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Machine Learning with Python Training in Hyderabad

Python With Machine Learning

Python With Machine Learning


Duration : 45 days ((Online & Class room)

ML | What is Machine Learning ?

Arthur Samuel, a pioneer in the field of artificial intelligence and computer gaming, coined the term “Machine Learning”. He defined machine learning as – “Field of study that gives computers the capability to learn without being explicitly programmed”.

In a very layman manner, Machine Learning(ML) can be explained as automating and improving the learning process of computers based on their experiences without being actually programmed i.e. without any human assistance. The process starts with feeding good quality data and then training our machines(computers) by building machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

Introduction To Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of Computer Programs that can change when exposed to new data. In this article, we’ll see basics of Machine Learning, and implementation of a simple machine learning algorithm using python.

Best Python libraries for Machine Learning

Machine Learning, as the name suggests, is the science of programming a computer by which they are able to learn from different kinds of data. A more general definition given by Arthur Samuel is - "Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. They are typically used to solve various types of life problems.

In the older days, people used to perform Machine Learning tasks by manually coding all the algorithms and mathematical and statistical formula. This made the process time consuming, tedious and inefficient. But in the modern days, it is become very much easy and efficient compared to the olden days by various python libraries, frameworks, and modules. Today, Python is one of the most popular programming languages for this task and it has replaced many languages in the industry, one of the reason is its vast collection of libraries. Python libraries that used in Machine Learning are:

  • Numpy
  • Scipy
  • Scikit-learn
  • Theano
  • TensorFlow
  • Keras
  • PyTorch
  • Pandas
  • Matplotlib


Data science is a collection of research-based methods and processes often with difficulty insights from data. machine learning in data science is a activity will become ever more important as the amount of data available continues to increase, and the challenge of extracting discernment from the data follows.

This observation defines of the difference among these three fields:

  • Data science produces insights.
  • Machine learning produces predictions.
  • Artificial intelligence produces actions.

The Components Of Data Science

Data science contents like machine learning, R, python and Deep learning..etc.. is a combination of mathematics, programming, problem-solving, and data capturing in "inventive ways". It is also the ability to find patterns, along with cleaning, preparing, and aligning data.

Data science is a field that encompasses anything related to data cleansing, preparation, and analysis. It is an umbrella term for techniques used when trying to extract insights and information from data.

Artificial Intelligence(AI)

An artificial intelligence (AI) tools to find an accurate and deep understanding that they are looking for.

The fabulous data science as an occupation is that it does not necessarily need a degree to get into the field, Skills in maths , statistics or operations research, business or many others, can be leveraged as long as they are supported by a base knowledge of mathematics and programming.

This role to play as data and AI evolve, and its complexities multiply.

The increasing intelligence of AI has a lot to do with how neural networks are being applied within the field.

Neural Network

A neural network is either a system software or hardware that works similar to the tasks performed by neurons of human brain. Neural networks include various technologies like deep learning, and machine learning as a part of Artificial Intelligence (AI).

Proposed three properties

Most professionals in these fields have been classified as data science, machine learning, or artificial intelligence, even if these are very hard tie together. But they're not interchangeable , The fields do have a great deal of overlap, and there's enough promotion around each of them that the possibility can feel like a matter of marketing.

Data Science Produces Insights

Data science is prominent from the other two fields because its agenda is to gain insight and understanding. It is descriptive , exploratory and causal, not everything that produces insights qualifies as data science. Traditionally data science involves a combination of statistics, software engineering, and domain expertise.

  • Communication
  • Data visualization
  • Domain knowledge
  • Experiment design
  • Statistical inference

Data scientists might use simple tools on SQL queries. They could also use very complex methods. They might work with distributed data stores to illustrates of records, developing most advanced statistical techniques, and build interactive with set of information as a chart or other image. Whatever they use, the hope strongly to achieve to gain a better understanding of their data.

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