yes, we understand that self-learning is most crucial and for the same we provide students with PPTs, PDFs, class recordings, lab sessions, etc, so that a student can get a good handle of these topics.

    We provide an option to retake the course within 3 months from the completion of your course, so that you get more time to learn the concepts and do the best in your interviews.

    We believe in the concept that having less students is the best way to pay attention to each student individually and for the same our batch size varies between 5-10 people.

    Yes, we have batches available on weekends. We understand many students are in jobs and it's difficult to take time for training on weekdays. Batch timings need to be checked with our counsellors.

    Yes, we have batches available on weekdays but in limited time slots. Since most of our trainers are working, so either the batches are available in morning hours or in the evening hours. You need to contact our counsellors to know more on this.

    Total duration of the course is 160 hours (80 Hours of live-instructor-led training and 80 hours of self-paced learning).

    You don’t need to pay anyone for software installation, our faculties will provide you all the required softwares and will assist you in the complete installation process.

    Our faculties will help you in resolving your queries during and after the course.

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