HIEE provides the best AI/ML Course Training for the passionate graduates. After completion of successful Training program the candidate will get the placement along with certification
HIEE Training Process :
On Line
90 days Duration
Daily 2 to 3 hours
Industry Oriented Curriculum
Real Time Projects
Placements
Our Job Oriented Courses for graduates gives you an in-depth knowledge on every single concept and are good enough to start their career as a AI Engineer
CRISP-DM methodology, ETL
SAI Application and use cases
Programming for ML ( python)
Data sourcing, Wrangling and Munging
Python : Numpy, Pandas
Sql basics for data science
Qualitative and quantitative data
Measure of central tendency
Covariance, correlation chi square techniques
Data Visualization ( practical in python Matplotlib, pyplot )
Contingency table
Probability distribution
Sampling distribution
Confidence Intervals, Hypothesis testing
Bayesian Methods
Practical (Statistics in Python )
Linear and Multivariate regression
Residual Analysis ( R square goodness of fit)
Identifying Significant features, Feature reduction using AIC
Regularization methods ( lasso, Ridge )
Generalized Linear models
Ml techniques
Variance and Bias
Validation techniques
Dimensionality reduction
Feature engineering, Unbalanced Data Treatment
Naive Bayes Classifier
Logistic regression
K-nearest Neighbours
Support vector machine
Association rule mining ( apriori algorithm)
Python Practical - Sk learn module
Decision Tree
Bagging and boosting ( Random forest, GBM, XG boost)
Python Practical - Sk learn module
Distance methods
Clustering methods
K-medoids, K- modes
Python Practical - Sk learn module
ANN application areas
Gradient Descend, Perceptron, MLP, FFN, Back propagation
Regularization - Dropout and batch normalization
ANN for structured Data
LSTM ( long short term memory)
Python practical ( Tensor flow module )
Convolutional Neural Networks ( CNN’s)
Recurrent Neural Network (ANN’s)
Auto Encoders
Object detection, Image classification, Face detection
Overview of reinforcement learning: the agent environment framework, successes of reinforcement learning
Bandit problems and online learning
Markov decision processes
Monte Carlo learning
Case studies: successful examples of RL systems ( using python - open AI gym module
DESIGNATIONS FOR SKILLED ENGINEERS
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