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Machine Learning is a tool to achieve Artificial Intelligence
Blogger Name : Mitesh Sharma
Date & Time: 14August2019 & 03:00 PM
Machine Learning is a subset of Artificial Intelligence or we can say that Machine Learning is a tool to achieve Artificial Intelligence. Robots, Automated Devices, Self Driving Car, Ecommerce Auto suggested Products system, Ads Recommendation and many more is the product of Machine Learning. These new techniques are running currently and try to reduce the risk and cost. This is possible with the help of Machine Learning. Machine Learning is very essential for smart Artificial Intelligence products. Machine Learning Algorithms works on a very good amount of past input data. If model gets a good amount of data as a input then trained model work on it and gives better result for every problem. But if problem is complex then model wants a very large amount of data. Deep Learning is used in that complex situation where model use Artificial Neural Network to solve the problem. But if the problem is not too much complex and we have a good amount of labeled or unlabeled input data then it would be nice to go with Machine Learning Algorithm. Most important thing is to find out the best Machine Learning method and algorithm for the problem, it will save the time and cost too. Machine Learning Algorithm result is compeletly depends upon input data, but it will improve continously with time. It learns from the mistakes and improve with experience.
Machine Learning is a technique which uses past data to solve the problem with the help of statically method and trained algorithm model.
It learns automatically and will improve with the experience. Machine Learning Engineer or Data Scientist train the machine model with the large amount of past data and take decision with the help of smart algorithms for any new problem. Some examples are Fraud Detection, Email Spam Filter, Online Customer Support and many more.
Machine Learning Process:
Understand the complete problem and collect its data from different sources. Choose the best Machine Learning method and algorithm according to its input pAST data and problem complexity. Train the model and its algorithm with the help of input past training data. Test the model with the new input data. If result is not good then again re-train the model and again test the input data untill get the best result.
This is the process to get the best result from Machine Learning Model Algorithm.
There are three types of Machine Learning:
This type of Machine Learning works on Labeled Data and model will predict the output solution from the input data predefined characteristics.
Methods: Classification and Regression
Algorithms: Logistic Regression, Linear Regression, Random Forest, Decision Tree, K-Nearest Neighbours, Naive Bayes, SVM etc.
This type of Machine Learning works on unlabeled data and model will predict the output solution according to undefined characteristics of input data.
Methods: Clustering and Association
Algorithms: K Means Clustering, Apriori, Fuzzy Means, Hierarchical clustering, Partial Least Squares etc.
This type of machine learning works on reward system. Machine learns from its feedback of result. It means machine learn from mistake and solve the problem. Machine makes better decision with Reward and Punish system. Suppose we give a picture of mango to trained reinforcement machine model to recognize it and machine works on it and gives an output that it is an apple. We know that it is a wrong answer, so we give the feedback to machine that your answer is wrong, it is a mango. Then machine learn it and use in the future to solve the same problem. This is Reinforcement Model where machine learns from its mistake and feedback. It will improve continuously to make better decision.
Machine Learning Applications:
Email Spam Filter
Netflix Movie Suggestion
Online Customer Support
Amazon Product Suggestion
Kinect (Body Track Movement)
Google Search Page (Response Rate)
Google Map (Faster Route Selection)
Machine Learning operations:
Number Plate Detection
Machine Learning is used in various sectors:
Crop Yield Estimation
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