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Cyber Security with AI, Data Science and Blockchain

Cybersecurity with Artificial intelligence training center in Kochi, Kerala

Cyber Security with AI, Data Science and Blockchain

(8 Months)

Cyber Security analysis through anomaly detection and enhance the application development practices through Blockchain. Course Includes use cases such as Computer Vision, Chatbot, Speech Recognition, Hand written text recognition, Spam Detection, Optical character recognition etc.

What is Cyber Security

Data Science is a core software development skill where Deep Learning is the heart. Data Analytics is the pre-processing activities of Data Science. The preprocessed data is generally used for Machine Learning Modelling. The time taken to run Data Science tasks are critical, When that concerned for huge amount of data to be processed to generate some significant information for decision making. Thus generally Data Science works with Big Data Analytics as well. These skills are generally needed together to make any important usecase completion. Deep Learning is a kind of automated Machine Learning. That means the preprocessing activities are not planned as a predecessor step. However the sampling, featurisation and vectorization are inherent process like automated machine learning in Deep Learning. Cyber Security has two parts in it. Detecting threats and anomalies using AI techniques and fix the issues adopting Blockchain.

Technologies

Advanced Python Programming (2 Months)
Complete Python programming with Advanced features like Django programming, Visualization using Seaborn and matplotlib graphs, Data re-shaping with Numpy and Pandas.
SciKit Learn (2 Months)
Machine Learning Libarary that supports, Regression, Logistic Regression, Classification, Clusterig, Decision Tree, Random Forest, Statistical algorithms and probability distributions.
TensorFlow and Keras (1 Month)
The number one choice of programmers to implement the Deep Learning Use cases. Direct acyclic Graphs are used to store the relationships. Python based Deep Learning extension over Tensorflow. Keras is widely used for Neural network topology planning, training and execution. CNN, RNN, LSTM and other advanced artificial nueral network use cases can be implemented using Keras Easily.
Cloud Computing (2 weeks)
AWS hosting, Regiions and Zones, Availablity Group, Load Balancing, Docker, Kubernetes, ESB, S3 Bucket, AMI Security configuration, RDS, MongoDB etc.

Syllabus

Expertzlab Data Science course in Kochi contains all the industry required contents and all of them will be implemented in the class with appropriate project use cases. Students will have immense experience in writing code and thus this Data Science training program is a easy gateway to an IT Job as Software Developer.

    Advanced Python programming and Web Design (2 months)
  • Html, CSS3, Web Design, Bootstrap, Project Web Design, Canvas
  • JavaScript and Advanced JavaScript (1 month Dedicated with all standard algorithms)
  • Python and Object Oriented Programming
  • conditional and control statements, Nested conditionals and loops
  • Recursion, importing and creating libraries, Packages, Functions
  • Built in functions, Variables and assignment and scopes, stack frames
  • creating a module and install it, Encapsulation, Generalisation, returning values
  • Composition of functions, Iteration, String manipulations, Searching and Matching
  • Enumeration, Collections Module, Iterators, Creating Iterators, using yield
  • Generators, Benefits of Generators, Chaining Generators, Decorators, Sequence Unpacking
  • Python, Object Oriented Principles, Solid Principles, Project
  • Classes, Interfaces, Abstract classes, packages, Destructive types
  • Dictionary, List, Set, Tuples, Tree, Dqueue, Heap, OrderedDict, List operations
  • Sorting, Slicing, Map and Reduce, Split, Delimiter, Objects, Variable sharing objects
  • Multi lists, Functional programming, Global variables, Mutal global variables, PIP, Virtual environments
  • Numpy, Pandas, Images, Videos data formats, Matrix Representations

  • Machine Learning (2 Months)
  • Statistics for data science, Response variables, Mean, Variance, Covariance, Beta, Alpha, Distributions
  • Normal Distribution, Link function, Logistic function, Logit function, Binomial, Poisoon, Chi Test, Anova, Use cases
  • SKlearn, Regression, Linear Regression, Multi-linear Regression, Classification
  • Navebays classification, Decision Tree, Random Forest, KNN, Clustering, K-Means,
  • Training data sets, over-fitting, under-fitting, cross-validation, Regularities
  • Variance, Bias, Performance measures, Bias Variance Tradeoff
  • Accuracy, Precesion, Recall, Sensitivity, Estimator, Visualization of training results
  • linear least squares, cost or loss function, Residuals, Residual sum of squares
  • least squares, Evalulating Model, calculate R-Squared, Regression Vector, Evolving Model
  • Finding Beta Vector, Calculating coefficients, Finding performance, Polynomial Regression
  • Compare Polynomial and Linear Regression, Quadratic and Cubic difference, regression curve
  • Nth Degree Polinomial, Regularization, Ridge Regression, Effect of Ridge regression
  • Apply Ridge regression, Lasso Regression, Elastic Net Regression, train/test split
  • Fitting evaluation technique, Validation, k-fold cross validation, Leave one out cross validation
  • Gradient Descent, Derivative of loss function, Learning rate, when to use GD
  • Stochastic Gradient Descent, Scaling features, Feature Extraction and Preprocessing
  • Label encoder, One-Hot encode, Extracting features from text, bag-of-words, Corpus, NLTK, Text processing, WordVect
  • Feature Vector, Tokenization, Document Similarity, Euclidian Distance, Semantic equivalent of documents
  • Sparse Vectors, Curse of Dimensionality, Hughes effect, Effect of adding dimensionality
  • Stop word filterning, Stemming and lemmatisation, Determiningl Lemma, TF-IDF weights, TF-IDF featurization
  • Logarithmically scalled frequencies, Augmented frequencies, Normalization, Inverse Document Frequency
  • hash trick, HashVectorizer, Computer Vision, Face detection, Extracting features from pixel densities
  • OCR, Hanwritten letter recognition, Feature vector of an image, Image featurization, Deep Learning from images
  • Extracting points of objects, Point of interestes, SIFT, SURF, mohotas, Data Standardization, Normalization
  • Unit scaling, z-score scaling, Standard scaler, Domination Feature, Logistic Regression
  • Binary classification, Confusion Matrix, Visualiz confusion matrix, Reading confusion matrix, Accuracy
  • Precesion and Recall, F1 measure, ROC AUC, Plot AUC, Hperparameters, grid search, tuning model
  • Multi-class classification, Sentimental Analysis, Medical data analysis, Multi-label classification performance
  • Jaccard Similarity, Hamming Loss, Non Linear classification and Regression, agglomerative and divisive clustering
  • Building Trees, Similarity Impact, Purity of Decesion Trees, Binary classification problem use case
  • Measure of Purity, Gini Impurity, Classification error, Training decesion trees, Entropy, Gain Ratio and over fitting
  • Pruning, Post Pruning, Titanic Disastor Analytics Use case, Data Exploration, Modeling, Decision Tree classifier
  • Render DT Pic, Ensemble, Random guessing, Ada Boost, Tree ensembling, Random input and combinations
  • Bagging, Boosting, Dimensionality Reduction, Extremely Randomised Trees, Overfitting RF, Stacking and Blending
  • Stacked encoder, Eager Learners, Lazy Learners, Classification Distortions, Local optima, elbow method, Evaluating clusters
  • silhouette coefficient, Image quantization, Dimensionality Reduction PCA, Effect of dimensionality reduction
  • Eigenvalues, Linear transformations, DR preprocess, project of DR, DR illustration, coordinate transformation
  • Managing information loss, coponent split, ScreePlot, face recognition, Singular value Decomposition, SVD works
  • Mathematical form of SVD, SVD DR, Reconstruction of noisy image, Reconstruction graph, Recommendation systems
  • Content based filtering, Cosine similarity, recommendation function, collaborative filtering itembased and model based
  • Estimating missing values, Estimate error, SVM, classification boundary, Goal of SVM, Maximum margin
  • Support Vectors, mathematical analysis, margin, optimisation function, Soft margin, constraints and margin
  • conditions for finding margin, Deduce from equations, Hyper plane, Kernel Trick, Mapping to Higher Dimensional plane
  • Kernel calculation, applying higher dimensional space, Kernel functions, Sequential Minimal optimisation, classifying characters use case
  • Recognise handwritten letters, SVM Regression, SVM classes

  • Deep Learning (1 Month)
  • Perceptron, Perceptron connections, Activation functions, Heavy sided, Sigmoid, logit
  • Perceptron learning algorithm, rate and epoch, Binary classification with Perceptron
  • Document classification with Perceptron, Limitations of Perceptron, Linearly seperable concept
  • ANN: Artificial Neural networks, Neural Net, Neural Decision boundaries, ANN for XOR
  • Feedforward and feedback ANN, Multilayer Perceptron, Hidden layer, Network Diagram, MSE cost function
  • Minimising cost function, Backpropagation, Training FF with BP, Forward Propagation steps, Error calculation
  • Updating weights, Classifying handwritten digits, Keras, Features of Keras, Keras backend
  • Keras Neural Networks, Layers, Deep learning trends, probabilistic modeling, Logistic regression using keras
  • Kernel methods, Decision boundary, Kernel function, Learning Deep, Keras workflow, sequntial layers
  • Compilation, Fit, Using Layers, optimiser, Preprocessing, Preparing the labels, Overfitting
  • Tensors, 1D, 2D and 3D tensors, Image processing, Time Series and uses, Video data, tensor operations
  • Elementwise operations, Broad casting, Tensor dot, dot operation, Tensor re-shaping, Transpose
  • Geometric interpretation of tensor operations, Deep learning algorithm, engine of neural networks
  • Derivatives, SGD, mini-batch, Issues with SGD, batch SGD, loss surface, GD intuition, Optimizers
  • Momentum, Local and Global Minimum, Naive implementation, Chaining derivatives, Topology selection
  • Choosing Loss function, Projects: Classifying reviews, Classifying news wires, Predicting house prices
  • MLPs, RNN, CNN, ReLU, Regulariztion in Deep Networks, Dropout, Softmax activation, tanh, Sigmoid
  • Choices of Lossfunction, Plotting the model, Conv2D, Convolution, Convolution process, Pooling
  • MaxPoolin2D, Pooling and Compression, Performance evaluation, RNN digit classification, SimpleRNN, LSTM Model
  • Sentiment Analysis using LSTM, GRU, Stacked RNN, Keras Functional API, Flattend and Dropout
  • Tensorflow, Dimensions, Immutability, Variables, Computational Graph, Sessions, Correct the Initialization errors
  • Global Variable Initializser, Assign values to Variables, Guidelines and Tensor types, Feed values, run graph
  • Eval and run, Evaluating multiple nodes, Dependencies between nodes, Solutions to multiple runs
  • clossing session, NN Architecture, using activation function in Tensorflow, Swish Activation
  • Project: US Census Bureau data sets, re-structure for iterations, Project: Fashion MINIST data set classification

  • Projects:
  • Speech to Text and Text to Speech conversions: MFCC, Mapping speech to Matrix, Creatingl Spectrograms
  • Speech Recognition Classifier, Resample audio, Preprocessing audio wave, convert to deep learning model
  • Building Keras models, Diagnostic Plot, Predictions, Text to Speech, loading waves, CNN models for wave predictions
  • Chatbot from Scratch: Developing chatbots, Retrieval based chatbots, Generative based chatbots, LSTM for chatbot
  • Context, Design of chatbots, Steps for building chatbots, Preprocessing data and loading data, Lemmatize, training and testing
  • Predict Response, Random Intents, Executing Chatbots, Chatbots using CNN, Genism, use of Genism, Wordvec, Intent classifier

How are we different from others?

  • By Developers for Developers (100% Software Development 100% Hands on training)
  • Placed more than 300 only in MEAN Stack in last three years
  • You will do at least 20 projects as part of the course in house with the trainer
  • Experienced Faculties, More than 15+ years experienced 3 members in house
  • Focused on coding skill development and by 6 months you become an Industry Expert
  • Dedicated lab sessions with numerous (20+) Projects/Use cases
  • Monthly Installments, Card Payment (Credit Card Accepted)
  • Loan Facility for eligible candidates
  • Amazing Discounts for eligible candidates, come with your BPL Card, get Free Training
  • Nactet Certificate and Government of India Discounts for SC/ST students (Showing Proof or certificates)
  • Interviews until you get placed in an IT company thus Assured placements.
  • Data Analytics and visualisation based course thus more towards latest trends in the industry
  • Free cloud computing with Heroku and AWS cloud providers
  • Provision to upgrade to JavaScript based Machine Learning and Deep Learning (AI)
  • Provision to upgrade to Blockchain programming with Deep Learning chains Technology

Our Other Programs/Courses:

Big Data Implementation and Analytics, Machine Learning, Artificial Intelligence, MEAN Stack Course, Python training with Data Science (AI), JAVA/ Advanced Java & JEE 7, SDET (Software Development Engineer in Test), Android Technology, Cloud Computing, RPA (Robotic Process Automation), Full Stack Developer, Blockchain with DeepLearning (Deep Learning chains) are all bench marked with global curriculums.
The Courses are comprehensive, trained by the best of faculty with many years of experience and with a huge focus on use-case driven training. The focus on use-case driven training, where the emphasis is on practical’s rather than theoretical itself is game-changer.

Easy Placements and assured Job!

Our students are getting easily placed with preferred corporates, a nd they have come back to us for more students from us which shows that our training methodology is working great. Our students are on a path to dream jobs in the IT sector. The fact that most of these technologies are getting increasingly adopted, and there is huge dearth of skills makes it imperative that the students shift gears and start adopting new technology platforms as part of the skillset.

The disruption with Indian IT has begun, and the next phase is for people who quickly adapt to the Gen 4.0 technologies. And irrespective of whether you are a corporate looking for our students or students who want to jumpstart their career, Expertzlab is the best partner for you.

Why Study With Us?
Our trainers are certified professionals working in the industry over 20+ years of expertize

Special Techniques
Our courses are categorized in to activity and project labs to get a feel of real project experiance.

 

Qualified Staff
Our Qualified trainers from industry give you best professional Knowledge.

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