Whether someone is a first-year student at university, pursuing a master’s, or a Ph.D., regardless of the stage they are in, if someone wants to work as a Machine Learning engineer in a big company like Google, Meta, Uber, or Amazon, they must define themselves as a Machine Learning engineer and develop expertise in the field. It’s possible that one might not want to pursue a job immediately but still have an interest in learning Machine Learning.

First, it’s important to understand the difference between a Machine Learning engineer and a Software engineer. Software engineering encompasses various sectors working with different technologies, such as iOS engineers and Android engineers who design and develop apps, as well as backend engineers who handle various system tasks.

 On the other hand, Machine Learning engineers are specialists who utilize various ML techniques for prediction and data analysis tasks. They leverage AI techniques to automate systems. To excel as a Machine Learning engineer, one needs to be proficient in software engineering, which serves as the foundation.

To become a proficient software engineer, it’s crucial to focus on three main areas:

1. Data structures
2. Algorithms
3. Problem-solving skills
4. Proficiency in programming languages

However, in this article, we will discuss how to start learning Machine Learning.

**Step 1:** 

1. Choose a programming language – preferably Python.
2. Install Anaconda and Jupyter Notebook.
3. Learn libraries:

   – “Pandas” for data processing.

   – “Scikit-learn” for using ML algorithms.

   – “NumPy” for performing a wide variety of mathematical operations on arrays.

**Step 2:** 

Mathematics for Machine Learning:

1. Statistics (basic understanding of probability and statistics).
2. Linear algebra (dot products, distance, matrix factorization, eigenvalues).

**Step 3:** 

Machine Learning Algorithms:

1. Slope
2. Gradient descent
3. Reinforcement learning
4. Supervised vs. unsupervised learning
5. Clustering
6. Basic linear regression

Database Knowledge:

1. Structural programming language (SQL).

**Step 4:** 

Learn Deployment:

1. Deploying ML models.
2. Frameworks.
3. Custom web apps.

**Step 5:** 

Books:

1. Cookbook (Machine Learning with Python): This book teaches how to train Machine Learning models using proper data with Python programming language. 
2. Machine Learning for Absolute Beginners: This book covers basic concepts of ML models.