Welcome to codingallrounders blogger 🙂
In this article, you are going to get an overview of Machine Learning.
By the end of this article, you will able to clear most of your doubts on Machine Learning.
Real-life Applications of Machine Learning:
1.Netflix.
2.Facebook.
3.PayPal.
Challenges before Machine Learning:
1. Rule-based systems fail in real life scenarios.
2. Hand coding systems are difficult.
3. Programs fail outside test scenarios.
4. Not suitable for practical situations.
How did Machine Learning evolve?
1.Tuning Machine,1950.
2.Gary Kasparov vs IBM Deep Blue,1997.
3.Microsoft Kinect Gesture Recognition.
4.Face Recognition on facebook.
5.Alpha Go.
Why Machine Learning?
1.Powerful Processing.
2.Better Decision &Prediction.
3.Quicker Processing.
4.Accurate.
5.Affordable Data Management.
6.Inexpensive.
7.Analyzing Complex Big Data.
What is Machine Learning?
Machine Learning is a method that allows computers to imitate and adapt human-like behavior.
Machine Learning makes predictions and decisions based on past data.
Systems learn, grow and develop when exposed to new data.
Types of Machine Learning:
1.Supervised Learning: A machine learning model is able to predict future outcomes with the help of training from past data.
I.Classification.
II.Regression.
2.Unsupervised Learning: Find hidden patterns without any external input other than the raw data.
I.Clustering.
II.Association.
Breakthroughs in Machine Learning:
1.Google Deep Mind.
2.Alpha Go.
3.Self-driving Cars.
4.Smarter investment opportunities.
5.Better medical diagnosis.
Machine Learning Career:
1.Mathematics.
2.Fundamentals of Statics.
3.Software design.
4.Applying ML libraries and algorithms.
5.Data Modelling.
6.Programming Languages.
I hope this article was successful in explaining you the basics of Machine Learning.
You can write to me at codingallrounders@gmail.com
