I will do data science, data mining, machine learning
I will do data science, data mining, machine learning
Data science is a field that studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods
Get I will do data science, data mining, machine learning
Data science is a field that studies data and how to extract meaning from it. It involves the use of statistical and computational methods to analyze and interpret complex data sets.
Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
Machine learning is a branch of artificial intelligence that involves the development of algorithms that can learn from data and make predictions or decisions based on that data123.
In summary, data science is about understanding data, data mining is about discovering patterns in data, and machine learning is about building models that can learn from data and make predictions or decisions based on that data.
Data mining has been around since the 1930s; machine learning appears in the 1950s. Data mining is designed to extract the rules from large quantities of data, while machine learning teaches a computer how to learn and comprehend the given parameters.
Machine learning has many applications in business, including risk analysis, fraud detection, predictive maintenance, and recommender systems1.
It is also used in healthcare for medical diagnosis and drug discovery2.
In addition, machine learning is used in image and speech recognition, natural language processing, robotics, self-driving cars and many more1.
There are many popular tools used in machine learning. Some of the most popular ones include:
- Python: Python is a popular programming language used for machine learning. It has many libraries and frameworks that make it easy to work with machine learning algorithms.
- TensorFlow: TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks.
- Scikit-learn: Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN.
- Keras: Keras is an open-source software library for neural networks written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML.
- PyTorch: PyTorch is an open-source machine learning library based on the Torch library. It is used for applications such as natural language processing and computer vision.
There are many popular tools used in data science. Some of the most popular ones include:
- Python: Python is a popular programming language used for data science. It has many libraries and frameworks that make it easy to work with data.
- R: R is a programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and data analysis.
- Apache Spark: Apache Spark is an open-source distributed computing system used for big data processing. It is designed to be fast and general-purpose.
- Tableau: Tableau is a business intelligence and data visualization tool that allows users to connect, visualize, and share data in a way that’s easy to understand.
- Excel: Excel is a spreadsheet program that can be used for data analysis and visualization.
I already answered that question earlier. Here are the tools I mentioned:
- Python
- TensorFlow
- Scikit-learn
- Keras
- PyTorch
Basic : $15
Standard : $25
Premium : $35
Basic Data Analysis, Basic Data Visualization, Basic Overview of Data, Basic Stats of Data
