This article is a complete guide to becoming an AI/ML Engineer with the best free resources. Enjoy learning!

Why should I become an AI/ML engineer in 2024?

Become an AI/ML engineer in 2024 to shape the future. AI is revolutionizing industries, from healthcare to finance. As an AI/ML engineer, you'll solve complex problems, develop innovative solutions, and work with cutting-edge technologies. High demand, excellent salaries, and the opportunity to create impactful products make it a thrilling career path

How can I become an AI/ML Engineer?

Here are the steps you need to take to become an AI/ML Engineer in 2024.

1. Get started by learning Python (18.5 hours)

Python is the go-to language for AI/ML. Learn data structures, algorithms, and object-oriented programming. Here are some of the best resources that you can use.

You can use the course below for Python.

For Data structures and algorithms you can use the course below.

For object-oriented programming, you can use the below course.

2. Hands-on with Python's most important libraries (7.5 hours)

Some of the most important libraries are NumPy, Pandas, and Matplotlib. These are the most basic libraries that you'll use as an AI/ML Engineer very frequently.

NumPy course:

Pandas complete course below.

Matplotlib course below.

3. Diving into the Machine Learning (23 hours)

Now you're ready to start with machine learning.

Basics of Machine Learning(45 minutes)

Diving deeper into Machine Learning(22 hours)

4. Deep Learning (40 hours)

Deep learning is a subset of machine learning that utilizes artificial neural networks to learn complex patterns from large amounts of data. Inspired by the human brain, these networks consist of multiple layers of interconnected nodes, allowing them to process information hierarchically.

Congratulations on learning ML!

Now, it's time to apply your knowledge and build a strong career. Here are some potential paths:

1. Deepen Your ML Expertise:

• Specialize: Focus on a specific area like deep learning, natural language processing (NLP), or computer vision.

• Advanced Techniques: Explore ensemble methods, reinforcement learning, or generative models.

• Research: Contribute to the advancement of ML by conducting research or publishing papers.

2. Build a Strong Portfolio:

• Personal Projects: Develop ML projects to showcase your skills.

• Kaggle Competitions: Participate in competitions to learn from others and build a strong profile.

• Open Source Contributions: Contribute to open-source ML projects.

3. Master Essential Tools and Technologies:

• Cloud Platforms: Learn to use cloud platforms like AWS, GCP, or Azure for ML deployment.

• Big Data Tools: Gain proficiency in tools like Hadoop, Spark, or PySpark for handling large datasets.

• MLOps: Understand ML operations to deploy and maintain ML models in production.

4. Network and Build Connections:

• Attend Conferences: Connect with industry professionals and learn about the latest trends.

• Online Communities: Participate in ML forums and communities to share knowledge and learn from others.

• Mentorship: Seek mentorship from experienced ML practitioners.

5. Explore Career Paths:

• Machine Learning Engineer: Develop and deploy ML models in production.

• Data Scientist: Extract insights from data using ML techniques.

• AI Researcher: Conduct research to advance the field of AI.

• ML Product Manager: Oversee ML product development.

6. Continuous Learning:

• Stay Updated: The field of ML is rapidly evolving, so keep learning and stay updated with the latest trends.

• Online Courses: Take advanced courses to enhance your skills.

• Certifications: Consider obtaining certifications like AWS Certified Machine Learning - Specialty.

Remember: Practical experience is crucial. Combine your theoretical knowledge with hands-on projects to build a strong foundation for your ML career.