Datascience Training

Home

Courses

Datascience Training

Datascience Training in Hyderabad

As newer technologies like Artificial Intelligence (AI), Augmented Reality and so on are all set to flourish soon, data administration and management will be a hurdle. Knowledge in Data Science can do wonders as it involves various kinds of algorithms, tools and unique principles to recognize underlying pattern within raw data. Data Science professionals are therefore in great demand. It is expected that around 5 million Data Scientists will be required by 2019. With years of experience in technical training, Peopleclick Techno Solutions provides full-fledged Data Science course in hyderabad with live projects and case studies. We have a team of supremely talented Data Science trainers who are working professionals in MNCs.

Be Expert with Data Science

Being the leading Data Science training institute in hyderabad, we provide rigorous Data Science training in hyderabad that makes you a Data Science expert. Data Science course with both R and Python are offered. In addition to interactive classroom sessions, exclusive lab sessions and workshops are also there to sharpen the practical skills.

Evaluate Yourself As a Data Scientist

Here, it's not simply mugging up facts and information, but a solid platform to rate yourself as a Data Scientist before entering into the industry. Daily assignments help you enhance your practical way of thinking. However, the big deal is our multiple live projects through which you can encounter real-world problems and work on it like a Data Scientist.

Start Your Career in Data Science

We offer complete job-oriented Data Science training in hyderabad so as to ensure you a stable career in Data Science. Once you finish all our Data Science classes in hyderabad, you can take the help of our placement cell for placement training programs. This will include resume preparation guidance to mock interviews by experts. 100% placement is assured.

Data Analytics - Part I

Programming Foundation in R

Data object: Vectors, Matrices, Data Frames, and Lists
Common functions
R - Studio Environment and package management
Local data input/output
Introduction to R data visualization
Data sorting and merging
String manipulation o Dates and times
Connecting to an external database

Data Science with R - Data Analytics

Data Manipulation with 'dplyr'
Join
Subset
Advanced manipulations with dplyr
Data Visualization with 'ggplot2'
Histogram
Point graphics
Columnar graphics o Line charts
Pie charts o Box plots
Scatter plots
Visualizing multivariate data

Matrix - based visualizations

Maps
Introduction to Shiny
Shiny introduction
Design the User -- interface
Control widgets
Build reactive output
Use data table in Shiny Apps
Use R scripts, data and packages
UI and server for the App
Make Shiny perform quickly
Matrix -- based visualizations
Use reactive expressions
Share and deploy Shiny apps

Statistics and Mathematical Essentials for Data Science

Measure of Central Tendency,
Mean : Arithmetic Mean, Geometric Mean, Harmonic Mean
Mode: Median
Dispersion techniques:
Range, Inter Quartile Range
Varience, Standard Deviation
Correlational Analysis
Introduction to Machine Learning.
Aproaches of Machine Learning.
- * supervised learning
- * unsupervised learning
- * semi supervised learning
- * reignforcement learning
Predictive models & Introduction.
- * Regression problems [Introduction]
- * Classification Problems [Introduction]
Regression Models [Deeper Study]
Linear Regression
Non Linear Regression
Accuracy Measurement
choosing Models
Lasso Regression
Ridge Regression
Gradient Descent Algorithm
Stochastic Gradient descent Algorithm.
More Models in Remaining Modules.
[Above all will be implemented in R, Python (with numpy)]

Machine Learning (Duration: 30 Hours)

Constructing a Classifier [Classification Models]

Building a simple classifier
Building a logistic regression classifier
Building a Naive Bayes classifier
Building a Decision Tree
Building a Random Forest
Splitting the dataset for training and testing
Evaluating the accuracy using cross-validation
Visualizing the confusion matrix
Extracting the performance report
Evaluating cars based on their characteristics (task)
Extracting validation curves
Extracting learning curves
Estimating the income bracket (task)

Machine Learning Using SVM Models

What is the goal of the Support Vector Machine (SVM)?
How to compute the margin?
How to find the optimal hyperplane?
Unconstrained minimization
Convex functions
Duality and Lagrange multipliers
Support Vector Machines (SVM) Overview and Demo using R
Svm implementation By Python
Building a linear classifier using Support Vector Machines(SVMs)
Building a nonlinear classifier using SVMs
Tackling class imbalance
Extracting confidence measurements
Finding optimal hyperparameters
Building an event predictor
Estimating traffic

Working With Time Series and Sequential Data

Transforming data into the time series format
Slicing time series data
Operating on time series data
Extracting statistics from time series data
Building Hidden Markov Models for sequential data
Building Conditional Random Fields for sequential text data
Analyzing stock market data using Hidden Markov Models

Machine Learning with Clustering Modeles[unsupervised Learning]

what is clustering ? types of clusters .
Clustering data using the k-means algorithm
Compressing an image using vector quantization
Building a Mean Shift clustering model
Grouping data using agglomerative clustering
Evaluating the performance of clustering algorithms
Automatically estimating the number of clusters using DBSCAN �� algorithm
Finding patterns in stock market data
Building a customer segmentation model
Python Examples
R Examples

Text MingingWith Natural Language Processing with NLTK(Python)

Introduction to NLP and NLTK
Preprocessing data using tokenization
Stemming text data
Converting text to its base form using lemmatization
Dividing text using chunking
Building a bag-of-words model
Building a text classifier
Identifying the gender
Analyzing the sentiment of a sentence
Identifying patterns in text using topic modeling

Speech Recognition

Reading and plotting audio data
Transforming audio signals into the frequency domain
Generating audio signals with custom parameters
Synthesizing music
Extracting frequency domain features
Building Hidden Markov Models
Building a speech recognizer

Building Recommendation Engines

What is recommendation Engine
Types of Recommendation Engines
Pattern Based Recommendations Using Apriori and FPGrowth.
Collaborative Filtering
IBCF (Item Based)
UBCF (User Based)
Graph Based Recommendations.
Building function compositions for data processing
Building machine learning pipelines
Finding the nearest neighbors
Constructing a k-nearest neighbors classifier
Constructing a k-nearest neighbors regressor
Computing the Euclidean distance score
Computing the Pearson correlation score
Finding similar users in the dataset
Generating movie recommendations

Free WorkShop On

Image Content Analysis
Biometric Face Recognition.

Free BigData Stuff

Hadoop
�Spark with scala and Python.
Data Science part-II

Deep Learning (Duration: 30 Hours)

Deep Learning

What is Deep Learning?
Introduction to Artificial Neural Networks
The Neuron
The Activation Function
How do Neural Networks work?
How do Neural Networks learn?
Gradient Descent
Stochastic Gradient Descent
Back propagation
Business Problem Description
Building an ANN - Step 1, Step 2, Step 3, Step 4 & Step 5

Convolutional neural networks introduction

step1. Convolution Operation
step1(b)- ReLU Layer
Different Activation functions and when to use them.
step2 -- Pooling
step3 -- Flattening
step4 -- Full Connection
Summary
Softmax& cross entropy
Building a CNN - step1, step2, step3, step4, step5, step7, step8, step9, step10
Case study: what the pet is ?

Introduction to Recurrent Neural Networks

The idea behind RNN
The vanishing Gradient Problem
LSTMs
LSTM variations
Building a RNN - step1, step2, step3, step4, step5, step6, step7, step8, step9, step11& step12
Summary & Next steps
case Study: Google stock price prediction.
Evaluating the RNN
Improving and Tuning the RNN

Self Organizing Maps

Introduction to SOMs
. How do SOM works?
why revisit K-Means ?
K-means Clustering(Refresher)
How do SOMs Learn?(part 1)
How do SOMs Learn?(part 2)
Live SOM example
Reading an Advanced SOM
Extra: K-means clustering(part 2)
Extra: K-means Clustering(part 3)
Building a SOM - step 1, step 2, step 3, step 4
Mega Case Study - step1 , step2, step3, step4

Introduction to Boltzmann Machines

Energy Based Models (EBM)
Restricted Boltzmann Machine
Contrastive Divergence
Deep Belief Networks
Deep Boltzmann Machines
Building a Boltzmann Machine - Introduction
Building a Boltzmann Machine - step 1, step 2,step 3, step 4,step 5,step 6,step 7,step 8,step 9,step 10,step 11,step 12, step 13 &step 14

Introduction to AutoEncoders

A note on biases
Training an Auto Encoder
overcomplete hidden layers
Sparse Autoencoders
Denoising Autoencoders
Contractive Autoencoders
Stacked Autoencoders
Deep AutoEncoders.
Building an AutoEncoder - step1, step2, step3, step4, step5, step6, step7, step8, step9, step10 & step11

Unsupervised Deep Learning

Introduction to Unsupervised Deep Learning.
Introduction to Reducing Dimensionality
What does PCA do?
PCA derivation
MNIST visualization , finding the optimal number of principle components
PCA objective function
t-sne(t-distributed stochastic Neighbor Embedding)
t-SNE Theory-SNE on the Donut,t-SNE on XOR,t-SNE on MNIST
Autoencoders
Denoising Autoencoders
Stacked Autoencoders
writing the autoencoder class in code (Theano)
Testing our Autoencoder(Theano)
Writing the deep neural network class in code(Theano)
Autoencoder in Code( Tensorflow)
Testing greedy layer-wise autoencoder training vs. pure backpropagation
Cross Entropy vs. KL Divergence
Deep AutoEncoder Visualization Description
Deep Autoencoder Visualization in Code
Restricted Boltzmann Machine Theory
Deriving Conditional Probabilities from Joint Probability
Contrastive Divergence for RBM training
RBM in Code(Theano) with Greedy Layer-Wise training on MNIST
RBM in Code(Tensorflow)
The Vanishing Gradient Problem Description
The Vanishing Gradient Problem Demo in Code

Natural Language Processing

what is nlp and its importance.
what we can do with nlp
Introduction to spam engines .
Introduction to sentiment analyzers.
word tokenization
sentence tokenization
parts of speech tagging
lemmatization
lemmatization
removing stop words
building word clouds
feature extraction techniques and importance
Word Existence feature
Word proportion feature.
TFIDF feature.
NLP vs Machine Learning
How to get the dataset
Natural Language Processing in Python
Homework Challenge

Artificial Intelligence Course

Tools Covered

R, Python, Jupyter, Spark, H2O, AzureML, Keras, IBM Watson

Introduction To AI And DL

Introduction to Artificial Intelligence and Deep Learning
Applications of AI in various industries
Introduction to the installation of Anaconda
Creating of Environment with stable Python version
Introduction to TensorFlow, Keras, OpenCV, Caffe, Theano
Installation of required libraries

Introduction To Mathematical Foundations

Introduction to Data Optimization
Calculus and Derivatives Primer
Finding Maxima and Minima using Derivatives in Data Optimization
Data Optimization in Minimizing errors in Linear Regression
Gradient Descent Optimization
Linear Algebra Primer
Probability Primer

Introduction To The Perceptron Algorithm, ANN / MLP

Understand the history of Neural Networks
Learn about Perceptron algorithm
Understand about Backpropagation Algorithm to update weight
Drawbacks of Perceptron Algorithm
Introduction to Artificial Neural Networks or Multilayer Perceptron
Manual calculation of updating weights of final layer and hidden layers in MLP
Understanding of various Activation Functions
R code and Python code to understand about practical model building using MNIST dataset

Various Hyper Parameters In Neural Networks

Understand about challenges in Gradient
Introduction to various Error, Cost, Loss functions
ME, MAD, MSE, RMSE, MPE, MAPE, Entropy, Cross Entropy
Vanishing / Exploding Gradient
Learning Rate (Eta), Decay Parameter, Iteration, Epoch
Variants of Gradient Descent

Techniques to overcome challenges of Mini-batch SGD

Momentum
Nesterov Momentum
Adagrad (Adaptive Gradient Learning)
Adadelta (Adaptive Delta)
RMSProp (Root Mean Squared Propagated)
Adam (Adaptive Moment Estimation)

Various Outcomes-Based MLP And Regularization Techniques

Binary classification problem using MLP on IMDB dataset
Multi-class classification problem using MLP on Reuters dataset
Regression problem using MLP on Boston Housing dataset
Types of Machine Learning outcomes - Self-supervised, Reinforcement Learning, etc.
Handling imbalanced datasets and avoiding overfitting and underfitting
Simple hold-out validation
Weight initialization techniques

Various Outcomes-Based MLP And Regularization Techniques

Binary classification problem using MLP on IMDB dataset
Multi-class classification problem using MLP on Reuters dataset
Regression problem using MLP on Boston Housing dataset
Types of Machine Learning outcomes - Self-supervised, Reinforcement Learning, etc.
Handling imbalanced datasets and avoiding overfitting and underfitting
Simple hold-out validation
Weight initialization techniques

Image Processing / Computer Vision Using OpenCV

Understanding about Computer Vision related applications
Various challenges in handling Images and Videos
Images to Pixel using Gray Scale and Color images
Color Spaces - RGB, YUV, HSV
Image Transformations - Affine, Projective, Image Warping
Image Operations - Point, Local, Global
Image Translation, Rotation, Scaling
Image Filtering - Linear Filtering, Non-Linear Filtering, Sharpening Filters
Smoothing / Blurring Filters - Mean / Average Filters, Gaussian Filters
Embossing, Erosion, Dilation
Convolution vs Cross-correlation
Boundary Effects, Padding - Zero, Wrap, Clamp, Mirror
Template Matching and Orientation of image
Edge Detection Filters - Sobel, Laplacian, LoG (Laplacian of Gaussian)
Bilateral Filters
Canny Edge Detector, Non-maximum Suppression, Hysteresis Thresholding
Image Sampling - Sub-sampling, Down-sampling
Aliasing, Nyquist rate, Image pyramid
Image Up-sampling, Interpolation - Linear, Bilinear, Cubic
Detecting Face and eyes in the Video
Identifying the interest points, key points
Identifying corner points using Harris and Shi-Tomasi Corner Detector
Interest point detector algorithms
Scale-invariant feature transform (SIFT)
Speeded-up robust features (SURF)
Features from accelerated segment test (FAST)
Binary robust independent elementary features (BRIEF)
Oriented FAST and Rotated Brief (ORB)
Reducing the size of images using Seam Carving
Contour Analysis, Shape Matching and Image segmentation
Object Tracking, Object Recognition

Convolution Neural Network (CNN) And Variants

Understand about various Image related applications
Understanding about Convolution Layer and Max-Pooling
Practical application when we have small data
Building the Convolution Network
Pre-processing the data and Performing Data Augmentation
Using pre-trained ConvNet models rather than building from scratch
Feature Extraction with and without Data Augmentation
How to Visualize the outputs of the various Hidden Layers
How to Visualize the activation layer outputs and heatmaps

Recurrent Neural Network (RNN), Long Short Term Memory (LSTM) And Gated Recurrent Unit (GRU)

Understand about textual data
Pre-processing data using words and characters
Perform word embeddings by incorporating the embedding layer
How to use pre-trained word embeddings
Introduction to RNNs - Recurrent layers
Understanding LSTM and GRU networks and associated layers
Hands-on use case using RNN, LSTM, and GRU
Recurrent dropout, Stacking recurrent layers, Bidirectional recurrent layers
Solving forecasting problem using RNN
Processing sequential data using ConvNets rather than RNN (1D CNN)
Building models by combining CNN and RNN

Generative Deep Learning, Generative Adversarial Networks (GANs), Autoencoders

Text generation using LSTM and generative recurrent networks
Understanding about DeepDream algorithm
Image generation using variational autoencoders
GANs theory and practical models
The Generator, the Discriminator, the Adversarial network
Deep Convolution Generative Adversarial networks
Producing audio using GAN
Unsupervised learning using Autoencoder

AI Game Playing, Reinforcement Learning, Advanced Techniques

Q-learning
Exploration and Exploitation
Experience Replay
Model Ensembling
Final project using a live Kaggle competition

What We Offer

Real Time live project training
Mock Interviews will be conducted on a one-to-one basis after the course duration
Job Assistance
Job Support After Getting JOB
FAQ's
Soft &Hard copy Material, Resume Preparation

+91-9110789667

Are You Looking For -

Captcha : 40077

Top Trending Courses

Do please get in touch with us with any queries.

Have a question for us? Just fill in your details in the form below, and we'll get back to you!