Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.

Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.

On a broad level, we can differentiate both AI and ML as:

AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.

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Below are some main differences between AI and machine learning along with the overview of Artificial intelligence and machine learning.

Artificial Intelligence

Artificial intelligence is a field of computer science which makes a computer system that can mimic human intelligence. It is comprised of two words “Artificial” and “intelligence”, which means “a human-made thinking power.” Hence we can define it as,

Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence.

The Artificial intelligence system does not require to be pre-programmed, instead of that, they use such algorithms which can work with their own intelligence. It involves machine learning algorithms such as Reinforcement learning algorithm and deep learning neural networks. AI is being used in multiple places such as Siri, Google?s AlphaGo, AI in Chess playing, etc.

Based on capabilities, AI can be classified into three types:

• Weak AI

• General AI

• Strong AI

Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is said that it will be intelligent than humans.

Machine learning

Machine learning is about extracting knowledge from the data. It can be defined as,

Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.

Machine learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate result or give predictions based on that data.

Machine learning works on algorithm which learn by it?s own using historical data. It works only for specific domains such as if we are creating a machine learning model to detect pictures of dogs, it will only give result for dog images, but if we provide a new data like cat image then it will become unresponsive. Machine learning is being used in various places such as for online recommender system, for Google search algorithms, Email spam filter, Facebook Auto friend tagging suggestion, etc.

It can be divided into three types:

• Supervised learning

• Reinforcement learning

• Unsupervised learning

Key differences between Artificial Intelligence (AI) and Machine learning (ML)

Artificial Intelligence Machine learning

Artificial intelligence is a technology which enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.

The goal of AI is to make a smart computer system like humans to solve complex problems. The goal of ML is to allow machines to learn from data so that they can give accurate output.

In AI, we make intelligent systems to perform any task like a human. In ML, we teach machines with data to perform a particular task and give an accurate result.

Machine learning and deep learning are the two main subsets of AI. Deep learning is a main subset of machine learning.

AI has a very wide range of scope. Machine learning has a limited scope.

AI is working to create an intelligent system which can perform various complex tasks. Machine learning is working to create machines that can perform only those specific tasks for which they are trained.

AI system is concerned about maximizing the chances of success. Machine learning is mainly concerned about accuracy and patterns.

The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing, intelligent humanoid robot, etc. The main applications of machine learning are Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.

On the basis of capabilities, AI can be divided into three types, which are, Weak AI, General AI, and Strong AI. Machine learning can also be divided into mainly three types that are Supervised learning, Unsupervised learning, and Reinforcement learning.

It includes learning, reasoning, and self-correction. It includes learning and self-correction when introduced with new data.

AI completely deals with Structured, semi-structured, and unstructured data. Machine learning deals with Structured and semi-structured data.

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

Machine Learning

Machine Learning Training

About the Course

Tecklearn’s Machine Learning training will help you develop the skills and knowledge required for a career as a Machine Learning Engineer. It helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning Certification Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. With these key concepts, you will be well prepared for the role of Machine Learning (ML) engineer. In addition, it is one of the most immersive Machine Learning online courses, which includes hands-on projects

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• The average machine learning salary, according to Indeed’s research, is approximately $146,085 (an astounding 344% increase since 2015). The average machine learning engineer salary far outpaced other technology jobs on the list.

• IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle & other MNCs worldwide are using Machine Learning for their Data analysis

• The Machine Learning market is expected to reach USD $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent.

What you will Learn in this Course?

Introduction to Machine Learning

• Need of Machine Learning

• Types of Machine Learning – Supervised, Unsupervised and Reinforcement Learning

• Applications of Machine Learning

Concept of Supervised Learning and Linear Regression

• Concept of Supervised learning

• Types of Supervised learning: Classification and Regression

• Overview of Regression

• Types of Regression: Simple Linear Regression and Multiple Linear Regression

• Assumptions in Linear Regression and Mathematical Concepts behind Linear Regression

• Hands On

Concept of Classification and Logistic Regression

• Overview of the Concept of Classification

• Comparison of Linear regression with Logistic regression

• Mathematics behind Logistic Regression: Detailed Formulas and Functions

• Concept of Confusion matrix and Accuracy Measurement

• True positives rate, False positives rate

• Threshold evaluation with ROCR

• Hands on

Concept of Decision Trees and Random Forest

• Overview of Tree Based Classification

• Concept of Decision trees, Impurity function and Entropy

• Concept of Impurity function and Information gain for the right split of node and

• Concept of Gini index and right split of node using Gini Index

• Overfitting and Pruning Techniques

• Stages of Pruning: Pre-Pruning, Post Pruning and cost-complexity pruning

• Introduction to ensemble techniques and Concept of Bagging

• Concept of random forests

• Evaluation of Correct number of trees in a random forest

• Hands on

Naive Bayes and Support Vector Machine

• Introduction to probabilistic classifiers

• Understanding Naive Bayes Theorem and mathematics behind the Bayes theorem

• Concept of Support vector machines (SVM)

• Mathematics behind SVM and Kernel functions in SVM

• Hands on

Concept of Unsupervised Learning

• Overview of Unsupervised Learning

• Types of Unsupervised Learning: Dimensionality Reduction and Clustering

• Types of Clustering

• Concept of K-Means Clustering

• Mathematics behind K-Means Clustering

• Concept of Dimensionality Reduction using Principal Component Analysis (PCA)

• Hands on

Natural Language Processing and Text Mining Concepts

• Overview of Concept of Natural Language Processing (NLP)

• Concepts of Text mining with Importance and applications of text mining

• Working of NLP with text mining

• Reading and Writing to word files and OS modules

• Text mining using Natural Language Toolkit (NLTK) environment: Cleaning of Text, Pre-Processing of Text and Text classification

• Hands on

Introduction to Deep Learning

• Overview of Deep Learning with neural networks

• Biological neural network Versus Artificial neural network (ANN)

• Concept of Perceptron learning algorithm

• Deep Learning frameworks and Tensor Flow constants

• Hands on

Time Series Analysis

• Concept of Time series analysis, its techniques and applications

• Time series components

• Concepts of Moving average and smoothing techniques such as exponential smoothing

• Univariate time series models

• Multivariate time series analysis and the ARIMA model

• Time series in Python

• Sentiment analysis using Python (Twitter sentiment analysis Use Case) and Text analysis

• Hands on

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