PYTHON + DATA SCIENCE + MACHINE LEARNING AND DEEP LEARNING

Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two concepts you’ve likely heard of before: machine learning and deep learning. These terms are often thrown around in ways that can make them seem like interchangeable buzzwords, hence why it’s important to understand the differences.

What is machine learning?

Here’s a basic definition of machine learning:

“Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions”.

Machine learning fuels all sorts of automated tasks and spans across multiple industries, from data security firms hunting down malware to finance professionals looking out for favourable trades. They’re designed to work like virtual personal assistants, and they work quite well.

Machine learning is a lot of complex math and coding that, at the end of the day, serves a mechanical function. the same way as a flashlight, a car, or a television does. When something is capable of “machine learning”. it means it’s performing a function with the data given to it and gets progressively better at that function. It’s like if you had a flashlight that turned on whenever you said: “it’s dark”, so it would recognize different phrases containing the word “dark”.

Now, the way machines can learn new tricks gets really interesting (and exciting) when we start talking about deep learning.

 

How does deep learning work?

A deep learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, deep learning uses a layered structure of algorithms called an artificial neural network (ANN). The design of an ANN is inspired by the biological neural network of the human brain. This makes for machine intelligence that’s far more capable than that of standard machine learning models.

A great example of deep learning is Google’s AlphaGo. Google created a computer program that learned to play the abstract board game called Go. a game is known for requiring sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned how to play at a level not seen before in artificial intelligence, and all without being told when it should be made a specific move (as it would with a standard machine learning model). It caused quite a stir when AlphaGo defeated multiple world-renowned “masters” of the game; not only could a machine grasp the complex and abstract aspects of the game, it was becoming one of the greatest players of it as well.

Deep learning vs machine learning

In practical terms, deep learning is just a subset of machine learning. It technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged), but its capabilities are different.

Basic machine learning models do become progressively better at whatever their function is, but they still some guidance. If an ML algorithm returns an inaccurate prediction, then an engineer needs to step in and make adjustments. But with a deep learning model, the algorithms are capable of determining on their own if the prediction is accurate or not.

Let’s go back to the flashlight example: it could be programmed to turn on when it recognizes the audible cue of someone saying the word “dark”. Eventually, it could pick up any phrase containing that word. Now if the flashlight had a deep learning model, it could maybe figure out that it should turn on with the cues “I can’t see” or “the light switch won’t work”. A deep learning model is able to learn through its own method of computing – its own “brain”, if you will

PREREQUISITE

Basic knowledge of programming in Python Language is mandatory If candidates fail to meet this prerequisite, then one day is required to make candidates familiar with basic concepts of programming

Code-Lipi Content of  Machine Learning and Deep Learning

   1. Machine Learning training in Faridabad

  • Data Pre Processing
  • Pandas, Numpy, Matplotlib libraries
  •  Data Visualization – Scatter and Area Plots
  •  Machine Learning Algorithms and Prediction Models
  •  Regression
  •  Classification
  •  Clustering
  •  Association Rule learning
  •  Reinforcement Learning
  •  Natural Language Processing
  •  Dimensionality Reduction
  •  Model Selection and Boosting

 

2. Deep Learning Course Detail

  • Artificial Neural Networks
  •  Convolution Neural Networks
  •  Recurrent Neural Networks
  •  Self-Organizing Maps
  •  Boltzmann Machines
  •  Auto-Encoders

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