# Best Way to Learn Machine Learning – 7 Easy Steps to become Expert

**The Best Way to Learn & Master Machine Learning**

Machine learning is the new electricity of the **IT industry**.

It is the science of getting things done with the help of intelligent machines.

It is a concept wherein computers uses a **set of algorithms** and **techniques** to **make decisions** and **predictions** from **available data**.

Machine learning is changing the world.

It is being used in a number of sectors such as **science**, **healthcare**, **production**, **retail**, **telecom**, etc.

Let’s quickly check the best way to get started with machine learning.

## Steps to Learn Machine Learning

Let’s explore the easiest way to learn machine learning in the form of steps:

### 1. Start with the Basics of Mathematics

Having a solid foundation in mathematics is necessary to start your journey in machine learning.

This helps in achieving a better understanding of machine learning algorithms.

Major concepts to cover in mathematics are:

#### a. Linear algebra

Linear algebra may be a sub-field of mathematics concerned with **vectors**, **matrices**, and **linear transforms**.

It’s a key foundation to the sector of machine learning, from notations wont to describe the operation of algorithms to the implementation of algorithms in code.

This helps in representing data as **linear equations**.

Learn the concepts of **algebra**, **radicals**, **graphical** **linear equations**, **how to perform operations on a slope**, **factoring**, **matrix analysis**, etc.

Algebra is actually used to find real-life variable and solving them.

It is used in **recommendation systems** and **facial recognition**.

Representation of data is done by the help of matrices learned in **linear algebra**.

#### b. Calculus

**Maxima & Minima**, Functions of **single** & **multiple variables**, and **partial derivatives** are some important topics of calculus to cover.

It is used to study variables and how they change.

Knowledge of calculus is required to build many machine learning techniques and applications.

**For example –** You’ll need to **calculate derivatives** and **gradients** for optimization of models.

#### c. Probability

Probability may be a field of mathematics that **quantifies uncertainty**.

It’s undeniably a pillar of the sector of **machine learning**, and lots of recommend it as a **prerequisite subject** to review before getting started.

It is the key area of **mathematics** for the **collection** and **analysis** of **data** in the **machine learning** field.

The concept of **probability** is, it is a measure of the chance that an event is going to occur.

You need to derive **insights** from the **available data**, and for this purpose, you need to understand probability.

Usually, **probability** and **statistics** are something that you need to study together.

A combination of both of these skills is required to become a **machine learning expert**.

As they provide you with knowledge of what type of **data analysis** is required.

### 2. Statistics

**Statistical techniques** and **methods** are needed to handle the **data**.

It is divided into two categories:

#### a. Descriptive Statistics

Descriptive statistics is required to **describe** and **summarize** the data available with you so that you can make decisions on what type of **data analysis tool** can be used to **interpret** the **outcomes.**

Topics to cover in descriptive statistics are – **Central Tendency**, **Normal Distribution**, **Variability,** and **Sampling Distributions**.

#### b. Inferential Statistics

Inferential statistics help you to draw out **inferences** and **conclusions** after the **analysis of data**.

Topics you’ll have to study to give conclusions are – **Estimation,** **Hypothesis testing**, **ANOVA**, **Correlation**, **Regression**, etc.

This technique is applied over a **smaller sample** and **imply** it over the larger group.

### 3. Machine Learning Algorithms

ML algorithms are **built-in machine** learning that can learn and make **predictions** from the data.

These are divided into** 3 groups**:

#### a. Supervised Learning

A model is prepared with the help of the **available data (known/labeled value)** and **predictions** are made on the basis of the model.

If the predictions are wrong, the **model** is **updated** and **predictions** are **corrected**.

This process continues until the model achieves a desired level of **accuracy** on the data.

#### b. Unsupervised Learning

A model is prepared without the help of any known or **labeled data**.

So connections are found between **non-assigned datasets**.

The machine does this through **patterns**, **trends,** and **similarities** between available data.

**Clustering** and **association** methods are examples that help in **unsupervised** machine learning.

#### c. Reinforcement Learning

Reinforcement learning falls between the above mentioned two algorithms, so it is also called **semi-supervised learning**.

It uses a small amount of known **input data** and supports a large amount of **unlabeled** input data (like **sampling method**).

It helps to provide** feedback** on the best method for **making predictions**.

### 4. Programming Languages

There is no one programming language that can cover the complete work in **machine learning**.

So language is **preferred** on the basis of the project you are working on.

Programming languages help you to **code** your problems in a language that is easily understood by the machine.

A few languages to start with are – **Python**, **Java**, **R**, and **Scala**.

Python is the most **popular programming language** used for machine learning projects.

### 5. Data Wrangling and Analysis

Without data, there is no **machine learning**, **data science**, or **big data**.

Both, the experts and beginners should know that **Data is the King**.

A well-structured data can allow us to perform complex algorithms very easily (only if you know how to).

The process of data analysis involves the **collection**, **cleaning** or **wrangling**, **storage**, **analysis**, and at the last **visualization** of the **data**.

You need to learn the various **data visualization** **tools** to **analyze** and **graphically** represent the data.

**QlikView**, **QlikSense**, **SAS visual analytics**, **D3.js**, **ggplot2**, etc. are some of the most used tools for **data visualization** in machine learning.

### 6. Refer Machine Learning Books and Communities

As it is said – Books are your best friends.

Read books in your **free time** and **increase** your **technical knowledge**.

Remember, learning is a **continuous process** because, except that, you’ll need to cycle your way back and review concepts to work.

Also, **participate actively** in the machine learning communities.

It will help you to **increase your network** and know the views of machine learning experts.

Thus, boosting your **knowledge** of technology.

### 7. Work on Real-time Machine Learning Projects

**Focus on Targeted Practice –**This involves the usage of**specific**and**concise exercises**to sharpen your skills.**Practice the Overall ML Workflow –**Start from**data collection**,**cleaning**, and**preprocessing**. Then move onto building models from the data and evaluate them on the basis of your problems.**Practice on Real Datasets –**Choose the**kind of data**that is appropriate for a**particular challenge**and**apply the algorithm**which will be best suited for the problem.

## Summary

If you have followed all these steps to learn **machine learning** and **practiced** with a real project, you’re now ready to take on the world!

Just kidding. You learned 90% of machine learning but still, there’s still much more to learn such as – **Deep Learning**, **Computer Vision**, **Natural Language Processing**, etc.

The key to becoming an expert in the **field** is to never stop learning.