Bootcamp Data Analysis from Mintic_Talento Tech Colombia Additions add Additions will be made during the course period, after this and more contributions... Adiciones se harán durante, después del periodo del curso y mas aportes...
Análisis de datos y visualización from Mintic Talento Tech Boot camp online learning close in Bogota, Colombia. Additions are made during the course and after this course with more contributions... The sessions are in real time online asynchronous. During the course period and after it you can make contributions... The contributions that enrich mutual knowledge.
-
You should to know and you keep in your mind to differentiate the types of data analysis like it Descriptive Analysis, Exploratory Data Analysis (EDA), Predictive Analysis, Inferential Analysis, Diagnostic Analysis, Prescriptive Analysis, Text Analysis, Spatial Analysis.
-
The sequentially but generally follow a logical progression. Problem Definition, Data Collection, Data Cleaning and Preprocessing, Exploratory Data Analysis (EDA), Hypothesis Formulation, Inferential Analysis, Predictive Modeling, Diagnostic Analysis, Prescriptive Analysis.
-
It's important to note that these stages are iterative and may require revisiting previous steps as new insights are uncovered or additional data becomes available. Flexibility and adaptability are key in the data analysis process.
Creating machine learning algorithms, applied to deep learning and neural networks Relationship among linear algebra, probability and statistics, optimization, and deep learning. Graph theory, linear algebra, and databases to address problems associated with Big Data. Display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. How to take raw data, extract meaningful information, use statistical tools, and make visualizations. Signal processing approach to these problems by combining linear algebraic graph algorithms, group theory, and database design.
Artificial intelligence (AI) learns through various techniques, with the most common ones being:
Supervised Learning: In supervised learning, the AI model learns from labeled data, where the input data is paired with the corresponding correct output. The model learns to map input data to the correct output by minimizing the error between its predictions and the true labels. This approach is commonly used for tasks such as classification and regression.
Unsupervised Learning: In unsupervised learning, the AI model learns from unlabeled data without explicit guidance. The goal is to find hidden patterns or structures within the data. Clustering and dimensionality reduction are common tasks performed using unsupervised learning techniques.
Reinforcement Learning: Reinforcement learning involves an AI agent learning to interact with an environment to achieve a goal. The agent learns by trial and error, receiving feedback in the form of rewards or penalties based on its actions. Over time, the agent learns the optimal behavior to maximize its cumulative reward. Reinforcement learning is commonly used in applications such as game playing, robotics, and autonomous driving.
Self-Supervised Learning: Self-supervised learning is a form of supervised learning where the model generates its own labels from the input data without human intervention. For example, in natural language processing, a model might be trained to predict masked words in a sentence.
Transfer Learning: Transfer learning involves leveraging knowledge from a pre-trained model to perform a new task. Instead of training a model from scratch, a pre-trained model is fine-tuned on a new dataset related to the target task. Transfer learning is particularly useful when the new task has limited data or computational resources.
Neural Networks and Deep Learning: Neural networks, particularly deep neural networks, are a fundamental component of many AI learning algorithms. These networks consist of interconnected layers of artificial neurons that learn to represent complex patterns in the data through iterative optimization of their parameters.
AI learns by extracting patterns and relationships from data, either through supervised, unsupervised, or reinforcement learning techniques, often leveraging neural networks and deep learning architectures. The choice of learning approach depends on the nature of the task, the availability of labeled data, and the desired outcomes.
Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so.
Subset of Artificial Intelligence: Machine learning is described as a subset of artificial intelligence. This means that machine learning is a specialized area within the broader field of AI, focusing specifically on how computers can learn from data to perform tasks without being explicitly programmed.
Development of Algorithms and Models: Machine learning involves the creation and refinement of algorithms and models. These algorithms and models are designed to analyze data and extract patterns or relationships that can be used for making predictions or decisions.
Learning from Data: The core idea of machine learning is that computers can learn from data. Instead of being explicitly programmed with rules to follow, machine learning algorithms are trained on large amounts of data. By analyzing this data, the algorithms can identify patterns and trends that they use to improve their performance.
Making Predictions or Decisions: Once trained, machine learning models can make predictions or decisions based on new, unseen data. For example, a model trained on historical stock market data could predict future stock prices, or a model trained on medical images could diagnose diseases.
Machine learning is a specialized area within artificial intelligence that focuses on developing algorithms and models capable of learning from data to make predictions or decisions without explicit programming instructions.
Conclusions: "Data structures are essential building blocks in obtaining efficient algorithms for methods of application."
"Thinking structurally about decision problems in order to make informed management decisions."
Plus add: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.