Before some years (about 40-50 years), machine learning was science fiction, but today it is the part of our daily life. Machine learning is making our day-to-day life easy from self-driving cars to Amazon virtual assistant “Alexa”. However, the idea behind machine learning is so old and has a long history. Below some milestones are given which have occurred in the history of machine learning:

6.1

The early history of Machine Learning (Pre-1940):

• 1834: In 1834, Charles Babbage, the father of the computer, conceived a device that could be programmed with punch cards. However, the machine was never built, but all modern computers rely on its logical structure.

• 1936: In 1936, Alan Turing gave a theory that how a machine can determine and execute a set of instructions.

The era of stored program computers:

• 1940: In 1940, the first manually operated computer, “ENIAC” was invented, which was the first electronic general-purpose computer. After that stored program computer such as EDSAC in 1949 and EDVAC in 1951 were invented.

• 1943: In 1943, a human neural network was modeled with an electrical circuit. In 1950, the scientists started applying their idea to work and analyzed how human neurons might work.

Computer machinery and intelligence:

• 1950: In 1950, Alan Turing published a seminal paper, “Computer Machinery and Intelligence,” on the topic of artificial intelligence. In his paper, he asked, “Can machines think?”

Machine intelligence in Games:

• 1952: Arthur Samuel, who was the pioneer of machine learning, created a program that helped an IBM computer to play a checkers game. It performed better more it played.

• 1959: In 1959, the term “Machine Learning” was first coined by Arthur Samuel.

The first “AI” winter:

• The duration of 1974 to 1980 was the tough time for AI and ML researchers, and this duration was called as AI winter.

• In this duration, failure of machine translation occurred, and people had reduced their interest from AI, which led to reduced funding by the government to the researches.

Machine Learning from theory to reality

• 1959: In 1959, the first neural network was applied to a real-world problem to remove echoes over phone lines using an adaptive filter.

• 1985: In 1985, Terry Sejnowski and Charles Rosenberg invented a neural network NETtalk, which was able to teach itself how to correctly pronounce 20,000 words in one week.

• 1997: The IBM’s Deep blue intelligent computer won the chess game against the chess expert Garry Kasparov, and it became the first computer which had beaten a human chess expert.

Machine Learning at 21st century

• 2006: In the year 2006, computer scientist Geoffrey Hinton has given a new name to neural net research as “deep learning,” and nowadays, it has become one of the most trending technologies.

• 2012: In 2012, Google created a deep neural network which learned to recognize the image of humans and cats in YouTube videos.

• 2014: In 2014, the Chabot “Eugen Goostman” cleared the Turing Test. It was the first Chabot who convinced the 33% of human judges that it was not a machine.

• 2014: DeepFace was a deep neural network created by Facebook, and they claimed that it could recognize a person with the same precision as a human can do.

• 2016: AlphaGo beat the world’s number second player Lee sedol at Go game. In 2017 it beat the number one player of this game Ke Jie.

• 2017: In 2017, the Alphabet’s Jigsaw team built an intelligent system that was able to learn the online trolling. It used to read millions of comments of different websites to learn to stop online trolling.

Machine Learning at present:

Now machine learning has got a great advancement in its research, and it is present everywhere around us, such as self-driving cars, Amazon Alexa, Catboats, recommender system, and many more. It includes Supervised, unsupervised, and reinforcement learning with clustering, classification, decision tree, SVM algorithms, etc.

Modern machine learning models can be used for making various predictions, including weather prediction, disease prediction, stock market analysis, etc.

So, this brings us to the end of blog. This Tecklearn ‘History of 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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