How I’d learn AI / ML in 2024 (if I could start over)

Summary

This video presents a 6-step roadmap to becoming a data science generalist in 2024. It covers essential skills such as Python programming, data structures, algorithms, and back-end engineering, all reinforced by free resources and hands-on projects. Key tools like NumPy, Pandas, and Matplotlib are introduced for data analysis, along with SQL for database management. The video also delves into machine learning, neural networks, and NLP models like ChatGPT, emphasizing unique project creation to stand out in the competitive job market. Patience and consistent effort are highlighted as crucial for success.

Takeaways

• 📚 Data science varies by company, making it essential to be a generalist with broad skills.
• 💻 Step 1: Learn Python. It's the backbone of data science, and resources like Mosh's Python tutorial are recommended for learning coding fundamentals and projects.
• 🎯 Step 2: Practice data structures and algorithms for interviews using platforms like LeetCode and Hackerrank.
• 🚀 Bonus: Backend knowledge, APIs, and tools like Docker and CI/CD pipelines can boost your CV and help with deploying machine learning models.
• 🧠 Step 3: Gain a solid understanding of probability, statistics, and basic calculus to better understand machine learning models.
• 📊 Step 4: Learn data analysis tools like Numpy, Pandas, Matplotlib, SQL, and Tableau to handle data manipulation, visualization, and databases.
• 🤖 Step 5: Dive into machine learning by learning classical algorithms using Scikit-learn and exploring neural networks with TensorFlow.
• 🔍 Step 6: Explore deep learning, especially computer vision with convolutional neural networks (CNNs) and natural language processing with transformers using Hugging Face.
• 🎓 Unique projects can set you apart from others. Collect your own data or tackle less common datasets to show originality.
• 💡 Consistency and patience are key to progressing in data science. Small daily efforts lead to success.

Q & A

What is the primary goal of the video?

The video aims to provide a step-by-step roadmap for becoming a data science generalist in 2024, including free resources and courses.

Why is it important to become a data science generalist?

Becoming a data science generalist allows individuals to have a broad skill set, increasing their job opportunities across different industries with varied data science needs.

What is the first step in becoming a data science generalist according to the video?

The first step is to become comfortable with coding, especially in Python, as coding is fundamental to solving real-world business problems in data science.

What resources does the video recommend for learning Python?

The video recommends the Python Tutorial by Mosh, which covers Python fundamentals like variables, loops, and advanced topics such as classes and inheritance. It includes three hands-on projects.

Why is learning data structures and algorithms important for data scientists?

Although data structures and algorithms may not be part of day-to-day tasks, they are essential for technical interviews. Platforms like LeetCode or HackerRank are suggested for practicing these skills.

How can backend engineering knowledge benefit a data scientist?

Backend engineering skills are helpful for deploying machine learning models to production, a process many data scientists struggle with. It also makes a CV stand out by showing versatility.

What math concepts are essential for data science, and why?

A solid understanding of probability, statistics, and basic calculus is important for data science, particularly for tasks like hypothesis testing and data transformations in machine learning.

Which tools and libraries are recommended for data analysis in the video?

The video recommends tools like NumPy for matrix operations, Pandas for data manipulation, and Matplotlib for data visualization. It also suggests learning SQL for database interaction.

What machine learning resources does the video suggest?

The video suggests the Machine Learning Specialization by Deep Learning AI, which covers classical machine learning models, neural networks, and advanced concepts like clustering and recommendation engines.

Why are unique projects important for a data science resume?

Unique projects help candidates stand out from the competition, as many people have similar projects from online courses. Personal projects that involve collecting data and solving real problems demonstrate applied skills.