What is Machine Learning and how to start learning Machine Learning?
Tags: Machine Learning, Supervised Learning, Semi Supervised Learning, Reinforcement Learning, Unsupervised Learning

For supervised learning, there are different algorithms to be used:

1. Logistic Regression

2. Ordinary Least Squares Regression

3. Naive Bayes Classification

4. SVM(Support vector machine)

5. Decision Tree

6. Random Forest

7. Artificial Neural Networks

Unsupervised Machine Learning: Unsupervised Machine Learning works with the unlabeled data that means the training data is neither classified nor labeled.It tries to discover the hidden structure from the unlabeled data sets.Unlike Supervised machine learning method, it can not be directly applied to a classification or regression problem because you have no idea how the output value looks like. Then one question may be arose in your head why we need this method. The answer is all data you find in the real life scenarios will not be well labeled.Some real life applications of Unsupervised machine learning are:

1. Clustering

2. Dimensionality Reduction

3. Visualization

4. Finding Association Rules

5. Anomaly Detection

Semi -supervised Machine Learning: It stands in-between the supervised and unsupervised machine learning techniques because Semi-supervised machine learning algorithms use both labeled and unlabeled data.It typically uses a small amount of labeled data and a large amount of unlabeled data for improving its learning accuracy.Programmers basically use a potion of unlabeled similar data for clustering and then use the existing labeled data to label the rest of the unlabeled data. Here are some real life Semi-supervised machine learning examples.

Reinforcement Machine Learning: It is a learning method about interacting with the environment by producing some actions and discovers errors to maximize the reward in a particular condition.Trial and error search and maximizing reward are the most relevant and important characteristics of reinforcement machine learning.In the absence of training data , it learns from the experience. It collects its training samples(this path is good, this path is risky so bad) though trial and errors to maximize its reward . An example of Reinforcement Machine learning is Robot mouse in a Maze.

In above I have tried to give an intuition about Machine Learning in brief.

Now the question is “how to start and from where to start?” .I’ll try my best to give this answer.

For learning Machine Learning algorithms you need to have knowledge on following topics :

* Linear algebra and multivariate calculus

* Basic Statistics

* Programming language-python is a better option.

If you have knowledge on the things mentioned above, you can enroll yourself in Andrew Ng’ s course on Machine Learning in Coursera. After you have done with this you can do another course on Practical machine learning in Coursera.

Now for practicing machine learning algorithms with different data sets you can use your PCs if they are configured enough.Anaconda is the best option according to me if you want to work in your PC locally.

You can also use different online platform for machine learning Practicing. Kaggle and Colab are two of them.

For Data repositories, here are the some names :

a. UCI data repository

b. Kaggle

c. Figshare

Here is the list of some books for beginner for learning Machine Learning.

1. “Machine Learning in action ” by Peter Harrington.

2. “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H.Witten.

3. “Hands–On Machine Learning with Scikit–Learn and TensorFlow” by Aurelien Geron.

And Finally Best of luck and enjoy your learning.