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Machine learning with python - concepts

Hey guys, once again bringing another technology trend on this blog for you all, but this time it’s something different. Rather than posting single article posts, I have made a free course on machine learning which will help you understand machine learning right from the beginning to advanced deep learning systems. You can find the full course under the label #machine learning in the menu. This is the 1st post of the course. Hope you enjoy this course and have meaningful knowledge about it.


Python is a very popular platform used for research and development of production systems. It is a very vast language with number of modules, packages and libraries. Packages and libraries provide multiple ways of achieving a task.

Python and its Libraries like Numpy, SciPy, Scikit-Learn, and Matplotlib are used in data science and data analysis. These libraries are also extensively used for creating scalable machine learning algorithms. Python implements machine learning techniques such as Classification, Regression, Recommendation, and Clustering.

Python offers ready-made framework for performing data mining tasks on large volumes of data effectively and in lesser time. It includes several implementation achieved through algorithms such as linear regression, logistic regression, Naïve Bayes, kmeans, K Nearest Neighbors, and Random Forest.

Python in Machine Learning:
Python has libraries that enable developers to use optimized algorithms, which further helps in model creation. It implements popular machine learning techniques such as recommendation, classification, and clustering, which is in itself a great advantage. Therefore, it is necessary to have a brief introduction to machine learning before we move further towards various algorithms.


What is Machine Learning?
Data science, machine learning and artificial intelligence are some of the top trending topics in the technology world today. Data mining and Bayesian analysis are trending topics in the present and this is adding the demand for machine learning. This tutorial article is your entry into the world of machine learning.

Machine learning is a discipline that deals with programming the systems so as to make them automatically learn and improve with experience, and is useful in creating models which can predict future outcomes. Here, learning implies recognizing and understanding the input data and taking decisions based on the supplied data using suitable machine learning algorithms. It is difficult to consider all the decisions based on all possible inputs. To resolve this drawback, algorithms are developed that build knowledge from a specific data and past experience by applying the principles of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory, thus resulting in predicting future outcomes and generating the best and yet efficient output.

Applications of Machine Learning Algorithms :
The advanced machine learning algorithms are used in various applications such as:
  • Vision processing
  • Language processing
  • Forecasting things like stock
  • Market trends, weather
  • Pattern recognition
  • Games
  • Data mining
  • Expert systems
  • Robotics
Steps Involved in Machine Learning :
A machine learning project involves the following steps that one should follow:
  • Defining a Problem
  • Preparing Data
  • Evaluating Algorithms
  • Improving Results
  • Presenting Results
The best way to get started using Python for machine learning or by using any other programming language is to work through a project and cover the key steps like loading data, summarizing data, evaluating algorithms and making some valuable predictions. This gives you a clonable method that can be used dataset after dataset. You can also add further data and improve the results (future outcomes).

So, this was all about what is machine learning and its applications in the present world. You can find out more by clicking the links below which will redirect you to the other parts of this course. See you right there…..
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