Languages Python, SQL, R, NoSQL(MangoDB) Databases Oracle 10g/11g, PostgreSQL, MySQL, Azure SQL, MS-Access, SSIS, SSRS Operating Systems Windows, Linux Machine Learning Algorithm’s Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machines, Gradient Boost Decision Trees, Naive Bayes, K-Means Clustering, Stacking Classifiers, Cascading Models, Hierarchical Clustering and Density, Based Clustering Machine Learning Techniques Principal Component Analysis, Truncated SVD, Data Standardization, L1 and L2 Regularization, Loss Minimization, Hyper Parameter Tuning, Performance Measurement of Models, Feature Engineering, Content Based and Collaborative Based Filtering, Matrix Factorization, Model Calibration and Validation, Productionizing and Deploying Models, A/B Testing, Point and Interval Estimation, Hypothesis Testing, Cross-Validations, Decision Surface Analysis, Retraining, Models periodically, t-distributed Stochastic Neighbour Embedding SQL Tools SQL Server Tools SQL Server Management Studio (SSMS), Erwin Data Modeler, SAS, Data Warehouse Tools, MS SQL Server Integration Services, Business Intelligence Tools SQL Server, Business Intelligence Development Studio Reporting Tools Deep Learning Artificial Neural Networks, Convolutional Neural Networks, Multi-Layer Perceptron’s, Recurrent Neural Networks, LSTM, Chain Rule, Choosing Activation Functions, Dropout, Optimization Algorithms, Vanishing and Exploding Gradient, Striding, Padding, Optimized Weight Initializations, Gradient Monitoring and Clipping, Batch Normalizations, Max Pooling BI & other Tools Tableau, Plotly, Power BI, Qlik View, Erwin, AWS, Azure,D3, Mule Soft, Shiny, Anaconda Datacamp Certifications Introduction to Python, Intermediate Python for Data Science, Introduction to R, Introduction to SQL, Joining Data in SQL