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Licenses & Certifications

* * Generative Adversarial Networks (GANs) Specialization * * Oct 2023

DeepLearning.AI [Credential URL]
- Provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach.
- Contains three courses:
(1) Build Basic Generative Adversarial Networks (GANs)
(2) Build Better Generative Adversarial Networks (GANs)
(3) Apply Generative Adversarial Networks (GANs)

(1) Build Basic Generative Adversarial Networks (GANs)Sep 2023

DeepLearning.AI [Credential URL]
- Learn about GANs and their applications.
- Understand the intuition behind the fundamental components of GANs.
- Explore and implement multiple GAN architectures.
- Build conditional GANs capable of generating examples from determined categories.

(2) Build Better Generative Adversarial Networks (GANs)Oct 2023

DeepLearning.AI [Credential URL]
- Assess the challenges of evaluating GANs and compare different generative models.
- Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs.
- Identify sources of bias and the ways to detect it in GANs.
- Learn and implement the techniques associated with the state-of-the-art StyleGANs.

(3) Apply Generative Adversarial Networks (GANs)Oct 2023

DeepLearning.AI [Credential URL]
- Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity.
- Leverage the image-to-image translation framework and identify applications to modalities beyond images.
- Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa).
- Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures.
- Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one.

* * Deep Learning Specialization * * Aug 2023

DeepLearning.AI
- Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications.
- Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow.
- Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data.
- Courses:
(1) Neural Networks and Deep Learning
(2) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
(3) Structuring Machine Learning Projects
(4) Convolutional Neural Networks

(1) Neural Networks and Deep LearningJul 2023

DeepLearning.AI [Credential URL]
- Study the foundational concept of neural networks and deep learning.
- Be familiar with the significant technological trends driving the rise of deep learning.
- Build, train, and apply fully connected deep neural networks.
- Implement efficient (vectorized) neural networks.
- Identify key parameters in a neural network’s architecture.

(2) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and OptimizationAug 2023

DeepLearning.AI [Credential URL]
- Open the deep learning black box to understand the processes that drive performance and generate good results systematically.
- Learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications.
- Be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking.
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
- Implement a neural network in TensorFlow.

(3) Structuring Machine Learning ProjectsAug 2023

DeepLearning.AI [Credential URL]
- Learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
- Be able to diagnose errors in a machine learning system.
- Prioritize strategies for reducing errors.
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance.
- Apply end-to-end learning, transfer learning, and multi-task learning.

(4) Convolutional Neural NetworksAug 2023

DeepLearning.AI [Credential URL]
- Understand how computer vision has evolved.
- Become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
- Build a convolutional neural network, including recent variations such as residual networks.
- Apply convolutional networks to visual detection and recognition tasks.
- Use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.

* * Machine Learning Specialization * * Aug 2023

Stanford University & DeepLearning.AI [Credential URL]
- Build ML models with NumPy & scikit-learn.
- Build & train supervised models for prediction & binary classification tasks (linear, logistic regression).
- Build & train a neural network with TensorFlow to perform multi-class classification.
- Build & use decision trees & tree ensemble methods.
- Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection.
- Build recommender systems with a collaborative filtering approach & a content-based deep learning method.
- Build a deep reinforcement learning model.
- Contains three courses:
(1) Supervised Machine Learning: Regression and Classification
(2) Advanced Learning Algorithms
(3) Unsupervised Learning, Recommenders, Reinforcement Learning

(1) Supervised Machine Learning: Regression and ClassificationJul 2023

Stanford University & DeepLearning.AI [Credential URL]
- Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
- Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.

(2) Advanced Learning AlgorithmsAug 2023

Stanford University & DeepLearning.AI [Credential URL]
- Build and train a neural network with TensorFlow to perform multi-class classification.
- Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
- Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

(3) Unsupervised Learning, Recommenders, Reinforcement LearningAug 2023

Stanford University & DeepLearning.AI [Credential URL]
- Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
- Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
- Build a deep reinforcement learning model.

* * IBM Data Science Professional Certificate * * Feb 2024

IBM [Credential URL]
- Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles.
- Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL.
- Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines.
- Contains 10 courses:
(1) What is Data Science?
(2) Tools for Data Science
(3) Data Science Methodology
(4) Python for Data Science, AI & Development
(5) Python Project for Data Science
(6) Databases and SQL for Data Science with Python
(7) Data Analysis with Python
(8) Data Visualization with Python
(9) Machine Learning with Python
(10) Applied Data Science Capstone

(1) What is Data Science? Nov 2023

IBM [Credential URL]
- Understand what data science is and what data scientists do, and learn about career paths in the field.

(2) Tools for Data ScienceNov 2023

IBM [Credential URL]
- Understand the Data Scientist’s tool kit, like Libraries & Packages, Data sets, Machine learning models, and Big Data tools.
- Know languages commonly used by data scientists like Python, R, and SQL.
- Familir with working tools such as Jupyter notebooks, RStudio, etc.
- Can create and manage source code for data science using Git repositories and GitHub.

(3) Data Science MethodologyDec 2023

IBM [Credential URL]
- Know about what a data science methodology is and why data scientists need a methodology.
- Know how to apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
- Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
- Determine appropriate data sources for data science analysis methodology.

(4) Python for Data Science, AI & DevelopmentDec 2023

IBM [Credential URL]
- Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.
- Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.
- Access and web scrape data using APIs and Python libraries, like Beautiful Soup.

(5) Python Project for Data ScienceDec 2023

IBM [Credential URL]
- Play the role of a Data Scientist / Data Analyst working on a real project.
- Apply Python fundamentals, Python data structures, and working with data in Python.
- Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter Notebook.

(6) Databases and SQL for Data Science with PythonDec 2023

IBM [Credential URL]
- Analyze data within a database using SQL and Python.
- Create a relational database and work with multiple tables using DDL commands.
- Construct basic to intermediate level SQL queries using DML commands.
- Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

(7) Data Analysis with PythonJan 2024

IBM [Credential URL]
- Data Preprocessing: cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data.
- Data Exploratory: apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy.
- Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines.
- Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making.

* * Python Programming Language & Algorithms * * Jun 2023

University of Michigan
- Courses:
(1) Python Data Structures
(2) Programming for Everybody (Getting Started with Python)

(1) Python Data StructuresJun 2023

University of Michigan [Credential URL]
- Explain the principles of data structures & how they are used.
- Create programs that are able to read and write data from files.
- Store data as key/value pairs using Python dictionaries.
- Accomplish multi-step tasks like sorting or looping using tuples.

(2) Programming for Everybody (Getting Started with Python)Jun 2023

University of Michigan [Credential URL]
- Describe the basics of the Python programming language.
- Use variables to store, retrieve and calculate information.
- Utilize core programming tools such as functions and loops.