certificates, Courses, Skills, and Languages

This course provides informative steps covering the LLM-based generative AI lifecycle, from choosing the model to integrating it into different applications. Throughout the course, I learned a lot about transformers, prompt engineering, in-context learning, quantization, fine-tuning using PEFT/LoRA, how to avoid Catastrophic forgetting, LLMs evaluation metrics Rouge, Bleu score, and evaluation benchmarks like HELM, as well as Reinforcement Learning from Human Feedback (RLHF) to align the LLMs with human feedback.

What I can do by finishing this course

• Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment
• Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases
• Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements
• Apply state-of-the-art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of my project
• Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners

In this Specialization, I explored the roles of data professionals within an organization. I created data visualizations and applied statistical methods to investigate data. I also worked on Building regression and machine learning models to analyze and interpret data. I learned how to communicate insights from data analysis to stakeholders.

skills I gained

• Python Programming
• exploratory data analysis (EDA)
• Tableau
• Descriptive Statistics
• Inferential Statistics
• Probability
• Probability Distriputiob
• Sampling
• Machine Learning Models
• Feature Engineering

Transferable skills I gained

• Determining the appropriate models based on problem and data.
• Using different machine learning models to find a soloution
• Explaining my process when working with machine learning models

In this Specialization, I learned how to build, train, tune, and deploy machine learning models with purposebuilt tools in the AWS cloud. I developed practical skills to effectively deploy my data science projects using well-established methodologies and overcome challenges at each step of the ML workflow using Amazon SageMaker. I've become familiar with the capabilities and challenges of practical data science in production environments. Then I became ready to level up my career by conducting complex data analysis and solving real-world business problems.

skills I gained

• Natural Language Processing with BERT
• ML Pipelines and ML Operations (MLOps)
• A/B Testing and Model Deployment
• Data Labeling at Scale
• and Automated Machine Learning (AutoML)

In this Specialization, I learned how to describe machine learning, deep learning, neural neetworks, and ML algorithms like classification, regression, clustering, and dimensional reduction. I implemented supervised and unsuoervised machine learning models using SciPy and SciKitLearn. I deployed machine learning algorithms and pipelines on Apache Spark. I built deep learning models and neural networks using Keras, PyTorch, and TensorFlow.

skills I gained

• Machine Learning
• Deep Learning
• Python Programming
• Mathematics
• Mathematical Theory & Analysis
• Data Analysis
• Network Architecture
• Data Visualization
• Big Data

In this Specialization, I mastered the most up-to-date practical skills and knowledge that data scientists use in their daily roles. I learned the tools, languages, and libraries used by professional data scientista, including Python and SQL. I learned how to import and clean data sets, analyze and visualize data, and how to build machine learning models and piplines. I applyed the new skills to real-world projects. I learned how to build a portfolio of data projects that showcase my proficiency to employers.

skills I gained

• Python Programming
• Machine Learning
• Data Analysis
• Algorithms
• Data Management
• Data Visualization
• Deep Learning
• SQL
• Statistical Programming
• General Statistics

In this course, i learned how to analyze and solve competitively such predictive modelling tasks. I understood how to solve predictive modelling competitions efficiently and learned which of the skills obtained can be applicable to real-world tasks. I learned how to preprocess the data and generate new features from various sources such as text and images. I learned advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve my predictions. I became be able to form reliable cross validation methodologies that help me benchmark my solutions and avoid overfitting or underfitting when tested with unobserved (test) data. I gained experience of analysing and interpreting the data. I became aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and I learned how to overcome them. I acquired knowledge of different algorithms and learned how to efficiently tune their hyperparameters and achieve top performance. I mastered the art of combining different machine learning models and learn how to ensemble. I got exposed to past (winning) solutions and codes and learned how to read them. This course taught me how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them.

skills I gained

• Competition Mechanics
• Machine Learning Algorithms
• Feature preprocessing, Extraction and Generation with respect to models
• Statistics
• Validation
• Data leakages
• Metrics optimization
• Hyperparameters tuning
• Ensembling
• LightGBM, XGBoost, and CatBoost models

In this course, I learned the language of Computer Science, the math that defines computer science, and practice applying it through mathematical proofs and Python code

skills I gained

• Random Variable
• Probability Interpretations
• Probability
• Combinatorics