Machine learning finds patterns in data to make a suitable decision. Deep learning is a branch of machine learning that uses neural networks with numerous layers. Today, problems similar as computer vision, natural language processing, driverless cars can be answered with deep learning.
To be suitable to do deep learning systems, I recommend you to learn machine learning first. Universities don't have a separate machine learning department. But fortunately, there are numerous free coffers and training videos on the Internet.
Whether you're a pupil, an hand who wants to change careers, or someone who wants to use machine literacy in your business.

What's Machine Learning?
As you know, the amount of data produced has increased with the development of the internet and socialmedia.However, data is moment’s oil, If ai is today’s electricity. Companies similar as Google, Facebook, Amazon came huge companies because they estimated the data they attained.
To use oil, you have to reuse it right? Just like oil, data have to reuse to be used. Machine learning is the wisdom of chancing retired patterns in data.

Step 1. Learn Programming Languages
An important part of machine learning is programming. You need to know a programming language to recognize data, clean data, preprocess data, make a model.

With Python, you can both do data- grounded systems and work in numerous areas similar as web programming or game development. Python is the most habituated language in machine literacy and deep literacy.

Step 2. Libraries for Machine Learning
You can write a machine learning model from scratch. But, there's no need to reinvent the wheel. You can build briskly and more practical models using libraries similar as scikit learn.

In machine learning, you come through numerous matrices and array operations. The library you need to know for multidimensional array operations is NumPy.

Another important library is Pandas. As you know, real- world datasets are complex. To analyze these complex datasets, data cleaning and data preprocessing are needed. The library you need to know for these operations is Pandas.
Matplotlib and Seaborn
It's important to explore the data before building the model. Data visualization is the easiest way to explore data. Matplotlib and seaborn libraries are substantially used for data visualization.
Matplotlib is a important library and you can make great visualizations with this library. For statistical graphs, the seaborn library is perfect.

Scikit- Learn
The main purpose of machine learning is to make a good model. You can use the scikit learn library to make a model. You can find numerous supervised and unsupervised learning algorithms in the Scikit learn library.

Another important library for making machine learning systems is TensorFlow. With TensorFlow, you can make end-to- end machine learning systems.

Step 3. Tools You Need to Know for Machine Learning
There are numerous tools you can use for machine learning. Let’s take a look at tools you need to know for machine learning.

Step 4. Disciplines for Machine Learning
To learn machine learning, it's enough to know these disciplines at a introductory level. It's also important that you know the field you ’re working on.

Step 5. Algorithms for Machine Learning
Data quality is very important for a machine learning project to be successful. Another important point is to use an algorithm suitable for the data.

Step 6. Websites for Machine Learning
There are numerous spots you can use for machine literacy. Kaggle comes first among these spots.

Author's Bio: 

I am a content writer from 4 years. I love to share my knowledge through writing. I work for fashion, travel, education, food and etc.