Data Science |
Data Science |
| Data Sciences. What is Data Science? It's a general field related with data, algorithms and AI. |
Data ScienceData science uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data as one of the hottest professions in the market today. |
Algorithms |
Linear RegressionLinear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between dependent and independent variables they are considering, and the number of independent variables getting used. |
| Exploratory Data Analysis: Performed initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations. |
| Data Visualization: Using data visualization, I summarized the data with graphs, pictures and maps, so that the human mind has an easier time processing and understanding the given data.
Data visualization plays a significant role in the representation of both small and large data sets, but it is especially useful when we have large data sets, in which it is impossible to see all of our data, let alone process and understand it manually. |
| Training and Testing: In this project, datasets are split into two subsets.
The first subset is known as the training data - it's a portion of our actual dataset that is fed into the machine learning model to discover and learn patterns. In this way, it trains our model. The other subset is known as the testing data. |
| Train and Evaluate Linear Regression:
Simple linear regression is an approach for predicting a quantitative response using a single feature (or "predictor" or "input variable"). It takes the following form: y=β0+β1x |
AI JobsKnowledge of machine learning is required in most jobs related with artificial intelligence and data sciences. Here are some examples of this jobs in the field of AI: Database developer, Database manager, Data architect, Data scientist, Big data engineer, AI architect, AI modeler and AI developer agaist other with high salaries in this industry. It is recommended to take AI and programming courses to get the required knowledge and experience in the field of machine learning and advance on the path to an exciting career of data sciences. Learn python and other programming languages such as that can help with building models and web applications. To become a machine learning engineer you can take AI related courses with data sciences, for example training in a boot camp, or taking a complete bachelor’s degree in computer and related subjects. To study you can take online courses or in-person, while the machine learning engineer jobs can require some knowledge of computer sciences, software engineering, advanced math skills, statistics, group theory, linear algebra or a related fields. The advanced students in machine learning learns more about data modeling, data architecture and AI frameworks used in real applications. |
| AI and Natural Language - Natural Questions Dataset used for training Large Language Models (LLM) with machine learning. |
AI IntroductionIntroduction to Artificial Intelligence - The history of AI in software and hardware 1950: Alan Turing Turing Test 1951: First AI program 1965: Eliza (first chat bot) 1974: First autonomous vehicle 1997: Deep Blue beats Gary Kasimov at Chess 2004: First Autonomous Vehicle challenge 2011: IBM Watson beats Jeopardy winners 2016: Deep Mind beats Go champion 2017: AlphaGo Zero beats Deep Mind |
AI and Data Sciences |
| AI from Google Learning with Natural Questions |
| Research Resources from Google |
| Natural Questions datasets |
| OpenAI |
| ChatGPT from OpenAI |
| Anaconda Freelearning - Get started with anaconda and create AI systems with python |
| OIG Dataset - The key motivation was to enable anyone to use OpenChatKit. Called the Open Instruction Generalist Dataset, the dataset contains more than 40 million examples of questions and answers, follow-up questions and more designed to “teach” a model how to respond to different instructions (like: Write an outline for a history paper on the Civil War). |