Roadmap for Machine Learning

Where to start and how to succeed?

Find out where to start your machine learning journey, what kinds of projects you can work on along the way, and how you can succeed in this complex field.

The data age is upon us, and if you’re not quite ready for it, don’t worry, we can still help you get on board.

Money may have made the world go round, but all it takes is data and information in the modern age. Applying these two basic concepts and analyzing and using such data helps us to truly collect and analyze valuable data that keeps us ahead of our competitors.

Machine learning in a nutshell

In general, machine learning is a subfield of artificial intelligence that is used to make decisions or make predictions based on prior patterns in data. It is a way for computers to learn from experience and adjust accordingly to repeat processes and results, without being pre-programmed with specific instructions like traditional algorithms.

In simpler terms: it basically makes machines smarter, enabling them to learn, predict and adapt to past behavior. It’s a way to achieve artificial intelligence without having to specify all the rules and processes in advance.

Machine Learning roadmap

First step: learning the programming language

Before learning anything, you must first learn a programming language that is suitable for machine learning and fully understand the concepts of that language. My suggestion to you is the Python programming language, because this language has many libraries for machine learning and can give you many possibilities, and learning the Python language is much easier than other programming languages.

Another programming language for this field is the R programming language, which is good for statistics and I do not recommend that you go to this language at first because you will get confused, and it will cause you to not understand the programming concepts well. Understand

You must be familiar with the following topics in Python and understand the concepts well so that you don’t get into trouble!

• Variables
• Data Types
• Numbers in Python
• Texts in Python (Strings)
• Operators
• Lists
• Tuples
• Sets
• Dictionaries
• Conditional commands (If/Else)
• Loops
• Functions
• Arrays
• Classes and object orientation (Classes/Objects)

The second step: learning the most used libraries

As far as you have learned Python, you need to learn the libraries that are required for machine learning to help you with machine learning, you need to know that you do not need to learn 100% of these libraries, and you must have a relative understanding of these libraries to solve problems. For example, you can easily calculate statistics and probability, or you can easily display the data graphically, and you can have a better understanding of the data.

A list of Python libraries that you should learn and be able to communicate with them well.

• NumPy: is used for mathematical and statistical calculations
• Pandas: for data and information and bracelets
• SciPy: for more advanced mathematical and statistical calculations
• Scikit learn: Algorithms and machine pre-processing
• Matplotlib: graphical display of charts
• Seaborn: Graphic display of charts

In the next posts and future articles, we will teach you all the introduced libraries in an easy way!

The third step: learning mathematics related to the machine

If we say that machine learning or machine learning does not need mathematics, and we don’t need to learn mathematics at all, you should know that we are deceiving ourselves and the reality is not like that, but don’t worry, I will tell you the solution, and then you can decide.

Machine learning requires math, but the math that I tell you to do, for example, do a matrix multiplication, and not the boring university statistics and probability! In machine learning, we only need to understand the math problem and not solve it, why? Because the bookstores that I introduced above do all these things for you, and they are faster than yours.

For example, when you hear the variance, you should know where the answer comes from and how it works, and you should understand these topics so as not to get confused.

In the future articles and posts, we will definitely talk about the mathematical concepts needed for the car and we will teach you all the necessary topics, so don’t worry about anything!

You must know topics such as linear algebra, statistics and probability, etc., so that you can have a good understanding and understand.

The fourth step: learning the concepts of machine learning

In the beginning, you don’t need to learn complex algorithms and advanced topics, it’s enough to gradually learn machine concepts like what is data? What is processing? And learn other things to understand the concepts well. You must know the specialized words of your field and be familiar with them.

After understanding the concepts, it is better to practice and repeat all the things you have learned so far so that they stay well in your mind and so to speak, watch and practice in a loop. For example, you don’t know a subject, and you go searching to learn it, and then you have to practice it and continue like this, know that the topics of artificial intelligence and machines are endless, and you must constantly seek to learn.

The fifth step: learning advanced topics of machine learning

At the end of the work, I must tell you that you should know more advanced topics such as machine algorithms, evaluation criteria, testing, testing, pre-processing, data collection and other topics that you should know well.

In machine learning, algorithms are instructions that tell a computer what to do. In some cases, they can be as simple as “if X is true, then do Y” or more complex formulas that may contain conditions and iterations.

Many algorithms in machine learning basically work by processing data points, having a specific output for each data point (e.g., classifying an email as spam or not), and using mathematical models to predict future outputs.

Now, you can get started with this extensive collection of topics.

• Supervised learning: Supervised learning is a type of machine learning in which a set of training data is given to the computer and its task is to learn how to map these inputs to desired outputs.
• Unsupervised learning: Unsupervised learning is a type of machine learning where the computer is given data, but not told what the correct outputs should be. The goal is to find structure in the data and learn from it.
• Classification: Classification is the task of identifying which category an item belongs to based on labeled samples. Examples of these labels are: spam vs. not spam, malignant vs. benign tumors, etc.
• Pattern Recognition: Pattern recognition is a machine learning task to identify patterns in data. Data includes input variables (such as pixels) and target variables (such as whether a tumor is malignant or not).
• Recommender systems: Recommender systems are programs that predict what a user wants based on a set of available preferences. This type of system is widely used in Netflix award, in search engines like Google or Bing, in social networks to predict friends’ recommendations.
• Imitation learning: Imitation learning is a method of machine learning that involves learning from representation. It uses observations of an expert’s behavior to learn how to perform tasks without any instructions on how to solve them.

Result

The field of machine learning is vast and there is a lot to learn. So, the best way to start your machine learning journey is to start with an end goal in mind, like “I want my business data to be smarter” or “I need a recommender system for my website”.

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