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
- 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 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
- 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
- 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)
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
- Image Content Analysis
- Biometric Face Recognition.
- Hadoop
- Spark with scala and Python.
- Data Science part-II
Deep Learning (Duration: 30 Hours)
- 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
- 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 ?
- 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
- 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
- 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
- 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
- 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
- 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 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 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
- 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
- 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
- Momentum
- Nesterov Momentum
- Adagrad (Adaptive Gradient Learning)
- Adadelta (Adaptive Delta)
- RMSProp (Root Mean Squared Propagated)
- Adam (Adaptive Moment Estimation)
- 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
- 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
- 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
- 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
- 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
- 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
- Q-learning
- Exploration and Exploitation
- Experience Replay
- Model Ensembling
- Final project using a live Kaggle competition
- 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
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