Certifications & Achievements

IBM Quantum Challenge 2024 Achievement

Demonstrated proficiency in Qiskit 1.0, mastering its significant updates for utility-scale quantum computing; applied Qiskit Patterns to efficiently map, optimize, execute, and process quantum circuits; gained hands-on experience in implementing quantum experiments and leveraging Qiskit’s latest advancements for real-world quantum applications.

Qiskit Global Summer School 2024 – Quantum Excellence

Acquired in-depth knowledge of quantum computing through intensive hands-on labs, mastering quantum algorithms and large-scale quantum circuit design using Qiskit; developed expertise in applying physics, mathematics, and Python to quantum computation, gaining practical experience in executing and optimizing quantum circuits for real-world applications.

Basics of Quantum Information

Gained foundational knowledge in quantum information theory, covering mathematical representations for single and multi-qubit systems; explored quantum circuits as a framework for quantum computation; studied fundamental quantum entanglement phenomena, including quantum teleportation, superdense coding, and the CHSH game (CHSH inequality); developed a strong theoretical understanding of quantum mechanics as applied to information processing, preparing for advanced topics in quantum algorithms and error correction.

Quantum Business Foundations

Developed a deep understanding of the limitations of classical computing and the business problems best suited for quantum solutions; analyzed the paradigm shift introduced by quantum computing and its strategic impact on business models, technology, and operations; gained insights into how organizations can leverage quantum advancements for competitive advantage.

Introduction to PennyLane

Gained foundational proficiency in PennyLane, mastering the basics of quantum machine learning (QML) and hybrid quantum-classical computing; explored quantum circuits, variational algorithms, and quantum differentiable programming, laying the groundwork for further advancements in quantum AI.

Quantum Machine Learning

Developed expertise in building basic and advanced quantum machine learning (QML) models, implementing classical and quantum algorithms for machine learning tasks using Python and Qiskit; explored key challenges and limitations in QML, gaining insights into the future prospects of quantum-enhanced AI and its real-world applications.

Introduction to Quantum Computing with Qiskit

Gained hands-on experience with Qiskit and IBM Quantum Services, executing quantum circuits and implementing foundational quantum algorithms such as the Deutsch-Jozsa and Grover’s algorithms; explored quantum measurement error mitigation techniques to enhance computational accuracy in real-world quantum applications.

Quantum Computing Essentials for Senior Leaders

Developed a strategic understanding of quantum computing, cutting through the hype to grasp its real-world impact on businesses; explored key quantum technologies, potential industry applications, and essential steps for organizations to prepare for the quantum era.

Fundamentals of Quantum Computing

Gained a foundational understanding of quantum computing, its current state, and emerging technological advancements; explored quantum advantage in solving complex problems beyond classical computing capabilities; examined the implications for secure communication, encryption, and potential cybersecurity challenges in the quantum era.

QBronze 135

Acquired fundamental knowledge of quantum computing, including quantum mechanics principles, quantum circuits, and essential quantum algorithms; gained hands-on experience with quantum programming using Qiskit, exploring topics such as qubits, superposition, entanglement, and quantum gates; implemented key quantum algorithms like the Deutsch-Jozsa and Grover’s algorithm, enhancing problem-solving skills in quantum computing.

Deep Reinforcement Learning

Mastered key reinforcement learning concepts, including the exploration-exploitation dilemma, Markov Decision Processes, multi-armed bandit problems, and Bellman equations; implemented value-based methods such as Deep Q-Networks (DQN), Double DQN, Dueling DQN, and prioritized experience replay; explored policy-based methods with stochastic policy gradients, REINFORCE, policy optimization, evolutionary algorithms, Monte Carlo methods, and Generalized Advantage Estimation (GAE); delved into multi-agent reinforcement learning, tackling Markov games and AlphaZero-inspired strategies; applied these techniques to build AI agents for robotics, financial trading, and multi-agent environments.

AI Programming with Python

Built strong Python programming foundations, mastering control flow with conditionals, loops, and list comprehensions; wrote custom functions with proper documentation, understanding variable scope, lambda expressions, iterators, and generators; developed scripting skills to handle user input, file operations, errors, and external modules; gained proficiency in object-oriented programming by creating and utilizing classes, preparing for advanced AI and data science projects.

AWS Machine Learning Fundamentals

Gained comprehensive knowledge of machine learning workflows, covering feature engineering, model training, and deployment using AWS SageMaker (data loading, processing, batch transform, and training jobs); mastered AWS Lambda for serverless AI applications and explored real-world machine learning use cases in various business contexts; strengthened Python, probability, and deep learning fundamentals while building end-to-end intelligent systems with hands-on projects using popular data science libraries.

AWS Machine Learning Foundations 2022

Developed a strong foundation in supervised and unsupervised machine learning, including computer vision, reinforcement learning, and generative AI using AWS ML tools; mastered software engineering practices for data science, covering clean, modular, and efficient code, refactoring, documentation, version control, testing, logging, and code reviews; gained proficiency in object-oriented programming, culminating in building a custom Python package.

Deep Reinforcement Learning Course

Gained practical and theoretical expertise in Deep Reinforcement Learning (Deep RL), mastering popular libraries like Stable Baselines3, RL Baselines3 Zoo, Sample Factory, and CleanRL; trained AI agents across diverse environments, from classic games like Space Invaders and PyBullet simulations to unique scenarios like SnowballFight, Huggy the Doggo 🐶, and VizDoom, building a versatile understanding of Deep RL applications.

AI and Meta-Heuristics (Combinatorial Optimization) Python

Gained expertise in artificial intelligence for combinatorial optimization, mastering pathfinding algorithms such as Breadth-First Search (BFS), Depth-First Search (DFS), and A* search; explored heuristics and meta-heuristics, including genetic algorithms, particle swarm optimization (PSO), and simulated annealing; developed optimization solutions for complex problem-solving using AI-driven search and evolutionary techniques.

Bio-inspired Artificial Intelligence Algorithms

Gained in-depth knowledge of bio-inspired AI techniques, applying evolutionary and swarm intelligence algorithms to real-world optimization problems; minimized airline ticket prices using Genetic Algorithms, optimized meal planning with Differential Evolution, and classified handwritten digits with Artificial Neural Networks; implemented Clonal Selection for adaptive immune system-inspired digit recognition, optimized course scheduling using Particle Swarm Optimization (PSO), and solved shortest path problems with Ant Colony Optimization (ACO).

Curiosity Driven Deep Reinforcement Learning

Developed expertise in implementing deep reinforcement learning agents, mastering Asynchronous Advantage Actor-Critic (A3C) algorithms and parallel processing in Python; gained hands-on experience in reproducing and implementing state-of-the-art deep reinforcement learning research papers; coded the Intrinsic Curiosity Module (ICM) to enhance exploration in RL environments, enabling AI agents to learn efficiently in sparse reward settings.

Practical Multi-Armed Bandit Algorithms in Python

Gained expertise in solving Multi-Armed Bandit (MAB) problems, modeling real-world business challenges as MAB scenarios, and automating decision-making with AI agents; mastered the exploration-exploitation dilemma in reinforcement learning and implemented various algorithmic strategies, including Epsilon-Greedy, Softmax Exploration, Optimistic Initialization, and Upper Confidence Bounds (UCB); developed Python-based solutions for estimating action values through incremental sampling while optimizing reward functions for improved sample efficiency.

Generative Adversarial Networks (GANs)

Gained in-depth knowledge of GAN architecture, mastering PyTorch implementations of basic GANs, advanced DCGANs with convolutional layers, and conditional GANs; compared generative models, evaluated performance using Fréchet Inception Distance (FID), and identified bias in GAN outputs; explored real-world applications like data augmentation and privacy preservation, and built image translation models including Pix2Pix and CycleGAN, with insights into StyleGAN techniques.

Deep Learning

Mastered deep learning fundamentals, including building and training deep neural networks, optimizing architecture parameters, and implementing vectorized models for real-world applications; gained expertise in training-validation analysis, variance handling, and optimization techniques using TensorFlow; developed Convolutional Neural Networks (CNNs) for image recognition, detection tasks, and neural style transfer; built and trained Recurrent Neural Networks (RNNs) for Natural Language Processing (NLP), working with word embeddings, Hugging Face tokenizers, and transformer models for Named Entity Recognition (NER) and Question Answering tasks.

DeepLearning.AI TensorFlow Developer

Acquired hands-on expertise in building, training, and optimizing deep neural networks using TensorFlow, mastering best practices for computer vision, natural language processing (NLP), and time series analysis; implemented data handling techniques, overfitting prevention strategies like augmentation and dropout, and developed NLP systems with Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory networks (LSTMs) trained on large text repositories.

Open Source Software Development, Linux and Git

Gained a strong foundation in open source software development, mastering Linux environments, command-line navigation, and essential tools; developed proficiency in Git for version control, branching, and collaboration workflows, enabling productive contributions to open source projects; earned a verified digital badge from The Linux Foundation, showcasing expertise in open source development practices, Linux systems, and Git version control.

AWS Fundamentals

Gained a comprehensive understanding of core Amazon Web Services (AWS) services, including computing, storage, and networking capabilities; mastered key AWS security concepts, migration strategies from on-premises infrastructure to the cloud, and the fundamentals of building serverless applications; completed hands-on labs and exercises designed by AWS technical instructors to reinforce practical skills in deploying, securing, and scaling applications on AWS.

Preparing for Google Cloud Certification: Cloud Data Engineer

Gained expertise in Google Cloud’s Big Data and Machine Learning products, mastering BigQuery for interactive data analysis, Cloud SQL and Dataproc for migrating MySQL and Hadoop/Pig/Spark/Hive workloads, and evaluating data processing solutions to optimize performance and scalability in cloud-based environments — preparing for the Google Cloud Professional Data Engineer certification.

Machine Learning: Algorithms in the Real World

Developed practical skills to design and implement machine learning projects by defining ML problems, surveying data resources, and identifying viable ML applications; mastered data preparation techniques for effective model performance and learned to translate business needs into actionable machine learning solutions.

Google IT Support

Built a strong foundation in IT support, mastering essential skills like computer assembly, wireless networking, software installation, and customer service; learned to troubleshoot, debug, and provide end-to-end technical support; gained hands-on experience with Linux, Domain Name Systems (DNS), Command-Line Interface (CLI), and Binary Code — preparing for entry-level IT roles with practical, job-ready expertise.

Build Better Generative Adversarial Networks (GANs)

Developed advanced GAN expertise by mastering evaluation challenges, comparing generative models, and applying the Fréchet Inception Distance (FID) for assessing fidelity and diversity; learned to detect bias in GANs and implemented state-of-the-art StyleGAN techniques for high-quality image generation; gained hands-on experience building and training GAN models with PyTorch, exploring real-world applications like privacy preservation and bias mitigation — providing a strong foundation from basic concepts to advanced generative techniques.

Science Research Foundations

Developed a strong understanding of scientific research methodologies, gaining proficiency in the techniques and methods necessary for conducting research, formalizing results, and fostering a culture of rigorous scientific inquiry.

University Admission Prediction Using Multiple Linear Regression

Introduction to Agent-based Modeling with NetLogo

Get Familiar with ML basics in a Kaggle Competition

Introduction to Psychology

Gained a comprehensive understanding of human thought and behavior by exploring topics like perception, communication, learning, memory, decision-making, emotions, and social behavior; examined how these mental processes develop in children, vary across individuals, and are represented in the brain; delved into the causes and effects of mental illness and injury, gaining insights into psychological phenomena such as fears, dreams, happiness, and bias.

Tweet Emotion Recognition with TensorFlow

XG-Boost 101: Used Cars Price Prediction

Apply Generative Adversarial Networks (GANs)

Explored advanced GAN applications in data augmentation, privacy, and anonymity; implemented image-to-image translation frameworks like Pix2Pix and CycleGAN to transform satellite images into map routes and adapt horses to zebras; compared paired and unpaired image-to-image translation techniques, understanding how their differences impact GAN architectures; gained hands-on experience applying GANs to real-world tasks, deepening knowledge in image translation and generative model design.

Time Series Forecasting and ARIMA Models in Python

Behavioral Finance

Explored the psychological factors that influence financial decision-making, focusing on common biases like loss aversion, mental accounting, and overconfidence that lead to predictable financial errors; learned how heuristics impact everyday choices, such as investment decisions and insurance selection; studied strategies to recognize and mitigate these biases, helping improve spending, saving, and investing decisions for better financial outcomes in the future.

Fake Instagram Profile Detector

Modernizing Data Lakes and Data Warehouses with Google Cloud

Gained expertise in differentiating data lakes from data warehouses, exploring use-cases for each storage type, and learning about Google Cloud’s data lake and warehouse solutions; examined the critical role of a data engineer in building successful data pipelines to enhance business operations; understood the advantages of conducting data engineering in a cloud environment for scalability, efficiency, and cost-effectiveness.

Building Resilient Streaming Analytics Systems on Google Cloud

Mastered real-time streaming analytics use-cases and learned to manage data events using Google Cloud’s Pub/Sub asynchronous messaging service; developed streaming pipelines and implemented necessary data transformations; gained hands-on experience integrating Dataflow, BigQuery, and Pub/Sub for seamless real-time streaming and data analysis, enhancing system resilience and scalability.

Google Cloud Big Data and Machine Learning Fundamentals

Gained a comprehensive understanding of the data-to-AI lifecycle on Google Cloud, exploring key big data and machine learning products; designed streaming pipelines using Dataflow and Pub/Sub for real-time data processing; learned different options for building machine learning solutions on Google Cloud; and described machine learning workflows, implementing pipelines with Vertex AI and AutoML for automated model training and deployment.

Building Batch Data Pipelines on Google Cloud

Learned various data loading methods (EL, ELT, and ETL) and their appropriate use cases; gained hands-on experience running Hadoop on Dataproc, optimizing Cloud Storage usage, and fine-tuning Dataproc jobs; built and managed data processing pipelines using Dataflow; and explored pipeline management with Data Fusion and Cloud Composer for streamlined, scalable data workflows.

Build Basic Generative Adversarial Networks (GANs)

Gained foundational knowledge of GANs and their applications, understanding the key components and intuition behind their architecture; explored and implemented various GAN models, including conditional GANs for generating category-specific images; built hands-on experience by training GANs with PyTorch and applying them to real-world image generation tasks, while also exploring the social implications of bias detection and privacy preservation in machine learning.

Preparing for your Professional Data Engineer Journey

Reviewed the domains covered in the Professional Data Engineer (PDE) certification exam and assessed gaps in knowledge and skills across each domain, preparing to effectively tackle the certification exam by strengthening key areas of expertise in data engineering.

Smart Analytics, Machine Learning, and AI on Google Cloud

Learned to differentiate between machine learning (ML), artificial intelligence (AI), and deep learning, and explored the use of ML APIs for analyzing unstructured data; executed BigQuery commands from notebooks for data processing; and created machine learning models using SQL syntax in BigQuery and without coding through Vertex AI AutoML for automated model development.

AWS Fundamentals: Building Serverless Applications

Gained foundational knowledge of AWS serverless architecture, learning to design and implement serverless solutions for real business problems; explored key AWS services such as Lambda, API Gateway, DynamoDB, and Lex, and gained hands-on experience in deploying scalable serverless applications using these tools.

Introduction to Philosophy

Explored key areas of contemporary philosophy, including epistemology, philosophy of science, philosophy of mind, political philosophy, moral philosophy, and metaphysics. Gained insight into foundational questions about knowledge, reality, morality, and the mind, while understanding philosophical methods and their distinction from other disciplines.

Transacting on the Blockchain

Learned about seven types of cryptoassets and the concept of asset tokenization. Gained an understanding of smart contracts and their diverse applications. Explored distributed, self-sovereign identity systems and examined how blockchain technology disrupts eight core functions of the financial services industry.

AWS Fundamentals: Going Cloud-Native

Gained knowledge of AWS core concepts, including Regions, Availability Zones, and Virtual Private Clouds (VPCs). Learned how to utilize AWS compute, storage, database, and security services through the AWS Console. Explored strategies to design durable, distributed, and highly available applications.

AWS Fundamentals: Migrating to the Cloud

Gained insights into analyzing current environments, planning cloud migrations, and understanding AWS services commonly used in migration processes. Explored step-by-step migration strategies and reviewed relevant AWS documentation, example applications, and code samples to support practical understanding.

Moral Foundations of Politics

Explored key political theories from the Enlightenment, including Utilitarianism, Marxism, and the social contract tradition, alongside their historical and contemporary contexts. Examined Anti-Enlightenment perspectives and modern democratic justifications, focusing on practical debates surrounding economic inequality, affirmative action, healthcare distribution, free speech, religious regulation, and global environmental challenges.

Digital Footprint

Explored the importance of managing online presence, balancing privacy with professional visibility. Learned strategies to curate a positive digital footprint, handle personal and professional boundaries (eprofessionalism), and leverage online networks for growth. Gained insights from experts on recognizing hidden data trails and using tools to control and enhance one’s online identity.

Programming a Quantum Computer with Qiskit

An Introduction to Consumer Neuroscience & Neuromarketing

Explored how consumers make decisions, what captures attention, and how unconscious processes influence final choices. Gained insight into brain mechanisms behind consumer behavior and learned key methods used in neuromarketing and consumer neuroscience. Examined current and future applications of neuroscience in business to enhance marketing strategies.

Introduction to Blockchain Technologies

Covered the limitations of the traditional Internet for business and how blockchain reshapes trust in digital transactions. Learned key terms like miner, hash, nonce, proof-of-work, and public key cryptography. Explored the steps of a blockchain transaction, seven core design principles, and ten major challenges in implementing blockchain solutions.

AWS Fundamentals: Addressing Security Risk

Gained a solid understanding of foundational security concepts like "least privilege" and the "Shared Responsibility Model." Explored detective controls including Amazon CloudTrail, AWS Security Hub, Amazon GuardDuty, and AWS Config. Learned about data encryption best practices for data at rest and in motion, along with securing storage across AWS services. Studied the AWS Well-Architected Framework and strategies for protecting compute resources like Amazon EC2 and AWS Lambda.

Sequences, Time Series and Prediction

Learned to solve time series and forecasting problems using TensorFlow. Gained expertise in preparing data for time series modeling following best practices. Explored how Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (ConvNets) can be applied to sequential data for predictions. Built a real-world sunspot prediction model, applying these techniques to practical time series data.

Fundamentals of Scalable Data Science

Gained foundational knowledge of Apache Spark using Python and PySpark, focusing on scalable data processing. Learned to perform exploratory data analysis and data preprocessing tasks, including identifying data patterns, trends, and outliers. Covered essential statistical measures and data visualization techniques such as Box Plots, Scatter Plots, Pareto Charts, and 3D visualizations. Explored dimension reduction, feature selection, partitioning, and parallel analysis to handle large datasets efficiently. Utilized Jupyter Notebooks and IBM Watson Studio for hands-on experience, earning an IBM digital badge upon completion.

Using Git for Distributed Development

Acquired hands-on skills in setting up, managing, and collaborating on Git repositories. Learned to initialize repositories, track changes, and push updates to remote repositories for distributed development. Gained practical knowledge of commits, diffs, merges, and rebases, along with techniques to identify errors, roll back to previous states, and troubleshoot workflow issues. Developed a strong understanding of Git workflows, enabling efficient collaboration in open-source and large-scale distributed projects.

Convolutional Neural Networks in TensorFlow

Gained practical experience handling real-world image data and building CNN models using TensorFlow. Learned to track model performance through loss and accuracy plots, apply strategies like data augmentation and dropout to prevent overfitting, and implement transfer learning to extract learned features from pre-trained models for improved performance on new datasets.

Applied AI with DeepLearning

Gained hands-on experience with Deep Learning models, focusing on Keras and TensorFlow, while also exploring PyTorch, DeepLearning4J, and Apache SystemML. Covered key areas such as Anomaly Detection, Time Series Forecasting, Image Recognition, and Natural Language Processing (NLP) with real-world datasets from IoT, financial markets, literature, and image databases. Additionally, learned to scale models using Kubernetes, Apache Spark, and GPU acceleration for efficient large-scale deployment.

Philosophy and the Sciences: Introduction to the Philosophy of Cognitive Sciences

Explored key intersections between philosophy and cognitive sciences, covering topics such as the evolution of the human mind, the nature of consciousness, and the relationship between cognition, machines, and the environment. Developed critical thinking skills to evaluate complex conceptual questions surrounding human intelligence, consciousness, and cognitive processes.

Natural Language Processing in TensorFlow

Learned to build NLP systems using TensorFlow, including text preprocessing (tokenization, vector representation), and applying advanced architectures like RNNs, GRUs, and LSTMs. Gained hands-on experience training LSTM models on existing text to generate creative outputs like poetry.

Open Source Software Development Methods

Explored the history and impact of open source software, learning collaboration best practices, fostering diversity in projects, and understanding different licensing models. Gained insights into key development methods like Continuous Integration and leveraging platforms like GitHub for effective project management, with examples from successful open source projects.

Linux for Developers

Gained practical knowledge of Linux systems, including its history, architecture, and the distinction between the kernel and the full operating system. Explored the graphical system, command-line navigation, filesystem structure, partitions, and essential monitoring utilities. Learned foundational skills for daily work on Linux systems, preparing for development and system management tasks.

Linux Tools for Developers

Built proficiency with essential Linux command-line tools and advanced Bash scripting for automating complex tasks. Explored filesystem management, program compilation using gcc and other compilers, and working with different types of libraries. Gained insight into running Java applications on Linux, addressing compatibility concerns. Additionally, learned to package software for easy deployment and distribution.

IT Security: Defense against the digital dark arts

Covered various IT security concepts, including encryption algorithms, authentication, authorization, and accounting (the three A's of information security). Explored network security solutions such as firewalls and Wi-Fi encryption. Built an understanding of multi-layered security architectures and best practices for risk evaluation and reduction. Gained insight into integrating a security culture within organizations and helping others grasp security concepts to protect themselves. Also included AI skills from Google experts to enhance IT security tasks.

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

This course dives into understanding and improving deep learning models. You'll learn best practices for training, developing test sets, and analyzing bias/variance to build robust deep learning applications. The course covers techniques like initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking. You'll also explore optimization algorithms such as mini-batch gradient descent, Momentum, RMSprop, and Adam, and learn how to check for convergence. Additionally, you'll gain hands-on experience implementing neural networks in TensorFlow, providing a solid foundation for advancing in AI and machine learning.

Sequence Models

Covered various sequence modeling techniques, including Recurrent Neural Networks (RNNs), GRUs, and LSTMs. Explored the application of these models to tasks such as Character-level Language Modeling and Natural Language Processing (NLP). Gained experience with HuggingFace tokenizers and transformer models for solving NLP tasks like Named Entity Recognition (NER) and Question Answering. Applied models to real-world use cases such as speech recognition, music synthesis, chatbots, and machine translation, gaining insights into cutting-edge AI applications.

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Covered best practices for using TensorFlow, a widely-used open-source machine learning framework. Learned how to build and train a basic neural network in TensorFlow for a computer vision application. Explored the use of convolutions to improve neural network performance and understanding, gaining practical experience in deep learning techniques.

Operating Systems and You: Becoming a Power User

Covered the main components of operating systems and critical tasks like managing software, users, and hardware configuration. Gained hands-on experience navigating Windows and Linux filesystems via both graphical user interface and command line. Learned how to set up users, groups, and permissions, install and configure software, and configure disk partitions. Explored system processes, log management, and remote connection tools. Developed troubleshooting skills for common issues, preparing for IT Support Specialist roles.

Convolutional Neural Networks

Gained expertise in building convolutional neural networks (CNNs) and applying them to visual recognition tasks, such as autonomous driving, face recognition, and radiology image analysis. Learned how to implement advanced CNN variations like residual networks and neural style transfer for art generation. Developed practical skills in working with 2D and 3D image, video, and other visual data, preparing for advanced AI applications in computer vision.

System Administration and IT Infrastructure Services

Developed expertise in systems administration, including managing cloud infrastructure, configuring and managing servers, and using industry tools to handle computers, user information, and productivity. Gained hands-on experience in selecting hardware, vendors, and services, and managing infrastructure servers. Learned to manage organizational user data with directory services like Active Directory and OpenLDAP. Mastered disaster recovery and data backup strategies to ensure organizational continuity and improve IT processes.

Neural Networks and Deep Learning

Gained a solid understanding of neural networks and deep learning, including key technological trends, and learned to build, train, and apply fully connected deep neural networks. Mastered the implementation of efficient, vectorized neural networks and identified essential parameters in neural network architectures. Acquired the skills to apply deep learning to real-world applications, setting a strong foundation for advancing in AI technology.

Optimizing Machine Learning Performance

Completed a comprehensive machine learning project, focusing on preparing a machine learning maintenance roadmap and addressing changing data. Gained skills in identifying and interpreting unintended effects, and defining procedures to operationalize and maintain machine learning models. Acquired the tools and understanding needed to confidently roll out and optimize machine learning projects in a business context.

Structuring Machine Learning Projects

Gained experience in leading machine learning projects, including diagnosing errors, prioritizing strategies to reduce them, and understanding complex machine learning settings. Learned how to apply end-to-end learning, transfer learning, and multi-task learning. Developed the skills to diagnose and solve issues such as mismatched training/test sets and surpassing human-level performance, preparing for a leadership role in AI projects and teams.

Ready, Set, Future! Introduction to Futures Thinking

Developed skills in futures thinking by learning to collect and analyze signals of change, identify historical patterns to predict future shifts, and explore real-world futures projects. Gained insights into preventing "future shock" and guiding others to adapt proactively to emerging changes.

Machine Learning Algorithms: Supervised Learning Tip to Tail

Gained hands-on experience with supervised learning techniques, implementing decision trees, k-nearest neighbors, and support vector machines on real-world business case studies. Developed skills to evaluate data preparation steps, address practical issues in applied ML, and analyze production challenges, backed by a solid foundation in Python, linear algebra, and statistics.

The Science of the Solar System

Explored the science behind solar system exploration, integrating principles from physics, chemistry, biology, and geology to analyze Mars, the outer solar system, exoplanets, and the search for habitability. Developed critical thinking through advanced-level content and assessments designed to encourage analysis over memorization.

The Bits and Bytes of Computer Networking

Gained a comprehensive understanding of computer networking, covering modern networking technologies, TCP/IP protocols, DNS, DHCP, and cloud computing concepts. Built practical skills in network troubleshooting and analyzed networks through a five-layer model, preparing for real-world IT environments.

Data for Machine Learning

Developed a strong foundation in handling data for machine learning, including understanding data biases, overfitting mitigation, feature engineering, and model validation. Gained practical skills in improving model generalization and analyzing the impact of algorithm parameters on performance, with a focus on training, testing, and operational phases.

Web Application Security Testing with Burp Suite

Introduction to Applied Machine Learning

Gained practical knowledge in defining machine learning problems across various domains like finance, medicine, and business, transforming business needs into ML applications by surveying data resources, preparing data, and applying structured approaches to problem-solving and automation.

Web Application Security Testing with OWASP ZAP

Acquired hands-on skills in web application security testing using OWASP ZAP, including setting up and configuring the ZAP proxy, scanning websites for vulnerabilities, performing spider crawls to discover URLs, and leveraging dictionary lists to uncover hidden files and directories.

Getting Started with Essay Writing

Gained proficiency in academic essay writing by learning to craft strong thesis statements, structure compare/contrast, cause/effect, and argumentative essays, and develop well-organized, compelling body paragraphs — essential skills for college-level writing and beyond.

Introduction to Personal Branding

Developed a strong foundation in personal branding by learning to define and embody a unique brand identity, create a mission statement, establish a presence on multiple social media platforms, build a personal "board of directors," and implement digital security and reputation management strategies for ongoing brand maintenance.

Global Diplomacy – Diplomacy in the Modern World

Gained a critical understanding of global diplomacy by exploring its evolution, practices, and relationship to contemporary politics, combining theoretical and empirical approaches from International Studies to analyze diplomatic changes and their historical and modern contexts.

Computational Neuroscience

Explored computational methods for understanding nervous system functions, including topics such as spiking neurons, neural networks, and learning algorithms, using Matlab/Octave/Python demonstrations to study sensory-motor control, vision, learning, and memory.

AI For Everyone

Gained an understanding of AI terminology (e.g., neural networks, machine learning, deep learning), explored its realistic capabilities and limitations, and learned how to identify AI opportunities within organizations, work with AI teams, build AI strategies, and address ethical considerations in AI.

Fundamental Neuroscience for Neuroimaging

Explored the fundamental principles of neuroimaging methods, including functional MRI, diffusion tensor imaging, and PET imaging, while gaining an understanding of neuroimaging physics, human neuroanatomy, cognitive domains, and experimental design. The course also reviewed the history, goals, and limitations of these methods in clinical and research settings.

Technical Support Fundamentals

Gained foundational knowledge in Information Technology, including computer hardware, software, troubleshooting, and customer service, with skills to assemble computers, install operating systems, understand the binary system, and grasp how the Internet works and its impact on the modern world.

The Brain and Space

Explored how the brain constructs our sense of spatial location from sensory and motor inputs, affecting cognitive abilities such as memory and perception.