Himanshu B.

Himanshu B.

DelhiIndia
Job Success

Machine Learning & Data Engineer | Full Stack Developer

Seasoned Machine Learning and Data Engineering Consultant. (2+ years dedicated enterprise experience) 🤵Completing my Executive MBA in Data to lead enterprise teams. Transitioned from Full-Stack Web Development Consultant (5 years). 🤖 Deep theoretical and engineering experience with Machine Learning covering classical Machine Learning, Deep Learning, Supervised, Unsupervised & Reinforcement learning spanning across Natural Language Processing (NLP), Statistical Analysis, Computer Vision (CV), Robotics, Image Processing, Pattern Recognition and Virtual Reality (VR). Cloud Providers I have worked with: ✅Amazon Web Services ✅Microsoft Azure ✅Google Cloud Platform ✅Databricks Examples of algorithms that I have implemented include: ➝ Regression ✅ OLS ✅ Linear Regression ✅ Logistic Regression ✅ Ridge Regression ✅ Multi-Output Regression ✅ LASSO ✅ Elastic Net ➝ Instance Based ✅ K Nearest Neighbours ✅ Self Organizing Map ✅ Support Vector Machine ➝ Tree Based ✅ CART ✅ CHAID ✅ Decision Tree ➝ Bayesian ✅ Naïve Bayes ✅ Gaussian Naïve Bayes ✅ Bayesian Knowledge Tracing ➝ Clustering ✅ K Means ✅ Hierarchical Clustering ✅ Expectation Maximization ➝ Association Rules ✅ Apriori ➝ Graph ✅ Social Network Analysis ✅ Beam Search ✅ A* ✅ Depth-first ✅ Breadth-first ✅ Best-first ➝ Neural Networks ✅ Perceptron ✅ MLP ✅ Hopfield Network ✅ Radial Basis Function ➝ Deep Learning ✅ Convolutional Neural Network (CNN) ✅ Recurrent Neural Network (RNN) ✅ Long Short-Term Memory Network (LSTM) ✅ Auto-Encoder ✅ Generative Adversarial Network (GAN) ➝ Dimensionality Reduction ✅ Principal Component Analysis (PCA) ✅ Linear Discriminant Analysis (LDA) ✅ t-Distributed Stochastic Neighbour Embedding (t-SNE) ➝ Ensemble ✅ Bootstrap Aggregation ✅ AdaBoost ✅ Blending ✅ Stacking ✅ Gradient Boosting Machines ✅ Random Forest ✅ Voting ✅ XGBoost ➝ Reinforcement Learning ✅ Q Learning ✅ Deep Q Networks And more… I am an expert with the following Machine Learning frameworks and languages: → Frameworks ✅ TensorFlow ✅ Keras ✅ PyTorch ✅ Caffe ✅ Scikit-Learn → Languages ✅ Python ✅ R ✅ MATLAB ✅ C++ →Tools ✔Looker Studio ✔Microsoft Excel ✔Tableau ✔Microsoft Power BI ✔Google BigQuery ✔Docker ✔Kubernetes ✔Hadoop
Feb 23, 2021 - Oct 20, 2021

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Oct 16, 2020 - Oct 21, 2020

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Himanshu B.

Himanshu B.

DelhiIndia
Job Success
10
Total Jobs
434
Total Hours

Machine Learning & Data Engineer | Full Stack Developer

Specializes in
Seasoned Machine Learning and Data Engineering Consultant. (2+ years dedicated enterprise experience) 🤵Completing my Executive MBA in Data to lead enterprise teams. Transitioned from Full-Stack Web Development Consultant (5 years). 🤖 Deep theoretical and engineering experience with Machine Learning covering classical Machine Learning, Deep Learning, Supervised, Unsupervised & Reinforcement learning spanning across Natural Language Processing (NLP), Statistical Analysis, Computer Vision (CV), Robotics, Image Processing, Pattern Recognition and Virtual Reality (VR). Cloud Providers I have worked with: ✅Amazon Web Services ✅Microsoft Azure ✅Google Cloud Platform ✅Databricks Examples of algorithms that I have implemented include: ➝ Regression ✅ OLS ✅ Linear Regression ✅ Logistic Regression ✅ Ridge Regression ✅ Multi-Output Regression ✅ LASSO ✅ Elastic Net ➝ Instance Based ✅ K Nearest Neighbours ✅ Self Organizing Map ✅ Support Vector Machine ➝ Tree Based ✅ CART ✅ CHAID ✅ Decision Tree ➝ Bayesian ✅ Naïve Bayes ✅ Gaussian Naïve Bayes ✅ Bayesian Knowledge Tracing ➝ Clustering ✅ K Means ✅ Hierarchical Clustering ✅ Expectation Maximization ➝ Association Rules ✅ Apriori ➝ Graph ✅ Social Network Analysis ✅ Beam Search ✅ A* ✅ Depth-first ✅ Breadth-first ✅ Best-first ➝ Neural Networks ✅ Perceptron ✅ MLP ✅ Hopfield Network ✅ Radial Basis Function ➝ Deep Learning ✅ Convolutional Neural Network (CNN) ✅ Recurrent Neural Network (RNN) ✅ Long Short-Term Memory Network (LSTM) ✅ Auto-Encoder ✅ Generative Adversarial Network (GAN) ➝ Dimensionality Reduction ✅ Principal Component Analysis (PCA) ✅ Linear Discriminant Analysis (LDA) ✅ t-Distributed Stochastic Neighbour Embedding (t-SNE) ➝ Ensemble ✅ Bootstrap Aggregation ✅ AdaBoost ✅ Blending ✅ Stacking ✅ Gradient Boosting Machines ✅ Random Forest ✅ Voting ✅ XGBoost ➝ Reinforcement Learning ✅ Q Learning ✅ Deep Q Networks And more… I am an expert with the following Machine Learning frameworks and languages: → Frameworks ✅ TensorFlow ✅ Keras ✅ PyTorch ✅ Caffe ✅ Scikit-Learn → Languages ✅ Python ✅ R ✅ MATLAB ✅ C++ →Tools ✔Looker Studio ✔Microsoft Excel ✔Tableau ✔Microsoft Power BI ✔Google BigQuery ✔Docker ✔Kubernetes ✔Hadoop
Feb 23, 2021 - Oct 20, 2021

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Oct 16, 2020 - Oct 21, 2020

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More than 30 hrs/week