Machine Learning Using Python

Machine Learning Using Python

Machine Learning Using Python

Machine Learning Using Python

Machine Learning Using Python

DexLab Machine Learning Certified Machine Learning Course

54 Hours (each session for 3 hours), Weekend classes

Flexible Online Training

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Machine Learning is a “Field of study that gives computers the ability to learn without being explicitly programmed”. In this data driven age, machine learning is being used to compile and extract the massive volumes of data that are generated daily. Big shot multinational corporations are now using machine learning to glean real-time insights to enhance their business performance or gain an extra edge over their competitors.

 Our course at Dexlab Analytics will provide a broad introduction to machine learning, data-mining, and statistical pattern recognition. Our team of experienced faculties will teach you the end-to-end process of investigating data by implementing machine learning. This module has been created with Python keeping with the most in-demand skills within the data analysis scenario globally, to help the participants to be industry-ready. The simplicity, scalability and easy integration with Python for machine learning. According to a survey about 58% of the respondents preferred using Python over other data tools. The reason behind Python’s popularity is its dynamic nature which allows RDD’s to hold multiple type objects. MLlib which is Sparks’s machine learning library also uses API’s written in Python. This makes Python a true match made in heaven for machine learning.

The course will include the following topics:

  • (i) Supervised learning (parametric and non-parametric algorithms, Naive Bayes, decision trees, kernels, neural networks).
  • (ii) Unsupervised learning (clustering, principal component analysis, recommender systems, feature scaling, deep learning).
  • (iii) Best practices in machine learning (bias/variance theory; cross validation, F1 score innovation process in machine learning and AI).

In the course of recent decades Machine Learning has gotten to be one of the backbones of data innovation and with that, a fairly focal, yet ordinarily concealed, a portion of our life. With the steadily expanding amount of data getting accessible there is a fair reason to accept the fact that the analysis of data will be penetrating as a vital element for innovative advancement into technologies.

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