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Artificial Intelligence

What is Artificial Intelligence?

The definition of artificial intelligence has also modified over the years, with common tasks, like optical character recognition not being considered as AI any longer. this is often because of a development called the “AI effect”. This development states that “AI is something that has not been done however.”

With the increase of accessible computing power, AI solutions are being adopted by enterprises at scale. AI is additionally a good answer for several issues that previously fell below “business intelligence”. AI is additionally very helpful in an exceedingly field called predictive analytics. depending on the information acquired within the past, predictive analytics permits for an informed guess of the longer term. The technology stands an opportunity to supply valuable insights into company processes.

AI very helpful in predictive analytics. depending on the information acquired within the past, predictive analytics permits for an informed gu

Goals of AI:

Creating professional systems:

The systems that are mentioned here ought to have the power to show intelligent behaviour, learn, demonstrate, explain, and supply the users with the simplest items of advice.

Implementing Intelligence in machines:

This intends to develop systems which may understand, think, learn, and behave like humans.

What is AI Technique?

Some of the weird properties of data within the universe are

  • the amount is big, on the far side out of the question
  • Not in an organized or correct format
  • It often undergoes changing

The AI technique may be a method to format and uses the data effectively

  • The data ought to be understandable
  • should be simply labile to correct errors
  • is used with efficiency even if it’s incomplete

Advantages of Artificial Intelligence:

  1. Error Reduction:

Artificial intelligence helps us in reducing the error and therefore the chance of reaching accuracy with a larger degree of precision. it’s applied in numerous studies like exploration of space.

Intelligent robots are fed with data and are sent to explore house. Since they’re machines with metal bodies, they’re a lot of resistant and have a larger ability to endure the house and hostile atmosphere.

They are created and acclimatized in such how that they can not be changed or get ugly or breakdown in a hostile atmosphere.

  1. difficult Exploration:

Artificial intelligence and therefore the science of AI is place to use in mining and different fuel exploration processes. Not solely that, these complicated machines is used for exploring the sea bottom and thence overcome the human limitations.

Due to the programming of the robots, they’ll perform more heavy and hard work with larger responsibility. Moreover, they are doing not wear out simply.

  1. Daily Application:

Computed ways for automatic reasoning, learning and perception became a typical development in our everyday lives. we’ve our woman Siri or Cortana to help us out.

We are touch the road for long drives and visits with the assistance of GPS. The smartphone is an apt and everyday example of however we tend to use artificial intelligence. In utilities, we discover that they’ll predict what we tend to are about to sort and proper the human errors in writing system. that’s machine intelligence at work.

When we take an image, the substitute intelligence formula identifies and detects the person’s face and tags the people once we are posting our pictures on social media sites.

Artificial Intelligence is wide used by money establishments and banking institutions to prepare and manage information. Detection of fraud uses artificial intelligence in a smart card based system.

  1. Digital Assistants:

Highly advanced organizations use ‘avatars’ that are replicas or digital assistants who will truly move with the users, so saving the necessity for human resources.

For artificial thinkers, emotions are available the approach of rational thinking and aren’t a distraction at all. the entire absence of the emotional facet, makes the robots suppose logically and take the correct program choices.

Emotions are related to moods which will cloud judgment and affect human efficiency. this is often completely ruled out for machine intelligence.

  1. Repetitive Jobs:

Repetitive jobs that are monotonous in nature is carried out with the help of machine intelligence. Machines suppose quicker than humans and might be place to multi-tasking. Machine intelligence is used to hold out dangerous tasks. Their parameters, in contrast to humans, is adjusted. Their speed and time are calculation based mostly parameters only.

When humans play a video game or run a computer-controlled mechanism, we tend to are actually interacting with artificial intelligence. within the game we tend to are taking part in, the pc is our opponent. The machine intelligence plans the sport movement in response to our movements. we are able to consider gaming to be the most common use of the advantages of artificial intelligence.

  1. Medical Applications:

In the medical field also, we are going to realize the wide application of AI. Doctors assess the patients and their health risks with the help of artificial machine intelligence. It educates them concerning the facet effects of various medicines.

Medical professionals are usually trained with artificial surgery simulators. It finds an enormous application in detection and observation neurologic disorders because it will simulate the brain functions.

Robotics is used usually in serving to mental health patients to return out of depression and stay active. a preferred application of AI is radiosurgery. Radiosurgery is employed in operating tumours and this will truly facilitate within the operation while not damaging the encompassing tissues.

  1. No Breaks:

Machines, unlike humans, don’t need frequent breaks and refreshments. they’re programmed for long hours and might continuously perform while not getting bored or distracted or maybe tired.

ML and Artificial Intelligence Course Curriculum

Introduction to Data Science

  • Data Science Overview
  • Introduction to Python
  • Understanding Operators
  • Variables and Data Types
  • Conditional Statements
  • Looping Constructs
  • Functions
  • Data Structure
  • Lists
  • Dictionaries
  • Exception Handling

Understanding Standard Libraries in Python for Data Analytics

Python for Data Analysis- Numpy

  • Introduction to Numpy
  • Numpy Arrays
  • Quick Note on Array Indexing
  • Numpy Array Indexing
  • Numpy Operation
  • Numpy Exercises

Python for Data Visualization- Matplotlib

  • Introduction of Matplotlib
  • Matplotlib Part 1
  • Matplotlib Part 2
  • MatplotlibExcercises

Python for Data Analysis – Pandas

  • Introduction to Pandas
  • Series
  • Dataframes – Part 1
  • Dataframes – Part 2
  • Missing Data
  • Groupby
  • Merging joining and Concatenating
  • Operations
  • Data Input amd output
  • Python for Data Analysis- Pandas ExercisesProjects

Machine Learning Contents-Using Scikit Learn

  1. Python and Machine Learning
  2. Introduction to Machine Learning With Python
  3. Tasks in Machine Learning Using Python
  4. Supervised Learning
  5. Unsupervised Learning
  6. Steps in Python Machine Learning 
  7. Applications of Python Machine Learning
  8. Companies Using Python Machine Learning

 

Deep Learning Using – TensorFlow

  •  Lesson 1: Introduction to Deep Learning Topics:
  • Define Deep Learning
  • Neural Networks
  • Deep Learning Applications

  Lesson – 2: Perceptron

  • What is a Perceptron
  • Logic Gates with Perceptrons
  • Activation Functions
  • Sigmoid
  •  ReLU
  • Softmax

Lesson 3: How to train ANNs

  •  Introduction
  •  Perceptron Learning Rule
  • Gradient Descent Rule
  •  Minimize Cost Function
  • Tuning Learning Rate
  • Stochastic vs Batch Gradient Descent
  • Lesson 4: Multi-layer ANN
  • Intro to MLP
  • Forward propagation
  • Minimize Cost Function
  • Backpropagation
  • Convergence in a neural net
  • Overfitting and Capacity
  • Hyperparameters in an ANN
  • Lesson 5: Introduction to TensorFlow
  • Intro to TensorFlow
  • Computational Graph
  • Key highlights
  • Creating a Graph
  • Regression example
  • Gradient Descent
  •  Keras-based networks
  • TensorBoard Lesson
  • 6: Training Deep Neural Nets
  • Vanishing/Exploding Gradient
  • Batch Normalization
  • Unsupervised Pre-training
  • Optimizers
  •  Regularization
  •  7: Convolutional Neural Networks
  • Intro to CNNs
  • Convolution Operation
  • Kernel filter
  • Feature Maps
  • Pooling
  • CNN Architecture
  • Implement CNN in TensorFlow
  • Lesson 8: Recurrent Neural Networks
  • Intro to RNNs
  •  Unfolded RNNs
  •  Basic RNN Cell
  • Dynamic RNN
  • Training RNNs
  • Time-series predictions
  • LSTM
  • Word Embeddings
  • Seq2Seq Models
  • Implement RNN in TensorFlow
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