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Artificial Intelligence (AI) and machine learning (ML) are related concepts but have distinct meanings.
Artificial Intelligence:
Artificial Intelligence refers to the broader field of creating intelligent machines or systems that can perform tasks that typically require human intelligence. AI involves developing algorithms and systems that can perceive their environment, reason, learn, and make decisions. It aims to replicate or simulate human intelligence in machines.
Machine Learning:
Machine Learning is a subset or application of AI. It involves developing algorithms that allow computers to learn and improve from data without being explicitly programmed. Instead of being explicitly programmed for specific tasks, machine learning algorithms learn from patterns and examples in the data. They automatically identify and learn from patterns, make predictions, or take actions based on the data they are trained on.
In simpler terms, AI is the broader concept that encompasses the idea of creating intelligent machines, while machine learning is a specific approach or technique within AI that focuses on enabling machines to learn from data and improve their performance over time.
To summarize:
AI is the overarching field that aims to develop intelligent machines. Machine learning is a subset of AI that focuses on algorithms and techniques that allow machines to learn from data and improve their performance. Machine learning is one of the ways AI systems can be created, but there are also other approaches like rule-based systems, expert systems, and deep learning, which is a subfield of machine learning.
Becoming a Data Scientist usually requires a combination of education, practical experience and certain skills. Here are the steps that can help you start on the path to becoming a Data Scientist:
Education: Most Data Scientists have a Bachelor's or Master's degree in a related field, such as computer science, statistics, mathematics, engineering or data science. A solid academic background provides the foundation for understanding data analysis and modelling.
Programming skills: Data Scientists typically need to know how to program in order to collect and clean data and develop models. The programming languages most commonly used in data science are Python and R. It is advisable to be proficient in these languages.
Statistics and Mathematics: A solid understanding of statistics and mathematics is essential to analyse data, identify patterns and build statistical models. Knowledge of areas such as probability, linear algebra and inferential statistics is an advantage.
Database skills: Data Scientists must be able to extract and manage data from various sources. Knowledge of databases and SQL (Structured Query Language) is therefore important.
Machine learning and artificial intelligence: Data scientists use machine learning and artificial intelligence to make predictions and build models. Knowledge of ML frameworks such as TensorFlow or scikit-learn is helpful.
Data visualisation: The ability to visually represent data is important to present complex information in an understandable way. Here you can use tools such as Matplotlib, Seaborn or Tableau.
Domain knowledge: Depending on the industry, it may be beneficial to have expertise in a specific area you want to work in as a Data Scientist. For example, healthcare, finance or marketing.
Practical experience: Practical experience is crucial. You can work on real-world projects, participate in competitions, contribute to open source projects or do an internship at a company to develop your data science skills.
Continuing education: The world of data science is constantly evolving. It is important to continuously educate yourself to stay up to date and understand new technologies and trends.
Networking: Networking is important in data science. Join online communities and social networks, attend conferences and meet professionals in your field to expand your knowledge and career opportunities.
Applications and career development: Create an impressive portfolio of your projects and skills to apply to potential employers or clients. Plan your career goals and development to take advantage of the best opportunities for your growth as a Data Scientist.
It is important to note that the path to becoming a Data Scientist can vary depending on individual prerequisites and interests. Some Data Scientists have a strong academic background, while others are self-taught. Practice and applying your skills in a practical way are crucial to your success as a Data Scientist.
In statistics, the term "outlier" or "outlier" denotes a data point that differs significantly from other data points in a data set. Outliers can occur either due to measurement error or due to an actual extraordinary phenomenon. They can potentially have a significant impact on statistical analysis as they can greatly affect the calculated averages and other metrics.
Detecting outliers is an important step in data analysis. There are several methods to identify outliers. Here are some common approaches:
Visual Methods: Charts such as scatterplots or boxplots can be used to identify potential outliers. Data points that are far from the general distribution of the data can be considered outliers.
Statistical Methods: There are several statistical tests that can identify outliers. A commonly used approach is the z-score method, which measures the distance of a data point from the mean of the data in standard deviations. Data points that have a z-score above a certain threshold can be considered outliers.
Robust Estimators: Robust estimation techniques such as median and interquartile range (IQR) can help identify outliers. Data points falling outside the range of 1.5 times the IQR from the quartiles can be considered outliers.
Machine Learning: Advanced machine learning algorithms can be used to detect outliers by identifying patterns and anomalies in the data. An example of this is the clustering method, in which outliers are regarded as data points that cannot be assigned to a specific group or cluster.
It is important to note that not every outlier is necessarily erroneous or needs to be removed. Sometimes outliers contain important information or can indicate interesting phenomena. The decision on how to deal with outliers depends on the specific analysis and context.
Google News is a news aggregator and online news platform operated by Google. It is designed to present users with personalised news and articles based on their interests and preferences. Google News offers a wide range of news sources and topics, including national and international news, politics, business, technology, health, sports, entertainment and more. Here are some key features and functions of Google News:
Personalisation: Google News uses machine learning and algorithms to customise news and articles based on a user's interests and reading habits. As a result, users receive news that is relevant to them.
News sources: Google News aggregates news from a wide range of news sources, including leading news organisations, newspapers, magazines, blogs and trade publications. This allows users to get a variety of perspectives.
Topics and headlines: The platform displays headlines and brief summaries of news articles, allowing users to quickly get an overview of current events.
Personalised feeds: Users can create personalised news feeds that match their interests by subscribing to specific topics or sources.
Regional News: Google News also provides regional news and local coverage tailored to the user's location.
Fact-checking: Google News also includes links to fact-checking sources and articles to help curb the spread of misinformation.
Multimedia content: In addition to text news, users can also find photos, videos and podcasts on a variety of topics.
Mobile apps: Google News is available through mobile apps for Android and iOS devices, as well as through the website.
Google News has evolved and improved over the years to meet the needs of its users. It is a popular platform for news consumption and offers users the opportunity to stay informed about current events and developments.
Machine learning is a branch of artificial intelligence that allows computers to learn from experience and complete tasks without being explicitly programmed. Machine learning is based on the idea that computers can develop algorithms that recognize patterns and structures in data and learn from them.
The machine learning process typically consists of the following steps:
Data collection:
First, data is collected that is relevant to the task being solved. This data can come from a variety of sources, such as sensors, databases, or the Internet.
Data cleaning and preparation: the collected data is prepared to ensure that it is of high quality and appropriate for the model. This may include tasks such as removing erroneous data, normalizing values, or converting data to an appropriate format.
Feature extraction: in this step, relevant features are extracted from the prepared data. This step is important to reduce the dimensionality of the data and capture the relevant information that is important for learning.
Modeling: This is where a machine learning model is created and trained on the prepared data. There are different types of learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, which can be applied depending on the nature of the task and the available data.
Model training: the model is trained on the prepared data by applying it to the data and adjusting its internal parameters to match the patterns in the data. During the training experiment, the model optimizes its parameters to achieve the desired results.
Evaluation and fine-tuning: after training, the model is evaluated on test data to assess its performance. If the model does not produce the desired results, it can be adjusted and trained again to improve performance. This step can be performed iteratively until the desired level of performance is achieved.
Prediction or decision making: After the model has been trained and evaluated, it can be used to make predictions or decisions when exposed to new data that it did not see during training.
These are the basic steps of machine learning. However, depending on the specific task and the available data, additional steps or techniques may be required.