Basic understanding of Machine Learning and tools
In today’s digital world, Machine Learning is undeniably one of the most influential and powerful technologies to simplify human life and resolve complex problems. Even those it growing fast on day by day, this will take couple of years to see the full potential usages. Without this critical technology, Human-kind wouldn’t able to solve highly complex problems, avoid and optimize the resource effort wastage's.
This article is designed for layman to understand basic about Machine Learning and support tools.
In 21st Century, DATA is new Gold, Oil. One of recent article says, “2.6 quintillion bytes of data are created each day as our many internet-connected devices track, produce, and store information (source)”. Even those all machine (applications) generating data just like anything. Unless we analyze the data and find any hidden patterns in it, there is no use of spending effort & cost for storing the data.
Machine learning is a tool for turning data, information into knowledge. These techniques are used to automatically find the valuable underlying patterns within complex data easily that Human would take couple of days or month, even some cases years. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.
· What is Machine Learning?
· What are the types of Machine Learning?
· Machine Learning tools
What is Machine Learning (ML)?
Machine Learning is an application of artificial intelligence (AI) where a computer learns from the historical (past) data and make decisions autonomously without any user interaction.
ML helps us to take right business decisions, improve productivity, automatically detect disease, forecast the weather, and much more.
In Simply language, Machine learn from experience (given past data) and predict future.
Some of the best machine learning real-time examples are given below:
· Netflix — Movie Recommendation
· Facebook — Suggested friend’s request
· Speech Recognition
· Medical diagnosis
· Image Recognition
· Fraud Detection
· Self-Driving Cars
What are the categories of Machine Learning?
Supervised learning: Computer application learn through the given sample inputs (dataset) and their desired outputs.
Famous techniques in supervised learning
a) Regression: — Linear regression is used in predicting, forecasting, and finding relationships between quantitative data. Eg. Predicting the house price, scores, etc.
b) Classification: — Classification is used in predicting a qualitative outcome by analyzing data and recognizing pattern in it. Eg. Segregate whether someone loan defaulter or not?
Unsupervised learning: Computer application learn by himself (no sample input and desired outputs like supervised learning) and discover the hidden patterns in the given data) or a means towards an end. It’s helpful for finding useful insights from the data.
Famous techniques in unsupervised learning
a) Clustering: — Clustering is used to group the data into similar clusters, in order to extract information or make it easier to handle. Eg. Selling the product to right customer group
Semi-supervised learning: Hybrid model of supervised and unsupervised learning. Eg. Speech Analysis, text document classifier
Reinforcement learning: A computer application interacts with a dynamic environment and perform a certain goal/task. I mean, learn to work on dynamic environment without any supervision. Eg. self-driving cars
Few Machine Learning Tools:
· TensorFlow — TensorFlow is an artificial intelligence library for numerical computations, data flow graphs to build models. It allows developers to create large-scale neural networks with many layers.
· Jupyter Notebook or Google Colab’s — It’s an environment allows us to store, execute and share live code in the form of notebooks
· Pandas — Pandas is used for data cleaning and analysis. Mainly used to extract and transform the data from different sources of systems.
· Numpy — Numpy is used in scientific calculations.
· Scikit-Learn — Scikit-learn (Sklearn) is a robust library which provides Statistical modeling.
· MatPlotLib — Matplotlib is a graph plotting library which used to draw histograms, bar graphs, pie graphs..
· Seaborn — Seaborn is a data visulization library which runs on top of the popular Matplotlib data visualization library, although it provides a simple interface and aesthetically better-looking plots...
Conclusion: There are so many popular machine learning tools available in the market. But these are some of critical tools to start our journey in machine learning programming.
