Presentation :

Slide 0:   Machine Learning 

Name Sourabh Agarwal 

baki tere ko pata hi hai kya likhna hai 

Slide-1  What is Machine Learning (ye haar page ke top pe likhna hai ) (jo jiss slide ka hai )(har pe topic likha hai slide ka )

 Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed

Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification,  regression etc

Slide 2 Categories in machine Learning

Supervised Learning 

Unsupervised Learning

Reinforecement Learning

Slide 3 Supervised Learning

Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

Slide 4 Supervised Learning 

Supervised Learning can further be classified into two categories :

• Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.
• Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

Linear Regression for regression problems

Random Forest for classification and regression problems

support vector machine for classification problems 

Slide 5:Supervised Learning

Slide 6 : Unsupervised Learning 

Unsupervised learning is where we only have input data (X) and no corresponding output variables.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

Slide 7: Unsupervised Learning 

Unsupervised learning problems can be further grouped into clustering and association problems.

• Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
• Association:  An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Some popular examples of unsupervised learning algorithms are:

• k-means for clustering problems.

• Slide 8:unsupervised Learning

Slide 9 : Supervised vs unsupervised learning (bro aage se number change kar lena slide ke )

Slide 9: Reinforcement Learning

Reinforcement learning is the training of machine learning models to make a sequence of decisions. 

I Works on the principle of feedback.

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Slide 10 :

 

Slide 11  Deep learning

Deep Learning  is a subset of Machine Learning in Artificial Intelligence that can imitate the data processing function of the human brain and create similar patterns the brain used for decision making. 

Deep Learning architectures like deep neural networks, belief networks, and recurrent neural networks, and convolutional neural networks have found applications in the field of computer vision, audio/speech recognition etc.

Slide 12: Neural Network 

 It is a structure consisting of ML algorithms wherein the artificial neurons make the core computational unit that focuses on uncovering the underlying patterns or connections within a dataset, just like the human brain does while decision making.

Slide 13: Architecture 

Slide 14: Architecture Explained

• Neurons – A neuron is a mathematical function designed to imitate the functioning of a biological neuron. 
• Connection and weights –  connections connect a neuron in one layer to another neuron in the same layer or another layer. A weight represents the strength of the connection between the units. The aim is to reduce the weight value to decrease the possibilities of loss (error).
• Propagation function – forward propagation that delivers the “predicted value” and backward propagation that delivers the “error value.”
• Learning rate – Neural Networks are trained using Gradient Descent to optimize the weights. 

Slide 15 Why Deep Learning .?

Slide 16 : References :

www.analyticsvidhya.com/

www.towardsdatascience.com/

Deep Learning : a brief review (by Athanasios Voulodimos)

Slide 17 : 

Thank You