Machine Learning Tutorial for Beginners

Machine Learning Tutorial for beginners: Machine Learning is the most in-demand technology in today’s market. In this blog on Introduction tIno Machine Learning, you will understand all the basic concepts of Machine Learning and Machine Learning Process steps, Machine learning types.

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So, Let’s start our first topic “Machine Learning tutorial”.

Introduction to Machine Learning

Machine Learning is a field of study that facilitates computers with capability to learn without being actually explicitly programmed. It is a scientific approach to solve certain tasks using various algorithms and predictions. A mathematical model for certain training data is built using certain algorithms based on statistics to make predictions without actually programming it. There are lot of techniques and approaches in machine earnings. As it has a lot of applications based on different scenarios in real life, different methods are used as per requirement. These methods use certain algorithms to predict and get precise result.

Machine learning Types

Machine learning is categorized under following subclasses:

Supervised Learning:

Here the labeled data set is considered as trainer for the model which make machine to learn and to predict future values accordingly.It makes the system enable to provide predictive entities for any new input after adequate training. The learning algorithm can also relate its output with the correct, expected output and find errors to provide feedback and to modify the model accordingly.

Unsupervised Learning:

For the information or dataset used to train is neither classified nor labeled, unsupervised learning comes into picture.It helps in finding hidden structure from unlabeled data. Hidden patterns and relationships in the dataset are figured out by clustering.

Semi-supervised Learning:

Semi Supervised learning is said to be the combination of supervised and unsupervised learning. It trains with labeled and unlabeled data, mostly unlabeled data is more compared to labeled data.

Reinforcement Learning:

This learning category interrelates with its environment by producing actions and discovers errors or correct output. Trial and error search and overdue reward are the most relevant characteristics of reinforcement learning. In supervised learning the training data set has answer keys with it, while in reinforcement; machine tries to learn from acknowledged reward or errors. It will try to get maximum rewards in learning procedure for particular input. Following image shows overall view of categories in Machine Learning.

Machine Learning Process Steps:

Steps involved in machine learning:

Data Collection:

This process involves collection of qualitative and quantitative data. The reference datasets can be collected from pre-collected data, through datasets from Kaggle, UCI, etc.

Data Cleaning:

Successful working of any model depends on qualitative dataset. So data cleaning is important step which includes wrangling data, removal of duplicates, correcting the errors, dealing with missing values, normalization, data type conversions etc.

Data Validation (Feature Selection):

It is process of picking up those features which are most contributing elements for dependant variable in prediction model. As irrelevant features decrease the performance of the model we have to choose only those which can help to train model for better prediction.

Model Design:

It is the step in which the model is designed on basis of dataset. Different algorithms are used to build suitable model. According to supervised, unsupervised and reinforcement types algorithms are used.

Evaluating Model:

This step involves testing the performance of model. Model is trained with combinations of some datasets. Generally 70% of data from set is used to train the model for objective performance. Remaining 30 % data is for testing purpose.

Parameter Tuning:

This step involves tuning of hyper parameter. It is one of the methods to improve the performance of model. It generally includes changes in certain functional parameters like number of training steps, learning rate, initialization values and distribution, etc.

Prediction:

In the final step model is tested with unseen data. It will generate the results which show how designed model approaches towards expected performance. If any unexpected result is obtained, then as per requirement above some or all steps are repeated.

So, This is the end of this machine learning tutorial for beginner's concept. I hope you all found this blog informative. If you have any thoughts to share, please comment them below. Stay tuned for more blogs like these! Get success in your career as a Machine Learning Engineer or Data Scientist by being a part of the Prwatech, India's leading Data Science training institute in Bangalore.