Machine learning is the branch of artificial intelligence (AI) which provide the ability to learning automatically learn and improve from experience. It was first introduced in 1959 by Arthur Samuel.

The primary aim is to allow the computer to learn automatically without human involvement or assistance and adjust actions accordingly. Many problems are historical very easy for humans, and very difficult for networks, Machine learning (deep learning in particular) is currently our best solution for many of those problems.

For example, medical diagnosis, image processing, prediction, classification, regression etc.

Evolution of Machines

Features of Machine Learning

Machine learning uses data to detect a various pattern in any dataset.
It is a data-driven technology.
It learns from past data and improves it automatically.
Machine learning is similar to data mining, as it deals with a considerable amount of the data.

Need of Machine Learning

The demand for machine learning is rapidly increasing day by day. As a human, we have many limitations as we cannot access a large amount of data manually, so for that, we need some computer systems. And machine learning to make things easy for us.

It’s use cases can easily understand machine learning. Right now, machine learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion on Facebook.

Some top companies, like Amazon and Netflix, have built machine learning models, who are using a large amount of data to analyze the user interest and recommend the product correctly. It is also used to finding a hidden pattern and extracting useful information from data.

Types of Machine Learning

Supervised Learning- “Train me!”
Unsupervised Learning- “I am self-sufficient in learning!”
Reinforcement Learning- “My life my rules (Hit and Trial)!”

Supervised Learning:

Supervised Learning is the type of machine learning, where we can consider a teacher guides the learning.

The dataset which we have will acts as a teacher and use to train the model and the machines. Once the model gets trained, it starts making the prediction or decision when new data is given to it.

It can be grouped into two types:

Classification
Regression

Classification

It is a technique that aims to reproduce class assignments. It produces the response value, and the data separated into “classes.”

Example: Recognition of a type of car in a photo.

Regression

Regression is a technique which aims to produce the output value. We can use it.

Example: Use to predict the price of a different product.

Unsupervised Learning:

The unsupervised machine learning algorithm is used when the train information is neither classified nor labelled. If the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it.

Supposed we presented images of apples, bananas, and mangoes to the model, based on some patterns and relationships it creates cluster and divides the dataset into clusters. Now if a new data is delivered to the model, it adds it to one of the generated groups.

It also has two types:

Clustering
Association

Clustering:

Clustering is used to find likeness and differences in a particular thing. It groups similar things. This algorithm can help us to solve many obstacles.

Example: Create clusters of similar tweets based on their content, find a group of photos with similar cars, or identify different type of news.

Association:

Association rules mining is another key of unsupervised data mining method, after clustering, which finds interesting associations (relationships, dependencies) in a large set of data items.

Semi-Supervised Learning:

It falls somewhere in between supervised and unsupervised learning. So, they use both labelled and unlabelled data for training where a small amount of labelled data and a big amount of unlabelled data is used.

Commonly, semi-supervised learning is chosen when the acquired labelled data requires skilled and significant resources to train it.

Reinforcement Learning:

Reinforcement learning is the ability of an agent to interact with the environment and find out the best outcome.

It chases the concept of hit and trial method. The agent is rewarded or condemned with a point for a correct or a wrong answer, and based on the positive rewards points gained the model train itself. And once again it trained to predict the new data presented to it.

The goal of the agent is to get the most reward points and to improve its performance.

Components of Reinforcement Learning

Decision making/ Agent learning
Environment– What agents interact with
Actions– What the agent do!

Deep Learning

Deep learning is also a subset of Machine learning in artificial intelligence. Learning can be supervised, semi-supervised, or unsupervised.

Deep learning is a machine learning approach that prepares computers to achieve what comes naturally to humans. Deep learning is a crucial technology behind driverless cars, enabling them to recognize a stop sign.

Working of Deep Learning

In deep learning is getting lots of attention lately and for a good reason. A computer model learns to perform the classification tasks directly from any images, text, and sound in deep learning.

The term “deep” commonly refers to the number of hidden layer in the neural network. Conventional neural networks only contain 2-3 hidden layers, while deep networks can have 150.

So, this brings us to the end of blog. This Tecklearn ‘Machine Learning and Deep Learning’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/artificial-intelligence-and-deep-learning-with-tensorflow/

Artificial Intelligence and Deep Learning with TensorFlow Training

About the Course

Tecklearn’s Artificial Intelligence and Deep Learning with Tensor Flow course is curated by industry professionals as per the industry requirements & demands and aligned with the latest best practices. You’ll master convolutional neural networks (CNN), TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, and hyperparameters via hands-on projects. The trainee will learn AI by mastering natural language processing, deep neural networks, predictive analytics, reinforcement learning, and more programming languages needed to shine in this field.

Why Should you take Artificial Intelligence and Deep Learning with Tensor Flow Training?

According to Paysa.com, an Artificial Intelligence Engineer earns an average of $171,715, ranging from $124,542 at the 25th percentile to $201,853 at the 75th percentile, with top earners earning more than $257,530.
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IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle and almost all the leading companies are working on Artificial Intelligence to innovate future technologies.

What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

Got a question for us? Please mention it in the comments section and we will get back to you.

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