With Python, building Machine Learning models for any type of Data Science problems. I have full hands-on experience with the following: Principle Component Analysis (Dimensionality Reduction Technique) t-Distributed Stochastic Neighbor Embedding (t-SNE (Dimensionality Reduction Technique)) Logistic Regression (Classification) Linear Regression (Regression Technique) Naive Bayes (Classification) K-Nearest Neighbor (KNN (Classification/Regression)) Support Vector Machines (Classification/Regression) Decision Trees (Classification/Regression) Ensemble Models (Bagging/Boosting etc.) Random Forest (Classification/Regression) Neural Networks (Deep Learning) Multi-Layer Perceptrons (Deep Learning) Tensorflow and Keras (Deep Learning) Convolutional Neural Nets (Deep Learning) Long Short-Term Memory (LSTMs (Deep Learning)) Generative Adversarial Networks (GANs (Deep Learning)) Encoder-Decoder Models (Deep Learning) Attention Models (Deep Learning)