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Create Modern Data Science, Machine Learning Projects

Create Modern Data Science, Machine Learning Projects

Solve business problems using machine learning, Learn data science and machine learning practically - Free Course.

  • Make robust Machine Learning models
  • Make powerful analysis
  • Master Machine Learning on Python
  • Have a great intuition of many Machine Learning models


  • Knowledge Of Machine Learning
make this happen, this Python project will require a deep learning model and libraries such as OpenCV, TensorFlow, Pygame, and Keras

  • Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2


Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence.

The focus of the field is learning, that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data.

As such, there are many different types of learning that you may encounter as a practitioner in the field of machine learning: from whole fields of study to specific techniques.

1. Supervised Learning

Supervised learning describes a class of problems that involves using a model to learn a mapping between input examples and the target variable.

Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.

Models are fit on training data comprised of inputs and outputs and used to make predictions on test sets where only the inputs are provided and the outputs from the model are compared to the withheld target variables and used to estimate the skill of the model.

Learning is a search through the space of possible hypotheses for one that will perform well, even on new examples beyond the training set. To measure the accuracy of a hypothesis we give it a test set of examples that are distinct from the training set.

There are two main types of supervised learning problems: they are classification that involves predicting a class label and regression that involves predicting a numerical value.

  • Classification: Supervised learning problem that involves predicting a class label.
  • Regression: Supervised learning problem that involves predicting a numerical label.

Both classification and regression problems may have one or more input variables and input variables may be any data type, such as numerical or categorical.

An example of a classification problem would be the MNIST handwritten digits dataset where the inputs are images of handwritten digits (pixel data) and the output is a class label for what digit the image represents (numbers 0 to 9).

There are many online courses about data science and machine learning that will guide you through a theory and provide you with some code examples

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