CERTIFICATES

Skills: building resilient teams; fundamentals of resilient teams; forming, leading, and managing teams in volatile conditions; preparing teams for success; efficiently managing, motivating, and resolving conflicts; managing multicultural teams; determining next steps and maintaining team progress.

Hard Skills: Numpy; Pandas; Statistics; Data Acquisition and Preparation; Exploratory Data Analysis; Feature Engineering; Classification Models; Regression Models; Time Series; Unsupervised Learning; NLP. Soft Skills: Agile Methodology; Interpersonal Skills; Storytelling Techniques; Project and Change Management. Recognized as the 3rd best student in the course.

Skills: Numpy; OpenCV; Python; image manipulation; histograms; smoothing, blurring, thresholding, and morphological operations; object detection; image segmentation; deep learning for image classification; TensorFlow and Keras.

Skills: identifying positive and negative risks; risk management strategies; qualitative risk analysis; identifying contingencies and cost reserves; developing a quality plan; cause-and-effect tools; prioritization matrix, and team management plan.

Skills: project scope statement; control project scope; break down work and develop work packages; create a Work Breakdown Structure (WBS); critical path scheduling and perform cost and schedule analysis.

Skills: project evaluation using previous project data and lessons learned; identifying key deliverables based on business requirements while managing client expectations; stakeholder analysis and management; project organizational structure, and project charter.

Skills: Recurrent Neural Networks (RNNs) and variants - GRUs and LSTMs; applying RNNs to Character-Level Language Modeling; Natural Language Processing (NLP); HuggingFace; Transformers, Named Entity Recognition (NER), and Question Answering.

Skills: diagnosing errors in a machine learning system; prioritizing strategies to reduce errors; understanding complex machine learning setups and Transfer Learning.

Skills: building a convolutional neural network; residual networks; image detection and recognition; Neural Style Transfer; applying algorithms to 2D or 3D shapes.

Skills: best practices for training and testing; analyzing bias/variance; standard neural network techniques - initialization, L2 regularization, and dropout; hyperparameter tuning, batch normalization, and gradient checking; optimization - Gradient Descent, Momentum, RMSprop, Adam - and TensorFlow.

Skills: key technology trends; building, training, and applying fully connected deep neural networks.

Skills: Spark for Big Data analysis; Machine Learning algorithms; Numpy, Pandas, Matplotlib, and Seaborn for data analysis; Plotly for interactive visualizations; SciKit-Learn; K-Means Clustering; Logistic Regression; Linear Regression; Random Forest and Decision Trees; Natural Language Processing, Neural Networks, and Support Vector Machines.