Machine learning is a category under artificial learning. The basic goal of machine learning is about understanding the structure of data and fitting it into models that can be easily understood and put into use.

Being a part of computer science, machine learning differs in style from other computation approaches. Machine learning allows the computers to train on data inputs and use analysis for the output of values that fall in a certain range.

The use of machine learning can be felt by the fact that it is widely used in social media like Facebook for identifying faces through facial identification and tagging people based on the same. Also, Optical Character Recognition (OCR) changes characters into movable type also uses Machine learning. Self-driving cars completely rely on machine learning to navigate will be available for the public in a short time.

Machine learning with Python has gained popularity recently because of its deep learning frameworks and ease to produce syntax. Python proves to be a straightforward and powerful language for processing and working with data.

**Steps to Master Machine Learning**

**Basic Skills**

Having a basic understanding of Python is essential to perform Machine Learning. Finding beginner’s tutorial for learning python is not difficult because of its widespread popularity and general- purpose nature.

Firstly, you have to get Python installed in your system. Installing Anaconda will be useful for learning machine language as well as scientific computing. Anaconda is an industrial-strength Python implementation for OSX, Linux, and Windows having all the required packages for machine learning, sci-kit-learn, and Matplotlib. The most dominant installed version is Python 2.7 and that is the reason professionals recommend its installation.

**Foundational ML Skills**

The data scientist is a reflection of fields from machine learning since the work performed by the data scientists involves usage of machine learning to varying degrees. Like all the other courses, theoretical understanding is related to practical application. Gaining skills in machine learning requires time investment in academic institutions or it can also be gained to some extent through self-studies.

One need not have a PhD-level understanding of the theoretical parts of machine learning in a similar way that all the programmers need not have theoretical computer science education for becoming effective coders.

**Python Packages Overview**

After having the basics of Python programming and a glimpse of machine learning, you can have access to a number of open source libraries to facilitate practical ML. There are certain libraries that are put to use for performing basic and general tasks linked with machine learning.

- Pandas: A library including structures like data frames
- Scikit-learn: This is an ML algorithm that is used for data mining and data analysis tasks.
- Numpy: This is mainly used for its N-Dimensional array objects.
- Matplotlib: This is a 2D plotting library that produces publication quality figures.
**Getting Started**

After getting to know the fundamentals of Machine learning, you can now implement its algorithms with Python using the Scikit-learn library. iPython Notebook is a very interactive environment for executing python programs. These notebooks can be ideally used online or can also be downloaded locally on your personal computer.

**ML Topics with Python**

Having your foundation laid in sci-kit-learn, you can have an in-depth exploration of some common and useful algorithms. *k-means* clustering is one of the well-known machine learning algorithms. This is an effective and simple method for solving unsupervised learning.

- k-means Clustering: This starts with defining a target number
*k*, which specifies the number of centroids required in the datasheet. This is imaginary or can also be a real location that represents the center of the cluster.

In simpler terms, the *k-means* algorithm identifies *k* number of centroids and then allocates data points to the nearest possible clusters also trying to keep the cluster possible.

*Means *in *k-means* refers to averaging of the data which can also be named as finding the centroid.

- Decision Trees: These are drawn in an upside down manner with its roots at the top. With the decision tree, the relations can be viewed easily. In general decision tree, algorithms are referred to as a CART that can also be called as Classification And Regression Trees.
- Linear Regression: This is learned to study the relations between input and output variables. This is a machine learning as well as a statistical algorithm.

This is a linear model that takes the relationship between single output variables and input variables.

With simple input variables, this method is called as a simple linear regression, whereas with multiple variables, it is called a multiple regression.

- Logistic Regression: This regression analysis is conducted when we have a binary dependable variable. This is a predictive analysis. This is used to describe the data and the relation between the binary dependent variable and the other ordinal, or ratio-level independent variable.
**Advanced ML Topics With Python**

After having a strong hold on sci-kit-learn one can easily pay attention towards some more advanced topics and concepts.

- Support Vector Machines: This is another set of simple algorithms that an expert shall have in his arsenal. This is a preferred language because of its ability to produce significantly accurate programs with consumption of less computational power. This can be used for tasks like classification as well as regression. But, majorly it is used in classification objectives.
- Dimensionality Reduction: Machine learning classification requires the use of a number of factors for the final classification. These factors are termed as features. Dimensional reduction reduces the number of random variables under consideration, by obtaining a set of principal variables. This can be divided into the selection and extraction of the features.

Most of the well-known machine learning algorithms have been covered under this article like k-means clustering and support vector machines. Also, machine language support tasks have been examined. Continuous practice can turn you into an efficient coder. Despite certain hurdles, the ease of learning this language makes it popular among its learners. The pre-designed libraries and frameworks along with the syntax in simple English language make it easy to learn and implement.