Machine Learning is a form of AI. so, It is one of the most predominant topic in Artificial Intelligence. It enables a system to learn from the data rather than programming, though Machine Learning is not a simple process to achieve.
Machine Learning uses a variety of algorithms that helps learn to improve data, describe data, and predict the outcomes. so, It is a vast concept to learn and there is a lot to achieve it. ML can also be define as the process of solving practical problems.
Therefore, it can be used to enhance the customer experience, better handle and predict the results from complex data. thus, Machine Learning is finding its way into every aspect of computing from social media to complex applications like financial applications.
Programming Languages for Machine Learning :
- Python
- C++
- C#
- R
Additional Languages for Machine Learning :
- Java
- JavaScript
- Julia
- Go
- Scala
Types of Learning :
- Supervised
- Unsupervised
- reinforcement.
1. Supervised Learning :
The dataset is a collection of the labeled data and it is also given as input, Supervised Learning has a feedback mechanism. so, Frequently used algorithms in Supervised Learning are Decision Trees, Logistic Regression and Support Vector Machine.
Supervised Learning can also categorise into “Regression” and “Classification”.
- What is Regression?
thus, Regression is a statistical method used in finance, investing and others to determine strength and the relationship between one variable and the series of other variable.
- What is Classification?
It predicts the class to where data elements belongs to. however, In this the output obtained will be categorical or discrete.
- In classification problem the algorithms has to classify examples in discrete classes.
2. Unsupervised Learning:
The dataset is a collection of unlabeled data and it is given as input. though Unsupervised Learning has no feedback mechanism. so, Frequently used algorithms in Unsupervised Learning are k-means clustering, hierarchical clustering and Apriori algorithm .
We can get the output by clustering the data based on the variables in the data. It is use for statistical data analysis in many fields. hence, User data is plot on a graph and they are divide up into individual clusters upon their algorithm analysis of the data. Face recognition is Also an example of Unsupervised learning
- Face Recognition is also implement using Deep Learning.
3. Reinforcement Learning :
Reinforcement learning is completely based on the person’s behaviour and psychology. so, There are three approaches to implement a reinforcement learning algorithm. they are Value-Based, Policy-Based, Model-Based.
Generally, it is studied in other disciplines, such as game theory and others. In Value-Based, we have to try to maximize a value function. Though In Policy-Based, We will try to be with a policy such that the execution at every state helps to get maximum reward in future. In Model-Based, we have to create a virtual model for each and every environment, then the agent will learn to perform in that particular environment.
Top 5 Machine Learning Tools :
- Accord.net :
This comes with image and also with audio.
- Sci-kit learn :
It assists in regression, clustering, classification and preprocessing.
- TensorFlow :
It is a mixer of Machine Learning and as well as neural network models.
- PyTorch :
It is a deep learning framework and this has a capability over the GPU also.
- Google Cloud AutoML :
Hence, The intent of the Google Cloud AutoML is to make AI convenient to all.
Finest Libraries in ML:
- Numpy:
It is a library for Python language. so, It gives support to large and multi-dimensional arrays.
- Pandas :
It is also a library for Python language. We use pandas in a dataset for data manipulation and analysis.
- Open CV :
though It is a library for programming functions predominantly focused at real time computer vision.
- Matplotlib :
It is a library for Python language for plotting. Its numerical mathematics extension Numpy.
- Scikit-learn :
thus, It is a library for Python language which is a free software to use.
The Top most important Machine Learning Algorithm :
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- Naive Bayes
Life Cycle for Machine Learning :
- Gathering Data :
We have to explore different data sources as data can be found and also collected from different sources like web, files, records.
- Data Preparation :
After the collection of the dataset and now we have to prepare the dataset for future steps. hence, Data exploration and Data pre-processing are the two processes involved in Data Preparation.
- Data Wrangling :
Here in this process, Cleaning of the dataset is done and converting the raw data into well defined dataset.
- Data Analysis :
Here in this step, the cleaned and prepared data is now moved to the analysis step. We also, build machine learning model to analyse the data using various techniques.
- Train Model :
Now in this step, we train the model to increase the performance for the better results at the output of the problem.
- Test Model :
so, as our ML model is trained on the provided dataset. now we test that model and check for the accuracy of the model.
- Deployment :
Hence, This is the final step in the Machine Learning life cycle. However, we deploy the model into the real system if we are getting the accurate result as per our requirement and predicted result.
Applications :
- Image Recognition
- Speech Recognition
- Traffic Prediction
- Product Recommendations
- Medical Diagnosis
- Online Fraud Detection
- Stock Market Trading
hence, These are the most common applications of Machine Learning.
Best websites for Machine Learning Datasets:
- FiveThirtyEight
- Kaggle
- Socrata
- Awesome-Public-Datasets on Github
- Google Public Datasets
- UCI Machine Learning Repository
- Quandl ( Economic and Financial Data )
- Academic Torrents
- BuzzFeed News
- Jeremy Singer-Vine
Conclusion:
Machine Learning is a vast concept to deal with, it is the first part to achieve when you want to deal with Artificial Intelligence in a very finite and easier way. Hence, ML techniques are required to improve the accuracy of the predictive ML model. so, Machine Learning is a powerful set of technologies that can help even organisations to transform their understanding into the data.
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Thanks for Reading ??…!
Written by: Sai Harsha Tamada
Reviewed by: Vikas bhardwaj
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