I am deeply enthusiastic about the realms of Machine Learning and Data Science. In an era defined by technological progress, I am profoundly convinced of the immense potential that Artificial Intelligence holds across various domains. The exponential strides in computing prowess coupled with the unprecedented access to vast datasets propel the domains of Data Science and Machine Learning into a realm of boundless opportunities. My resolute aspiration is to ascend to the echelons of world-class expertise in these transformative fields.
I hold a Bachelor's & Masters's Degree in Data Science at Vietnam National University of Agricultural - Faculty of Environmental Science. I have 3+ years of experience building, and deploying machine learning & deep learning models. Furthermore, I completed courses such as Machine Learning Certification by Stanford University, IBM Data Science Professional Certificate IBM, and Deep Learning Specialization by Andrew Ng. These transformative courses have sculpted my theoretical understanding of machine learning, deep learning, and data science. Later, I began to work on projects ranging from the pharmaceutical, agricultural, retail, service, and real estate industries.
I worked at companies such as INOGSI, and Soils and Fertilizers Institute as a Soil Scientist & Data Scientist. These professional experiences have been pivotal in harnessing the power of data to create tangible value.. This portfolio is created to demonstrate a wide range of skills that I possess in solving and tackling machine learning problems. Thanks.๐
Starting in highschool, I acquired coding skills by initially learning the PASCAL language. Currently, I am expanding my knowledge by studying Python, which enhances my comprehension of machine learning and data science. Consequently, I have developed a strong grasp of coding principles. Additionally, I have furthered my expertise by gaining proficiency in other programming languages, including R, which are essential for pursuing a career as a machine learning engineer or a data scientist.
In parallel, my pursuit of excellence has led me to embrace a diverse array of programming languages. Proficiency in languages like R has fortified my skill set, vital for carving a path as a machine learning engineer or a data scientist.
My voyage through the realms of data science and machine learning has been a transformative one, culminating in the acquisition of a multifaceted skill set. This comprehensive toolkit encompasses a spectrum of invaluable techniques, arming me with the prowess needed to conceptualize and actualize intricate machine-learning projects with finesse.
To showcase my skills, I invite you to explore my GitHub profile. It presents a range of programming languages I'm proficient in and highlights the commitment I have to my projects. By examining this overview, you can gain an understanding of the extent and depth of my expertise.
I encourage you to explore my machine learning and deep learning projects to gain insight into my expertise. The links are provided below, along with detailed descriptions at the bottom of this website. My portfolio showcases a diverse and in-depth understanding of various areas, including computer vision, natural language processing, and more.
| ๐ดโ Washington Bike Demand Prediction | ๐ Car Prices Prediction |
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| ๐ฆ Predicting Loan Default | ๐ซ Heart Disease Prediction |
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| ๐ Airbnb Home Prices Prediction | โ๏ธ Telco Customer Churn Prediction |
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| ๐ Predicting Readability of Texts | ๐น Twitter Sentiment Analysis |
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| ๐ Fake News Prediction | ๐ Automated Essay Scoring with Transformers |
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| ๐ Wheat Disease Detection Using Deep and Transfer Learning | ๐ Solar Panels Dust Detection |
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| ๐พ Wheat Localization With Convolutional Neural Networks (CNNs) | ๐ฅ Steel Defect Detection |
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| ๐ข MNIST Digits Classification | ๐ธ Convolutional Neural Networks CNN Implementation Using Keras |
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| ๐ Article Recommender System |
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| ๐ฝ YouTube Video Analysis | ๐ Google Play Store Genre Prediction |
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| ๐ Cab Reservation System |
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| ๐ฟ IMDB Movies Web Scraping | ๐ Restaurant Recipes Web Scraping XML |
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| ๐ฎ Popular Gaming Titles Wikipedia Web Scraping | ๐ University Instructors Information Scraping |
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| ๐ JSON file Web Scraping |
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| ๐ Adare Restaurant Webpage | ๐๐ฒ Roar Bikes Webpage |
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โโ ๐งช๐จโ๐ฌ Soils scientist | Soils and Fertilizers Institute [May 2009 - Present]
โโ ๐ค๐ฆพ Data Scientist | Ignosi Global [Nov 2020 - Present]
โโ ๐ซ Vietnam National University of Environment (Faculty of Environment Science) - Masters of Environment Science
โโ ๐ซ Vietnam National University of Environment - B.Tech in Environment Science
โโ ๐ซ Xuan Dinh High School - High School
โโ ๐ซ Nguyen Binh Khiem School - Junior High School
โโ ๐ฑ Machine Learning by Stanford University 
โโ ๐ฑ IBM Data Science
โโ โโ ๐ Advance Data Science
โโ โโ ๐ Google IT Automation with Python
โโ ๐ฆธ Leadership Skills
โโ ๐ฆธ Communication Skills
โโ ๐ฆธ Team Work
โโ ๐ฆธ Curiositys
โโ ๐ฆธ Problem-solving Skills
โโ ๐ฆธ Time Management
โโ ๐ https://www.https://www.kaggle.com/thangdt1109
โโ ๐ Data Scientist Resume
โโ ๐ซ Email: [email protected]
โโ ๐ LinkedIn: https://www.linkedin.com/in/thang-do-trong-747b7562/
โโ ๐ป https://www.https://www.kaggle.com/thangdt1109
My interest in machine learning and artificial intelligence began when I was in my final year of a Bachelor of Technology program. I suggested to my teammates that we work on a machine learning project in healthcare, specifically using it to predict the chances of a person suffering from heart disease. We were able to download a dataset from Kaggle and implement machine learning models to make predictions.
The results of the test set were promising, and this sparked my interest in finding more ways to apply machine learning. I have since taken courses in machine learning, including Andrew Ng's Machine Learning and Deep Learning Specialization, and have worked on various data science projects in healthcare, academia, and the retail industry. Some of these projects involved data analysis and visualization to gain insights.
There are numerous machine learning and data science courses that I went through in order to gain a theoretical understanding of the concepts before their practical implementation in the form of projects. Below are some of my certifications and the contents covered in the course respectively.
๐ฑ ASSOCIATE DATA ANALYST 
As a participant in the Associate Data Analyst course, I was empowered with the skills to extract, clean, and transform data into actionable intelligence. I also gained proficiency in data visualization, enabling me to craft impactful narratives that drive informed decision-making. Ultimately, the course serves as a springboard, nurturing the growth of aspiring data analysts and preparing them to make valuable contributions in today's data-driven landscape.
๐ฑ IBM Data Science
Through a well-structured curriculum, I was guided through the entire data science lifecycle, from data collection and wrangling to exploratory analysis, machine learning, and model deployment. With a hands-on approach, learners engage with real-world datasets and industry-relevant tools, gaining practical experience that bridges the gap between theoretical knowledge and real-world applications.
By completing the IBM Data Science course, individuals not only solidify their understanding of key concepts but also gain proficiency in utilizing cutting-edge technologies such as Python, Jupyter Notebooks, and various data analysis libraries. This course not only imparts technical skills but also emphasizes collaboration, critical thinking, and effective communication โ attributes crucial for succeeding in the dynamic landscape of data science.
Ultimately, the IBM Data Science course stands as a beacon for aspiring data professionals, offering them the resources, guidance, and knowledge to confidently navigate the intricacies of data analysis and make meaningful contributions to the ever-evolving field of data science.
โโ ๐ Advance Data Science by IBM
Designed as a natural progression for those with a foundational understanding of data science, this course delves into advanced topics such as deep learning, natural language processing, and big data technologies. Participants are exposed to cutting-edge tools and methodologies that empower them to extract deeper insights from data, develop sophisticated machine-learning models, and harness the potential of unstructured data sources.
The course is characterized by a blend of theoretical learning and hands-on experience, ensuring that learners not only comprehend complex concepts but also acquire the ability to apply them effectively. Through real-world case studies and projects, participants gain the proficiency to unravel intricate patterns within data and engineer solutions that drive innovation and strategic decision-making.
By enrolling in the Advanced Data Science course by IBM, individuals not only position themselves as frontrunners in the data science landscape but also engage with a community of like-minded learners, experts, and mentors. This journey symbolizes a commitment to mastering the cutting edge of data science, unlocking the doors to intricate insights and transformative discoveries in a world fueled by data-driven possibilities.
๐ฑ Machine Learning by Stanford University
Delivered through a structured curriculum, this course covers the fundamental principles and techniques that underlie machine learning. From supervised and unsupervised learning to neural networks and deep learning, participants embark on a journey that gradually unveils the layers of machine learning intricacies. The theoretical concepts are seamlessly interwoven with practical implementation, offering learners hands-on experience with real-world datasets and tools.
The course stands out not only for its technical depth but also for Andrew Ng's skillful articulation of complex concepts, making them accessible to learners from diverse backgrounds. As a result, the Machine Learning course has become a stepping stone for aspiring data scientists, engineers, and researchers seeking to harness the power of machine learning to solve real-world problems.
The Machine Learning course by Stanford University and Andrew Ng transcends being just an online course; it's a transformative learning experience that has ignited a wave of curiosity and understanding in the field of machine learning, enriching learners with the tools to navigate the data-driven landscape and make impactful contributions.
๐ฑ Google IT Automation with Python Professional Certificate 
Structured to be both comprehensive and practical, the program covers a spectrum of IT topics including troubleshooting, system administration, networking, and automation. However, what truly sets this course apart is its focus on utilizing Python for automation. Participants gain hands-on experience in leveraging Python to streamline processes, automate tasks, and optimize IT operations, aligning with the contemporary demands of efficiency and scalability.
Through interactive exercises, real-world projects, and guided labs, learners engage in a learning process that mirrors real IT scenarios. This experiential approach ensures that graduates not only acquire theoretical knowledge but also the practical skills needed to navigate the dynamic IT landscape.
The Google IT Automation with Python Professional Certificate isn't just about gaining skills; it's about becoming adept at managing IT infrastructure while harnessing the power of automation. Whether you're a novice seeking to enter the IT field or a seasoned professional aiming to enhance your skill set, this program stands as a testament to Google's commitment to equipping individuals with the expertise to thrive in the world of IT automation.
๐ฆธ Leadership Skills
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During my roles as a Soil Scientist at the Soils and Fertilizers Institute and as a Data Scientist at Ignosi Global, my leadership skills took on a vital role in shaping successful outcomes and fostering collaborative environments.
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At the Soils and Fertilizers Institute, I was entrusted with guiding projects that demanded not only technical expertise but also effective leadership. By spearheading teams of researchers and technicians, I not only shared my knowledge as a soil scientist but also cultivated an atmosphere of teamwork, where each member's insights were valued and integrated. Through this experience, I refined my ability to navigate the intricacies of project management while fostering an environment where innovation and collaboration thrived.
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Similarly, during my tenure at Ignosi Global as a Data Scientist, my leadership skills were pivotal in driving projects that hinged on data-driven insights. I led cross-functional teams, merging the analytical prowess of data science with effective communication and coordination. This required not only a deep understanding of the technical aspects but also the finesse to align diverse perspectives and talents toward a common goal. This dual role of technical guidance and team orchestration honed my leadership skills, enabling me to inspire and guide teams toward effective problem-solving and innovative solutions.
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Both experiences underscore my ability to harmonize domain-specific knowledge with leadership acumen, ensuring that every project I engage in benefits from a synthesis of expertise and collaboration.
๐ฆธ Communication Skills
Proficient communication skills are a pivotal facet in the toolkit of a successful software engineer and data scientist. The ability to convey ideas clearly and effectively is not merely an asset but a cornerstone for both roles.
In the complex landscape of software engineering and data science, communication operates as a connective thread that weaves the team's progress, project development, and overarching workflow into a coherent narrative. The role of communication goes beyond just sharing updates; it encompasses the facilitation of seamless collaboration, allowing team members to synchronize their efforts and align their understanding.
During my pursuit of a master's degree, I consciously honed my communication skills. Engaging in discussions about assignments and projects not only solidified my comprehension of the subject matter but also compelled me to articulate complex concepts in a relatable manner. This practice extended beyond academia; I recognized its relevance in professional settings, where effective communication is the linchpin to harmonious teamwork and project success.
Recognizing the multi-dimensional importance of communication, I've pursued courses dedicated to enhancing this skill set. These courses aren't just about rhetoric; they equip me with the ability to engage with diverse audiences, whether it's a group of peers, clients, or stakeholders. This ensures that the information I convey not only reaches its destination but resonates and influences effectively.
In the realms of software engineering and data science, where innovation hinges on collaboration and understanding, my commitment to cultivating communication skills is a testament to my dedication to fostering effective partnerships, facilitating project advancements, and driving impactful results.
๐ฆธ Team Work
- Effective teamwork is crucial for project success and discussing outcomes with various stakeholders.
- That's the reason why companies like Apple and Facebook attribute their revenue growth to collective team efforts.
- Leveraging my active participation in numerous team-based projects, I've refined my networking and team management skills, emphasizing the significance of collaborative accomplishments.
๐ฆธ Curiosity
- Fostering creativity during application development is the catalyst for producing superior and highly innovative products.
- The most remarkable advancements often stem from a spirit of curiosity within the field.
- I am a firm believer that cultivating a deep sense of curiosity in our pursuits paves the way for superior outcomes, not only in the immediate context but also in the long-term journey ahead.
- These skills are instrumental in swift and efficient issue resolution, often acquired through formal education or training.
- The process involves acquainting oneself with prevalent challenges across industries and gaining insights from seasoned professionals.
- This holistic approach aids in swiftly addressing issues by drawing from a well-rounded pool of knowledge and experience.
๐ฆธ Time Management
- While working, I focused on mastering time management to deliver optimal outcomes for the company through efficient strategies and swift execution.
- This led me to actively engage in organizing and planning diverse activities.
- Consequently, the company witnessed heightened productivity as time was deftly managed, fostering a culture of effective practices.
In essence, my core value revolves around fostering mutual respect among individuals. This principle not only guides us towards a trajectory of positive impact but also steers us in the right direction as a society. By embracing education's universal accessibility and celebrating the distinctive strengths of each person, we can forge a path toward transformative change, ensuring that every individual's potential is unlocked and contributing to the betterment of our collective journey..
Amidst the expansive realm of machine learning and data science, a surge of research is underway, ushering forth cutting-edge algorithms driven by prominent companies and research institutions; my aspiration is to adeptly articulate and disseminate insights regarding these evolving trends.
โโ ๐๐ Vision Transformers (ViTs) - The transformative impact of Transformers in the realm of Natural Language Processing (NLP) is well-recognized, generating contextualized representations from text inputs. An intriguing avenue of research has emerged, exploring the application of these Transformers in computer vision tasks. Convolutional Neural Networks (CNNs), which account for spatial relationships in images, could potentially be supplanted by Vision Transformers, leveraging contextual dependencies for image analysis; though not yet surpassing CNN performance, the increasing complexity of Vision Transformers hints at their potential future role in image processing.
โโ ๐ฃ๐ LIME & SHAP (Explainable AI) - LIME, or Local Interpretable Model-agnostic Explanations, and SHAP, which stands for Shapley Additive Explanations, address a fundamental challenge in AI and machine learning: the lack of interpretability. While models like Random Forests and Decision Trees provide feature importance on a broader scale, they often fall short in explaining specific query points. LIME and SHAP bridge this gap by offering explanations for individual outcomes within machine learning models, enhancing interpretability significantly, and making their execution simpler through readily available libraries.
โโ ๐๐ BERT & RoBERTa - BERT, which stands for Bidirectional Encoder Representations from Transformers, and RoBERTa, an acronym for Robustly Optimized BERT Pre-training Approach, have garnered significant attention for their exceptional performance in natural language processing tasks. Operating as transformers, these models employ bidirectional context vector representation, encompassing both the forward and backward pass. This approach effectively captures the contextual nuances inherent in human language, establishing their prowess in comprehending word meaning within a document's wider context.
โโ ๐๐ฆ FAISS & ScaNN - When dealing with identifying similar rows within high-dimensional vector representations of data, conventional algorithms tend to be time-consuming when utilizing metrics like the Euclidean distance or cosine similarity. Addressing this, Facebook Research introduced FAISS, streamlining the search for similarity and vector clustering. In a similar vein, Google AI Research developed ScaNN, accelerating the computation of feature distances and significantly reducing time complexity. The outputs of these models yield information-rich vectors that prove valuable for constructing effective recommender systems, recommending highly similar items to users, and powering a range of other applications.
โโ ๐ง๐ Audio Signal as a Spectrogram - In the realm of audio processing, our audio signals find representation through spectrograms. This transformation holds a significant role in deep learning, as audio signals are converted into spectrogram images that subsequently merge with convolutional neural networks (CNNs) for tasks in Natural Language Processing (NLP). This innovative approach effectively reframes the audio challenge as a computer vision problem, yielding heightened accuracy and improved outcomes. Leveraging the prowess of CNNs, particularly when coupled with transfer learning and ample image data, bolsters performance across various NLP tasks, including but not limited to speech detection.
Machine learning's widespread applications across industries underscore its high demand and expansive scope. Aspiring machine learning engineers have a plethora of tools and resources at their disposal for skill development. However, they must grapple with several intrinsic challenges that require addressing before effectively leveraging machine learning for analysis.
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Availability of data - Acquiring access to high-quality data is pivotal for accurate machine learning predictions. Enterprises often face constraints with data volume and predictive accuracy, hindering effective machine learning utilization.
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Infrastructure Requirements - Startups and companies may lack the required infrastructure for seamless machine learning operations. To bridge this gap, some resort to third-party platforms like Amazon Web Services (AWS), raising concerns about data security and ownership.
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Rigid Business models - The rigidity of business models can limit flexibility in allocating resources for effective machine learning implementation. Adaptability is crucial for incorporating deep learning models that are versatile and robust, especially in the face of dynamic market demands.
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Talent Shortage - Despite the increasing popularity of roles like Machine Learning Engineers and Data Scientists, a talent gap persists in the industry. While educational institutions are introducing AI programs, there remains a need to bolster accessible courses and online learning platforms, encouraging more individuals to enter the field of data science and machine learning.
Furthermore, I'm an avid sports enthusiast and have found a unique avenue for camaraderie and teamwork through my passion for football. Embracing the physical realm, I've forged connections and built bonds with fellow teammates, resonating with the principles of collaboration and strategic thinking that underlie both the world of sports and the domains of data science and machine learning. Just as in a football team, where unity and camaraderie are essential, my experiences on the field highlight my prowess as a team builder and collaborator, traits that seamlessly translate across diverse domains.
Understanding the significance of a well-rounded life, I appreciate the importance of engaging in a spectrum of activities to facilitate holistic growth. While my dedication remains steadfast in the intricate landscapes of data science and machine learning, I also recognize the vitality of leisure activities like football. This dynamic balance between my professional and personal pursuits empowers me to infuse fresh perspectives and innovative thinking into both spheres, enhancing my problem-solving capabilities and enriching my ability to foster collaboration. Just as every player contributes uniquely to a football team, I bring a blend of leadership, collaboration, and strategic insight to the table, ensuring that I contribute effectively both on and off the metaphorical field.
https://www.ibm.com/cloud/learn/machine-learning
https://www.salesforce.com/eu/blog/2020/06/real-world-examples-of-machine-learning.html
https://www.springboard.com/library/machine-learning-engineering/how-to-become/
https://machinelearningmastery.com/what-is-deep-learning/
https://healthinformatics.uic.edu/blog/machine-learning-in-healthcare/
https://www.analyticsvidhya.com/blog/2021/05/convolutional-neural-networks-cnn/
I've showcased a selection of my Projects and Certifications on my GitHub repository. I'm enthusiastic about continuing my learning journey in the realms of AI and Machine Learning through additional courses and diverse projects. Feel free to reach out if you require any clarifications or insights regarding my projects. Eagerly anticipate the opportunity to contribute and share knowledge within the community.
Below, you'll find various avenues for connecting. Your insights and thoughts are most welcome. Thank you!
๐ LinkedIn: https://www.linkedin.com/in/thang-do-trong-747b7562/
๐ซ Email: [email protected]
๐๐๐๐๐๐๐๐๐๐๐๐
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent