This repository contains guidance related to SageMaker AI Projects. SageMaker Projects help organizations set up and standardize developer environments for data scientists and CI/CD systems for MLOps engineers.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

Repository of OpenClassrooms' AI Engineer path, project #9 : create a books recommandation system, integrate and deploy it as a mobile app

Documenting my journey toward becoming a research engineer. Exploratory research lab for building and documenting foundational CS, security, AI, and systems concepts — one reproducible project, paper-inspired idea, and reflection at a time.

A self-taught AI/ML Engineer and Data Scientist showcase of 30+ production-ready and research projects in Machine Learning, Deep Learning, NLP, and MLOps. Built end-to-end pipelines, real-time systems, and deployable apps using tools like Docker, Kubernetes, Airflow, MLflow, TensorFlow, PyTorch, Hugging Face, and cloud platforms (GCP, AWS).

This report focuses on the aspects of data science and machine learning to feature engineer a dataset to develop a predictive model the can be implemented into a Clinical Decision Support System(CDSS). It will outline typical workflows and methodologies of analysis and development of classification models using a common data science stack. A further reason for attempting this project is that it could provide insight into how data science and AI can give a much-needed boost to efficiency in diagnostics. Furthermore, finding out how these systems might be diffused or are diffusing into society will be part of the success of the final system.

This repository houses the development of a cutting-edge AI-powered Text-to-Speech (TTS) model tailored for the Hausa language. The project aims to create a state-of-the-art TTS system through iterative development, rigorous testing, and collaboration among machine learning engineers, data scientists, linguists, and software developers.

An open source, end-to-end AI engineering curriculum—blending hands-on projects with deep theory. Covers ML, deep learning, data science & full-stack AI system design. Built for future engineers, researchers & innovators.

This repository features 100+ Arduino & OpenCV projects that combine embedded systems and computer vision. From basic image processing to advanced robotics, each project includes hardware setup, Arduino code, and OpenCV scripts. Perfect for makers and engineers wanting to build real-world AI and automation systems.

🚀 A powerful Personal AI Engineer CLI for Ubuntu. Scaffold production-ready projects (NLP, CV, ML), inspect datasets, diagnose system health (CUDA/GPU), and generate AI project roadmaps instantly.

My first project as an AI Engineer Trainee, exploring the Langchain framework to build a Q&A system based on a paper.

Hritick Jha, a passionate Software Engineer with expertise in Backend Development and interests in AI, Automation, DevOps, Web Security, and Software Development, actively exploring DevOps and skilled in Project Management and System Architecture.

A backend project built using Java and Spring Boot as part of my journey to become a production-level backend engineer. This project evolves from basic REST APIs to a scalable, secure, AI-enabled system following real-world software engineering principles.

Autonomous multi-agent AI development system built with AutoGen. Simulates a collaborative software team with role-based LLM agents (Admin, Project Manager, Engineer) capable of planning, coding, and executing features from user input or functional specs.

AI-powered real-time PCB component identification with a modern, interactive web interface This project is an end-to-end PCB component detection system built using YOLOv8 (Ultralytics) and deployed through a modern Streamlit web application. It enables engineers, researchers, and students to automatically detect components on circuit boards

This is a personal portfolio website crafted to reflect my journey as a Machine Learning Engineer. It showcases my skills, services, experiences, and featured projects — from AI-powered solutions like face sentiment analysis to real-time face detection systems. Built using HTML, CSS, and JavaScript, the site is fully responsive and user-friendly.

Python is point of fact the Next Big Thing to investigate. There is no need to be worried about its worth, profession possibilities, or accessible positions. Python's commitment to the advancement of your calling is huge, as its notoriety among designers and different areas is step by step waning. Python is "the one" for an assortment of reasons. It's a straightforward pre-arranged language that is not difficult to get. Subsequently, the general improvement time for the task code is diminished. It accompanies an assortment of structures and APIs that assistance with information examination, perception, and control. Employment opportunities in Python While India has a critical interest for Python engineers, the stock is very restricted. We'll utilize a HR master articulation to validate this. For both Java and Python, the expert was relied upon to employ ten developers. For Java, they got over 100 fantastic resumes, however just eight for Python. In this way, while they needed to go through an extensive method to get rid of resilient people, they had no real option except to acknowledge those eight individuals with Python. What does this say about the circumstance to you? Regardless of Python's straightforward language structure, we desperately need more individuals in India to update their abilities. This is the reason learning Python is a particularly colossal opportunity for Indians. With regards to work openings, there may not be numerous for Python in India. Notwithstanding, we have countless assignments accessible per Python developer. In the relatively recent past, one of India's unicorn programming organizations was stood up to with an issue. It had gotten a $200 million (Rs. 1200 crore) arrangement to develop an application store for a significant US bank. Be that as it may, the organization required talented Python developers. Since Python was the best language for the undertaking, it wound up paying a gathering of independent Python developers in the United States multiple times the charging sum. For sure and Naukri, for instance, have 20,000 to 50,000 Python work postings, showing that Python vocation openings in India are copious. It is an insightful choice to seek after a profession in Python. The diagrams underneath show the absolute number of occupation advertisements for the most well known programming dialects. Python Job Descriptions Anyway, what sorts of work would you be able to get in the event that you know Python? Python's degree is broad in information science and investigation, first off. Customers regularly demand that secret examples be separated from their informational indexes. In AI and man-made reasoning, it is additionally suggested. Python is a top choice among information researchers. Furthermore, we figured out how Python is used in web advancement, work area applications, information examination, and organization programming in our article on Python applications. Python Job Profiles With Python on your resume, you might wind up with one of the accompanying situations in a presumed organization: 1. Programmer Investigate client necessities Compose and test code Compose functional documentation Counsel customers and work intimately with other staff Foster existing projects 2. Senior Software Engineer Foster excellent programming engineering Mechanize assignments by means of prearranging and different apparatuses Survey and troubleshoot code Perform approval and confirmation testing Carry out form control and configuration designs 3. DevOps Engineer Send refreshes and fixes Break down and resolve specialized issues Plan systems for support and investigating Foster contents to mechanize representation Convey Level 2 specialized help 4. Information Scientist Recognize information sources and mechanize the assortment Preprocess information and dissect it to find patterns Plan prescient models and ML calculations Perform information representation Propose answers for business challenges 5. Senior Data Scientist Manage junior information experts Construct logical devices to create knowledge, find designs, and foresee conduct Execute ML and measurements based calculations Propose thoughts for utilizing had information Impart discoveries to colleagues While many significant firms are as yet utilizing Java, Python is a more seasoned yet at the same time well known innovation. Python's future is splendid, on account of: 1.Artificial Intelligence (AI): Machine knowledge is alluded to as man-made consciousness. This is as a conspicuous difference to the regular astuteness that people and different creatures have. It is one of the most up to date advances that is clearing the globe. With regards to AI, Python is one of the main dialects that rings a bell; truth be told, it is probably the most ideally equipped language for the work. We have different structures, libraries, and devices devoted to permitting AI to swap human work for this objective. It supports this, however it additionally further develops productivity and precision. Discourse acknowledgment frameworks, self-driving vehicles, and other AI-based advancements are models. The accompanying devices and libraries transport for these parts of AI: AI – PyML, PyBrain, scikit-learn, MDP Toolkit, GraphLab Create, MIPy General AI – pyDatalog, AIMA, EasyAI, SimpleAI Neural Networks – PyAnn, pyrenn, ffnet, neurolab Normal Language and Text Processing – Quepy, NLTK, genism 2. Enormous Data Enormous Data is the term for informational collections so voluminous and complex that conventional information handling application programming is insufficient in managing them. Python has assisted Big Data with developing, its libraries permit us to break down and work with a lot of information across groups: Pandas scikit-learn NumPy SciPy GraphLab Create IPython Bokeh Agate PySpark Dask 3. Systems administration Python additionally allows us to design switches and switches, and perform other organization mechanization undertakings cost-viably. For this, we have the accompanying Python libraries: Ansible Netmiko NAPALM(Network Automation and Programmability Abstraction Layer with Multivendor Support) Pyeapi JunosPyEZ PySNM Paramiko SSH Python Course

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Evidence-based roadmap to becoming an AI System Engineer. Mathematical foundations, ML systems, production habits, and proof-backed progression.

Professional AI Engineer portfolio: ML systems, MLOps, deployment, data engineering, projects, skills, contact, collaboration.

Cybersecurity Engineer portfolio. Showcasing AI/ML security research, IoT defense systems, and other projects.

This repo tracks my journey ➝ AI Engineer. It contains hands-on projects across deep learning, generative AI, and AI systems.

Founder & CEO of Qwerify | SWE | Aspiring AI Engineer | Building Scalable Systems, Technology & AI Projects that Help People Without Distraction

A Series like project of Essential Software Engineering Concepts for a Machine Learning Engineer or/and Data Scientist implementing AI systems.

🤖 Complete roadmap to become an AI Systems Engineer — 7 phases, 35+ problems/topic, 6 mini projects & 1 major project. From foundations to production. ⭐ Star to save!

This repository contains a collection of small, focused mini projects designed to build intuition and hands-on experience for an AI Systems Engineer (SDE-level) role.

EE ZONE is a next-generation ultimate electrical and electronics engineering platform combining AI tools, circuit simulation, power system calculators, and project management features to help engineers design, analyze, and troubleshoot electrical systems efficiently.

Imagine if your household had a full-time software engineer, dedicated solely to you. This project builds that engineer, an agentic AI system capable of designing, coding, testing, and deploying bespoke software tools on demand, tailored to a single household’s unique needs.

AiChatPoweredEcommerce: From Flatiron School capstone to AI-driven e-commerce innovation. A project blending NLP and machine learning for a unique shopping experience, crafted by a senior systems engineer turned software developer.

An AI-powered billing and quantity-management system for civil engineering projects, featuring automated RA bill generation, digital measurement extraction, BOQ structuring, and workflow tools designed for contractors, QS teams, and billing engineers.