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Robotics with AI and Data Science
Robotics with AI and Data Science
(8 Months)
Roboitics with Reinforcement Learning and appropriate hardware simulations. Course Includes use cases such as Computer Vision, Chatbot, Speech Recognition, Hand written text recognition, Spam Detection, Optical character recognition etc.
What is Robotics with AI and Data Science
Technologies
Advanced Python Programming (2 Months)
SciKit Learn (2 Months)
TensorFlow and Keras (1 Month)
Cloud Computing (2 weeks)
Syllabus
 Html, CSS3, Web Design, Bootstrap, Project Web Design, Canvas
 JavaScript and Advanced JavaScript (1 month Dedicated with all standard algorithms)
 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
 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, Multilinear Regression, Classification
 Navebays classification, Decision Tree, Random Forest, KNN, Clustering, KMeans,
 Training data sets, overfitting, underfitting, crossvalidation, 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 RSquared, 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, kfold 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, OneHot encode, Extracting features from text, bagofwords, 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, TFIDF weights, TFIDF 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, zscore 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
 Multiclass classification, Sentimental Analysis, Medical data analysis, Multilabel 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
 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 reshaping, Transpose
 Geometric interpretation of tensor operations, Deep learning algorithm, engine of neural networks
 Derivatives, SGD, minibatch, 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, restructure for iterations, Project: Fashion MINIST data set classification
 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
Advanced Python programming and Web Design (2 months)
Python and Object Oriented Programming
Machine Learning (2 Months)
Deep Learning (1 Month)
Projects:
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:
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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Rush before all seats are reserved for current batch.
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If you Have Any Questions Call Us / Whats app On 917034256363
Detail Syllabus:
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 Robotics with AI and Data Science training Kochi
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Institute Hours:
 Monday to Saturday:
9:30 PM to 5:30 PM  Sunday :
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