Artificial Intelligence

Implementation of Collaborative Filtering with RBM Learn industry standards and best practices regarding AI & Deep Learning Explore various Neural Network architectures such as Autoencoders, Convolutional Neural Network and Recurrent Neural Network Thorough understanding of Deep Neural Networks

Implement various type of classification methods including SVM, Naive bayes, decision tree, and random forest Interpret Unsupervised learning and learn to use clustering algorithms Tuning of ML solutions, Bias-variance tradeoff, Minibatch, and Shuffling, Overfitting avoidance Basics of Neural Networks, Perceptron, MLP Data preprocessing techniques, Univariate and Multivariate analysis, Missing values and outlier treatment etc Implement linear and polynomial regression, understand Ridge and lasso Regression Appreciate the breadth & depth of ML applications and use cases in real-world scenarios. Import and wrangle data using Python libraries and divide them into training and test datasets

Interpret and Evaluate How AI & ML can help your organization. Explain What is Artificial Intelligence Explain the sub-sets of AI

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