AI Engineer Roadmap | How I'd Learn AI in 2024

codebasics
22 Feb 202430:40

Summary

TLDRThe video provides an 8-month roadmap to become an AI engineer, requiring 4 hours of dedicated daily study. It covers core computer science fundamentals, Python programming, data structures/algorithms, SQL databases, math/statistics, machine learning, deep learning, NLP/computer vision, and LLM/Langchain. Soft skills like building an online presence and ATS resume are also discussed. Tips are provided for effective learning through more implementation/sharing and less passive video watching.

Takeaways

  • 😊 The video provides a detailed 8-month roadmap to become an AI engineer, requiring 4 hours of dedicated study per day
  • 📈 AI engineer is currently the highest paying technical role, with very high demand
  • 🧠 Strong coding and math skills are an absolute must to become an AI engineer
  • 🐍 Learn Python basics, data structures, algorithms and advanced concepts like inheritance, generators etc
  • 💻 Develop software engineering fundamentals like OS, networks, HTTP, databases etc if you don't have a CS degree
  • 📊 Spend 1 month focused just on math and statistics - linear algebra, calculus, probability etc
  • 🤖 Spend 1 month on machine learning models like regression, classification etc and MLOps concepts
  • 🧠 Consider specializing in NLP or Computer Vision by spending weeks 28-30 focused on one of these
  • 👩‍💻 Build projects, online presence on LinkedIn, portfolio and work on soft skills in parallel
  • 🚀 Lifelong learning is key for AI. This roadmap sets a strong foundation to start an AI career

Q & A

  • What are the two key skills needed to become an AI engineer?

    -The two key skills needed are strong coding skills and strong math skills. The video mentions that without skills in these two areas, you cannot become an AI engineer.

  • What is the recommended daily study time for the 8-month AI engineer roadmap?

    -The recommended daily study time is 4 hours per day for the 8-month roadmap.

  • What Python concepts should be learned in weeks 3-4 of the roadmap?

    -In weeks 3-4, you should focus on learning Python basics like variables, data types, conditional statements, loops, functions, classes etc. The video recommends going through the first 16 Python beginner tutorials.

  • What data structures and algorithms should be learned in weeks 5-6?

    -In weeks 5-6, you should learn arrays, linked lists, stacks, queues, trees, graphs, sorting algorithms, search algorithms etc. The fundamentals of time and space complexity should also be understood.

  • What key soft skills are recommended to develop in parallel with technical skills?

    -The key soft skills recommended are: building a strong LinkedIn profile, following AI influencers, commenting meaningfully on posts, developing presentation skills, learning business concepts, and mastering the art of asking questions.

  • What are some good resources for learning SQL in weeks 10-11?

    -Some good free resources for learning SQL are: Khan Academy SQL course, W3Schools SQL tutorial, SQLBolt interactive platform, and MySQL tutorial playlist on YouTube.

  • What machine learning topics should be covered in weeks 18-21?

    -The machine learning topics to cover are: data preprocessing, linear/logistic regression, decision trees, SVM, KNN, clustering algorithms, ensemble methods like random forests and gradient boosting machines.

  • What frameworks are used for building ML model APIs?

    -Popular frameworks used for building ML model APIs are Flask and FastAPI. These allow you to wrap a trained model in a web service/API that can serve predictions.

  • What tips are provided for effective learning of AI skills?

    -Tips include: spend more time implementing than just watching videos, learn concepts thoroughly before moving ahead, share your learning with friends, work on real projects, build an online presence.

  • What should be the next steps after completing the 8-month roadmap?

    -After completing the roadmap, next steps are: work on more projects, build online credibility, apply for jobs/internships, continue lifelong learning as AI is a fast-evolving field.

Outlines

00:00

🎥 Brief intro about AI engineers roles and salaries

The paragraph introduces AI engineers, stating they earn the highest salaries among technical roles. It mentions the skills needed - coding and math. It compares AI engineers to a combination of data scientists and software engineers.

05:02

📝 Roadmap PDF overview and starting guidance

The paragraph overviews the 8-month roadmap to become an AI engineer. It emphasizes avoiding scams, doing proper research, and not taking shortcuts. It mentions LinkedIn posts to avoid scams.

10:03

🖥️ Learn computer science fundamentals

The paragraph advises spending 2 weeks learning computer science fundamentals using a Khan Academy course if you lack a Computer Science background. It explains why software engineering fundamentals are critical for AI engineers.

15:04

🐍 Python programming language basics

The paragraph focuses on learning Python over weeks 3-4. It recommends specific YouTube playlists for tutorials and exercises. It advises completing the first 16 beginner tutorials and all exercises.

20:05

💼 Develop soft skills and LinkedIn profile

The paragraph parallels technical skill development with improving soft skills like building a strong LinkedIn profile. It shares a checklist to optimize LinkedIn profiles.

25:06

📊 Learn data structures and algorithms

The paragraph stresses the importance of data structures and algorithms over weeks 5-6. It links to a YouTube playlist for concepts and exercises to spend 2 weeks on.

30:06

😊 Motivational videos and advanced Python

The paragraph inserts motivational videos in weeks 7-8 when learning advanced Python like inheritance and multiprocessing. It links to the advanced section of the Python playlist.

🏢 Business concepts and asking questions

The paragraph covers improving business acumen using a YouTube channel in weeks 7-8. It recommends asking coding questions on Discord and shares tips on this art.

🌐 GitHub for version control and collaboration

The paragraph stresses learning GitHub and version control systems in weeks 7-8. It shares YouTube playlists explaining concepts clearly for beginners.

🎤 Presentation skills and communication

The paragraph focuses on presentation skills by recommending a specific YouTube video. It explains why presentations are critical for AI engineers working with stakeholders.

💾 SQL and relational databases

The paragraph covers SQL over weeks 10-11, listing specific topics to learn. It shares free resources like Khan Academy and paid courses for more extensive applied learning.

📊 Exploratory data analysis (EDA)

The paragraph focuses on EDA using pandas and matplotlib to clean and transform data. It recommends practicing EDA over weeks 12-13 using datasets from Kaggle.

🤯 Essential math and statistics

The paragraph stresses the importance of math and statistics fundamentals over 4 weeks. It lists the key topics and links to free learning resources on YouTube and paid courses.

🔎 More exploratory data analysis

The paragraph parallels technical EDA skills with communication skills via LinkedIn posts. It sets goals to analyze more datasets and share via 2 LinkedIn posts.

🧠 Core machine learning module

The paragraph focuses on machine learning over 4 weeks using a popular YouTube playlist. It also links short videos on agile methodologies used in ML projects.

🤖 MLOps and model deployment

The paragraph quickly introduces key MLOps concepts like APIs, Docker, and cloud. It states ML engineering roles often overlap with MLOPs so foundation is key.

🎯 End-to-end ML projects

The paragraph focuses on building regression and classification ML projects over weeks 23-24. It suggests customizing public GitHub code for uniqueness.

⚡️ Trendy deep learning module

The paragraph covers deep learning over 3 weeks using YouTube playlists. It explains how deep learning drives innovations like ChatGPT.

📷 Computer vision or NLP specialization

The paragraph suggests specializing in either computer vision or NLP instead of both. It shares topic details and playlist links for both domains.

🤗 LLMs and LangChain framework

The paragraph focuses on the popular LLMs and LangChain over the last 2 weeks. It states most ML roles now desire LangChain exposure.

😃 Tips for effective learning

The closing paragraph shares tips for effective learning like group studies and spending more time practicing versus consuming videos.

Mindmap

Keywords

💡AI engineer

An AI engineer is a software engineer who specializes in developing artificial intelligence systems and machine learning models. The video focuses on the high demand and career potential for AI engineers. It provides a detailed roadmap for how to acquire the necessary skills over 8 months to become an AI engineer.

💡machine learning

Machine learning is a core field within AI where models are trained to make predictions or decisions without explicit programming. Understanding machine learning techniques is critical for AI engineers to build effective AI systems.

💡programming fundamentals

Strong programming skills in languages like Python are foundational for implementing AI systems. The roadmap includes learning computer science basics, Python, data structures & algorithms.

💡data exploration

Before building models, AI engineers need to understand the data through exploratory data analysis using libraries like NumPy and Pandas. This includes cleaning, transforming and visualizing data.

💡MLOps

Similar to DevOps in software engineering, MLOps focuses on automating and streamlining machine learning workflows for production systems. This allows continuous model improvement.

💡deep learning

Deep learning uses neural networks to learn complex patterns from large datasets. Understanding deep learning frameworks like TensorFlow enables building advanced AI models like computer vision and NLP.

💡soft skills

In addition to technical expertise, the video emphasizes developing soft skills like presentations, asking questions, using version control systems, and showcasing work through portfolios.

💡career development

The roadmap focuses not just on acquiring AI skills but also actively building online credibility via platforms like LinkedIn, Kaggle and GitHub to get a job and progress in your career.

💡continuous learning

The fast pace of advancement in AI means learning is lifelong. The roadmap allocates time specifically for more advanced skills like LLM and langchain after covering fundamentals.

💡motivation

Persistence and willpower over 8 months of daily study is key. Tips like group learning, progress tracking and inspirational stories aim to drive motivation.

Highlights

AI engineers make highest salary among all technical roles

Preparing to be an AI engineer requires a lot of hard work over 8 months studying 4 hours every day

You need strong coding and math skills to become an AI engineer

An AI engineer is like a data scientist plus software engineer combined

LinkedIn helps you build relationships and get job referrals from people in the AI industry

Asking good questions and getting quality answers accelerates the learning process

Working on public resume project challenges showcases skills and personality

Math and statistics form the foundation for any AI project

Exploratory data analysis involves data cleaning, transformation, and visualization

Machine learning skills take a whole month to gain competency

MLOps focuses on automating parts of the machine learning development process

An ATS resume gets through automated screening systems used by companies

A project portfolio website showcases work samples to demonstrate skills

Deep learning is behind innovations like chatbots and language models

The learning process for an AI engineer never stops as the field rapidly evolves

Transcripts

play00:00

we are going through a gold rush

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companies have started investing

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billions of dollars in AI projects and

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there is one career role that is going

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to benefit the most out of this boom AI

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engineer or ml engineer in my company at

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lck Technologies I hire AI Engineers I

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have previously worked with Bloomberg

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and Nvidia based on my industry

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experience I'm going to discuss a road

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map with week by week study plan using

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free learning resources and checklist

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that you can use to become an AI

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engineer AI engineers make highest

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amount of salary among all the technical

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roles what I'm showing you on the screen

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is just a range the exit salary depends

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on your skills and experience the

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company that is hiring and the location

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now obviously if company's paying you

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such a high salary they will have a high

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expectation therefore preparing for AI

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engineer requires a lot of hard work if

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you're looking for any shortcut then

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please leave this video right now

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because this road map will require 4

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hours of dedicated study for 8 months

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and that will help you set a strong base

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the actual learning is a lifelong

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process before you begin the study you

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need to figure out if this is the right

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career for you and the way you can find

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it out is you need to evaluate if you

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have interest in coding and math AI job

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requires strong coding and math skills

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and without that you cannot become a

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engineer so if you don't have interest

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or skills in any of these two then don't

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go for a engineer this is not the end of

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the road because there are other career

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roles as well such as AI sales

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representative AI product manager AI

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ethics executive Etc we will not only

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talk about tool skills we will also

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discuss core skills and all learning

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resources associated with it once we

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have discussed upskilling we'll also

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discuss how you can show showcase your

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work to the world so that you can get an

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interview call and crack an interview in

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case you are confused about data

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scientist and AI engineer role think of

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it as data scientist plus software

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engineer is equal to AI engineer that's

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a very simple way of looking Ed it here

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is a road map PDF you can download it

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from video description below requires 8

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months of study 4 hours every day and

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the week zero starts with proper

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research folks there are so many scams

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going on in the market so if you buy a

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wrong course or if you learn from an

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instructor who doesn't have industry

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experience let's say or who is not

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legitimate then you will get into

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trouble nowadays you see many YouTubers

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many people teaching on online learning

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platforms they claim that their courses

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are the best but when you look at their

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background they don't have experience

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they just know how to talk nicely and

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they conduct all kind of scams we have

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created couple of LinkedIn post which

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you can look at it and make sure you

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don't get into those scams we are in

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fact running a scam awareness program

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once you have done enough research week

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one and two will go into learning

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computer science fundamentals if you

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have computer science degree you are

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covered but let's say you don't have

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Computer Science Background then I will

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suggest you go through this Khan

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academic course which covers the basics

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such as bits and bites storing text and

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numbers basic B of computer networks

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HTTP worldwide web basics of programming

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and so on once again look at this

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particular equation you need to have

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solid software engineering fundamentals

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in order to become AI engineer here is

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the course which is free and you need to

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just finish the first four modules

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remaining modules you can go over it if

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you have interest in time but the four

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modules are good enough enough Khan

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Academy is a very good platform this

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person teaches you very well along with

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the practice exercises in week three and

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four you will focus on python python is

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the most popular programming language in

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the AI world today learning python is

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actually very easy you need to start

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with these basic concepts okay and we

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have a playlist on YouTube this is on my

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channel and the other playlist is on

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Cory shaffer's Channel you can refer to

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whichever tutorials you feel comfortable

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with in this stage I would suggest you

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go through only first 16 tutorials

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because that will cover the beginner's

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logic in Python and as an assignment

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you'll have to finish all the exercises

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so if you click on this link you will

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see all the exercises okay so let's say

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there is an exercise and there is a

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solution link as well I know you all are

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sincer students so you will practice on

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your own and then only you will look at

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the exercise exercises now in this time

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period week three and four along with

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python you need to learn some soft

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skills which are those soft skills well

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you need to build a LinkedIn profile

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LinkedIn is the platform that will help

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you get a job eventually therefore you

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should not wait until you done with all

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your technical skills the approach I

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suggest is in parallel you will start

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building LinkedIn profile and all other

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softes okay we have created a check list

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that will help you make your LinkedIn

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profile stronger all you have to do is

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follow all this guideline check check

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check and once you have checked all

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these check boxes your LinkedIn profile

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will look nice once you have covered the

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python Basics week five and six you

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should focus on data structures and

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algorithms as a ml engineer or AI

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engineer you will be writing programs

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which needs to scale so you need to know

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the trade-off between memory and CPU you

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need to have deeper understanding on how

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the data structures work underneath okay

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and for that we have once again a

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YouTube playlist it's a free uh Learning

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Resource the playlist contains exercises

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as well so you can go through all the

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data structures and algorithm it will

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take you 2 weeks time period to go

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through all of these and also practice

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those exercises now this is going to be

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a long learning Journey it's very

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important that you keep yourself

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motivated for that I have included some

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inspirational videos for example in this

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video I interviewed tanul Singh who was

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a mechanical engineer and he used Kagel

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platform to become ml engineer he's

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sharing a lot of useful tips and

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insights so please go through this video

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while you are learning your technical

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skills in week seven and8 now you can

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learn Advanced python such as what is

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inheritance generators iterator

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when you writing big programs for

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Enterprises which scales well or which

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is doing a huge volume of data

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processing knowing all these concepts

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are going to be extremely beneficial

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when I was at Bloomberg we were using

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generators and iterators a lot because

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we used to deal with huge objects and

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when you're running a full loop it's

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hard to keep those objects in memory so

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using generator you can return it on the

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Fly list comprehensions are going to be

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super important for optimizing your

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program multi-threading and

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multiprocessing is very useful when you

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want to uh utilize your computer

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resources such as the course or even

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multiple processor within the computer

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to achieve a high throughput for this

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once again refer to the same playlist

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but in this uh you should go through

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video number 17

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227 and all of these videos have

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exercises so make sure you watch the

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video and cover the exercises in all

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these videos I will be talking about

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Theory then we'll be writing some code

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and then there will be an exercise in

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terms of soft skills you should start

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following some prominent AI influencers

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on LinkedIn one of them is for example

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natin he is a head of AI services at

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Google and he writes posts which will

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talk about the current trends he will

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also talk about the hiring trends that

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he's seeing because he himself hire a

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lot of AI engineers in his team I find

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reading his post to be extremely useful

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the other person is dalana she writes

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mainly on data science Ai and data

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science are kind of overlapping so

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therefore you can follow all the post on

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data science as well so follow all these

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influencers and spend let's say half an

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hour every day that way you're keeping

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yourself up to date and also you are

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becoming active on LinkedIn you should

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also start commenting meaningfully on

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those post when you comment on anybody's

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post what happens is your post if it is

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having valuable content your post or

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your comment then it will get an

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engagement okay so let's say this post

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got 155 likes some of these people who

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are giving you likes could be hiring

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managers or they could be AI Engineers

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working in other company so this way you

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are building a relationship with those

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folks and tomorrow when you're looking

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for a job maybe they will give you a

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referal or maybe they will hire you in

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their own team building relationship

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ship on LinkedIn is super important and

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you posting comments valuable comments

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okay don't post comments like this okay

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true Absolut right because that will not

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generate any engagement you're not

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adding any value but when you add some

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value to the post you are omitting your

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personality in this online world of

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LinkedIn and that will help you build

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relationship that will help you get

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attention to your profile remember that

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online presence is a new new form of

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resume along with online presence you

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need to also think about business

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fundamentals as an AI engineer you will

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be working in some industry Retail

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Finance any industry if you have good

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understanding of business Concepts it

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will help you communicate better with

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the stakeholders which are involved in

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your project to learn the business

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Concepts I will suggest you follow this

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think school YouTube channel okay so

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I'll will show you one video where he

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talked about how amul beat the

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competition and here he's talking about

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numbers and strategies and dairy

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industry in general so when you go

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through these kind of business studies

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you are building your business

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understanding you're developing a

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business acument okay Additionally you

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need to learn the art of asking

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questions Discord is a platform which

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allows you to ask questions while you

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are learning let's say python SQL

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whatever and if you have question where

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should you go one of the ways is asking

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questions in Discord server okay now

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there are many Discord servers for code

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basics for our Channel we have this

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Discord server which has around 33,000

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members okay and if you have question

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let's say uh for Math and statistics or

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let's say for machine learning you can

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post a question and the community

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members will answer those questions now

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asking questions is an art do not just

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copy paste the error that you're facing

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and ask for the help because then people

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will not help you because you're looking

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for spoon fitting the right approach is

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to look for direction not the spoon

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feeding I have highlighted that art in

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this particular LinkedIn post I have

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link linked it here you can go through

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it and your assignment for this time

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duration will be to write meaningful

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comments on at least 10 AI related

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LinkedIn post and KN down your key

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learnings from three case studies on

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things School and share them with your

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friend as in when you finishing those

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assignments you can just keep on marking

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them that way you are tracking a

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progress as an AI engineer you will not

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be working alone on a project you will

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be working with a team now how do you

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collaborate with the team how do you

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share your code with the team how do you

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review that code well the way to do that

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is via Version Control therefore you

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need to have sound understanding of

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Version Control Systems such as git G

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Hub is a website which is using git as

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an underlying Version Control System

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there is another website called gitlab

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too okay but GitHub is very popular

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develop an understanding of how git and

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GitHub works the topics you will learn

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are listed here and in terms of learning

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resources once again you can use YouTube

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on YouTube you can refer to Cory safer's

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playlist or I also have a playlist here

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and in this playlist I have explain

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things as if you are a high school

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student in a very simple language using

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a practical approach to keep the

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motivation High I have linked an

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interview of a mechanical engineer who

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became deep learning engineer using

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self- study mahad is the name of the

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person and I love his confident and the

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way he approached his entire journey is

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really inspirational so I would highly

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recommend you watch this interview when

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it comes to soft skills presentation is

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the most underrated skill I would say

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for this I would suggest you watch uh

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this Death by PowerPoint video this

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video is a gold mine it is giving you

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very simple and very powerful tips of

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how do you build effective

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presentations as an AI Engineers you

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will be working with stakeholders you

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will be in a meeting rooms you will be

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presenting all the time and if you don't

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know how to present well there is no use

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of your technical work because you're

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not able to sell your work or you're not

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able to convey your ideas in a language

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that the business stakeholders

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understand watching this video and

play14:05

preparing skills for presentation is

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going to boost your career week 10 and

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11 we need to focus on SQL and

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relational databases as an AI engineer

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we will need data to train our models

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and to do variety of operations this

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data is often stored in a relational

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database and SQL is called structured

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query language it's a language that you

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use to query data from those databases

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here you need to learn all these topics

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and in terms of free learning resources

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we have an excellent Khan Academy SQL

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course so you can go through it learn

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those skills you can also use W3 schools

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or a platform like SQL bolt which allows

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you to practice SQL while you're

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learning it so I really love this

play14:54

platform you should definitely try it

play14:55

out and then on YouTube also there are

play14:58

tons of video my channel have this

play15:01

particular video which goes through SQL

play15:03

skills uh there are so many other high

play15:06

quality SQL tutorials available on

play15:09

YouTube in case you want to speed up

play15:11

your learning and you want to learn in a

play15:13

very practical approach and also work on

play15:17

an industry project then I have this SQL

play15:20

course okay this SQL course is very

play15:23

highly rated it's very affordable and uh

play15:27

we are not only going through all the

play15:28

SQL technical fundamentals but we are

play15:30

teaching how these SQL projects are

play15:33

executed in the industry so all the

play15:35

stakeholder management skills project

play15:37

management skills are also covered for

play15:40

assignment uh you need to work on SQL

play15:43

resume project Challenge on our platform

play15:45

Cod basics. we run this free resume

play15:49

project challenges where we share

play15:51

problem statement and data with folks

play15:54

and people work on these projects and

play15:56

not only that they build presentation

play15:58

and they present it on LinkedIn so let

play16:01

me show you so here is the resume

play16:04

project challenge where you see the data

play16:06

said the mockups everything the problem

play16:09

statement so many people participate in

play16:12

this one and the winner for example here

play16:15

is Aran Sharma so if you click on this

play16:17

LinkedIn post what he did is he built a

play16:20

solution in SQL and then he created a

play16:23

linken post where he explained the

play16:25

solution that he built not only that he

play16:28

attached a video presentation where he

play16:31

was talking as if he's presenting this

play16:33

to business stakeholders now when you

play16:36

are doing this kind of activity you are

play16:39

uh showcasing your verbal your written

play16:44

uh English communication skills to the

play16:46

world let's say if a potential hiring

play16:48

manager watches this video they will get

play16:51

lot of clues about aran's personality

play16:53

his technical as well as his softes the

play16:57

fact here is that Aran literally got

play17:00

hired in a company as a data analyst

play17:02

just based on this particular resume

play17:05

project challenge so this is really

play17:07

effective it has worked for Aran and

play17:09

many other folks and it can work for you

play17:12

as well next comes numai and pandas and

play17:14

I have attached the playlist and

play17:17

learning resources for it numai and

play17:19

pandas are used for data cleaning data

play17:22

exploration those kind of things so you

play17:25

are spending just one week in learning

play17:27

this basic libraries and later on there

play17:30

will be a time period where you will

play17:32

actually practice the Eda skills

play17:36

exploratory data analysis skills then

play17:38

comes the heavy module math and

play17:40

statistics for AI math and states is the

play17:43

foundation for AI any AI project so if

play17:46

you're working as an AI engineer you

play17:48

need to have sound fundamentals in math

play17:51

and statistics now math and states is a

play17:53

vast field I have listed down all the

play17:56

topics which are need needed by an AI

play17:59

engineer okay so just focus on all these

play18:01

topics I have also linked the learning

play18:04

resources which includes Khan Academy

play18:06

scores the YouTube channels you know

play18:10

channels such as St quest uh there is a

play18:12

free YouTube playlist uh and a channel

play18:16

called three blue one brown this person

play18:18

teaches mathematics in a very Visual and

play18:21

very appealing way so just refer to his

play18:24

videos if you are interested in learning

play18:26

things like calculus linear algebra Etc

play18:29

I have also linked my math and

play18:31

statistics course here which covers all

play18:34

the fundamentals it also covers an

play18:37

industry project where we had a database

play18:39

of half a million records and we did

play18:42

hypothesis testing on the launch of a

play18:45

new credit card okay so you can refer to

play18:47

this course if you want to uh learn

play18:50

using industry Style Project based

play18:52

learning next one is exploratory data

play18:55

analysis you might have heard this term

play18:57

Eda Eda is nothing but you get all the

play19:01

data that you need for your AI project

play19:03

you need to First do some exploration

play19:05

there might be lot of bad values you

play19:07

need to clean those bad values you also

play19:10

need to perform certain data

play19:12

transformation okay so this module

play19:13

covers that the technical skills that

play19:16

you need for this are numai pandas

play19:18

matplot lib Etc which you have learned

play19:21

previously correct but in this

play19:23

particular module what I want you to do

play19:26

is go to kel.com Kel is a website which

play19:29

is hosting uh data sets and competitions

play19:33

related to Ai and here you will find a

play19:36

lot of useful data sets and also the

play19:39

problem statements so you have to go

play19:42

through some of these problem statements

play19:44

okay and practice you will see solutions

play19:48

from other folks as well but I want you

play19:50

to practice things on your own first and

play19:54

then look at the solution from other

play19:56

people so the exercise here Will be if

play19:58

initially during learning you do Eda

play20:01

using three data sets and then you work

play20:04

on additional two data sets and perform

play20:07

exploration now comes probably the most

play20:10

important module machine learning here

play20:13

you will be spending week 18 to 21

play20:15

entire month machine learning is a vast

play20:18

field and this particular segment covers

play20:21

only this statistical machine learning

play20:23

okay so you need to First cover

play20:26

pre-processing techniques and then model

play20:29

building techniques the great news here

play20:31

is that we have a YouTube playlist this

play20:34

is a playlist on my own channel it has

play20:36

received more than 2 million views I

play20:39

have explained the theory in a very

play20:41

intuitive way then there is code and

play20:44

then there is exercise so go through

play20:46

this playlist first 21 videos only when

play20:49

you get a job as an AI engineer you will

play20:52

be using some kind of project management

play20:54

tool in the industry right now scrum and

play20:57

Canan are the two popular agile project

play21:00

management techniques it will be good to

play21:03

have some understanding of scrum and

play21:06

Canan I have linked excellent free

play21:08

resources for both of it it won't take

play21:10

you much time so please go through them

play21:12

and here is the assignment you need to

play21:14

complete all the exercises in the ml

play21:16

playlist work on two Kel ml notebooks

play21:19

write two LinkedIn post on whatever you

play21:22

have learned in ml on LinkedIn let's say

play21:24

if you have learned about uh

play21:26

classification you know let's say

play21:29

logistic regression and if you have

play21:30

worked on a small problem statement you

play21:32

can write a nice summary of what you

play21:34

have learned and that will generate some

play21:36

engagement so being active on LinkedIn

play21:39

is going to be a constant requirement in

play21:41

week 22 we will be looking at mlops

play21:44

mlops is similar to devops if you are

play21:47

aware about software engineering in

play21:49

software development there is this role

play21:51

called Dave Ops where a person will look

play21:55

into uh you know automating some parts

play21:58

of a software development so they will

play22:00

be working on cicd pipelines on Jenkins

play22:04

on automating workflows integrating

play22:06

linters and many other useful tools in

play22:09

GitHub Etc similar to that ml Ops is a

play22:14

field where you are trying to automate

play22:16

some of the things in machine learning

play22:19

project development here you need to

play22:21

learn what is API and then fast API fast

play22:24

API and flask are the two popular

play22:26

Frameworks that people use to write

play22:28

server around a train model once you

play22:31

have train model you will write this

play22:33

server so that it can serve HTTP request

play22:36

coming from a client fast API is getting

play22:39

popular for which we have once again a

play22:42

free YouTube video which goes through

play22:45

all the fundamentals of fast API and you

play22:48

are creating this You Know sample

play22:50

website and calling fast API from that

play22:53

then comes Docker and kubernetes these

play22:57

two technical tools are used widely in

play23:00

the industry whenever we build any ml

play23:03

solution we usually put them in

play23:04

container and doer is something that

play23:06

helps you with

play23:08

conization and you can also use

play23:10

kubernetes for orchestration okay uh

play23:13

also make yourself aware about at least

play23:16

one Cloud platform okay AWS or Azure and

play23:20

you don't need to go crazy just uh

play23:22

fundamental understanding of how Cloud

play23:24

Works create a free uh account on either

play23:28

Azure or AWS if you're talking about AWS

play23:32

there is something called Amazon Sage

play23:33

maker that's a platform that allows you

play23:36

to do machine learning on the cloud okay

play23:38

so on the sage maker create a platform

play23:41

try to run some Notebook on sagemaker

play23:43

mlops itself is a vast topic and many

play23:46

companies have a separate mlops engineer

play23:49

role but as an AI engineer at least you

play23:52

need to have some understanding of mlop

play23:54

so don't go crazy here okay because for

play23:57

details there is mlops engineer it's a

play23:59

separate career role but as an AI

play24:01

engineer sometimes when you are working

play24:03

in a small company where there is no

play24:04

separate mlops role you will have to do

play24:07

some of the mlops all right so just

play24:09

having fundamentals clear is going to be

play24:11

super important now that you have

play24:13

learned essential skills in week 23 24

play24:16

you will be building some machine

play24:18

learning projects so I have linked two

play24:20

projects one for regression one for

play24:23

classification both of these are YouTube

play24:25

playlist end to endend projects incl

play24:27

including deployment please go through

play24:30

them in terms of soft skills you need to

play24:32

build an ATS resume don't build resum

play24:36

towards the end you can start building

play24:38

resume right now ATS stands for

play24:40

application tracking system which many

play24:42

companies are using and they will use

play24:44

this system to filter out your resume so

play24:46

make sure your resume is ATS compliant

play24:49

so that it doesn't get filtered

play24:50

automatically by ATS system we have

play24:53

created a video on this topic so please

play24:56

go through that video and and there is

play24:58

also a checklist that will help you make

play25:01

your resume ATS compliant so just go

play25:03

through all this point check check check

play25:06

and once you have checked all the boxes

play25:08

your resume will indeed be ATS friendly

play25:12

other than resume you need to build a

play25:14

project portfolio website we have linked

play25:16

some resources here so for example I'm

play25:19

going to show you one sample a project

play25:22

portfolio website this website is like

play25:24

your own website where you are writing

play25:26

about your skill

play25:28

what kind of projects you have worked on

play25:31

and you will give a link to a GitHub or

play25:34

whatever that online tool is where you

play25:36

are showcasing your work and here are

play25:39

some ideas for the assignment the

play25:41

projects that we have done on YouTube

play25:43

maybe you can start using different

play25:45

technology for example instead of flas

play25:47

use fast API okay in classification

play25:51

project uh instead of sport celebrity

play25:53

classification you can use

play25:55

classification of movie stars or maybe

play25:57

your family member pictures that will

play26:00

give your project a unique flavor and it

play26:02

doesn't look like you're just copying a

play26:04

project from YouTube now comes a very

play26:06

hot topic deep learning you'll spend 3

play26:09

weeks learning about what is neural

play26:12

network the fundamentals of

play26:14

convolutional neural network sequence

play26:16

models such as RNN Etc deep learning is

play26:20

getting very popular it is the biz of J

play26:25

llm chat GPT all the hype that you

play26:27

seeing is using deep learning underneath

play26:31

for learning deep learning there are two

play26:33

playlist I will uh refer you to so the

play26:36

tensor flow is a framework from Google

play26:39

we have this very popular playlist on

play26:43

YouTube once again exercises code Theory

play26:46

everything is covered folks all the

play26:48

learning resources are available for

play26:51

free all you need is a willpower

play26:53

motivation a computer and a stable

play26:55

internet and then comes end to end deep

play26:58

learning project for potato disease

play27:00

classification in this project we built

play27:02

a mobile app which any farmer can use to

play27:05

take a picture of a potato plant and it

play27:08

will tell you whether the plant has a

play27:10

disease or Not underneath it is using

play27:13

deep learning and convolutional neural

play27:15

network week 28 230 you can either learn

play27:19

NLP or computer vision you don't need to

play27:22

learn both there will be AI Engineers

play27:24

who will be specializing either in

play27:26

computer vision or NLP it's like you

play27:29

become a general doctor and then you

play27:31

become lung doctor or heart doctor you

play27:33

don't need to become both in terms of

play27:35

NLP these are the topics that you can

play27:38

learn there is once again a YouTube

play27:40

playlist that you can use to learn

play27:43

theory practice coding and also work on

play27:46

exercises the last two weeks of this

play27:49

entire 8 month long journey will go in

play27:53

learning llm and Lang chain these are

play27:56

the buzzword and and Lang chain is a

play27:58

framework that is getting very popular

play28:00

and if you look at any machine learning

play28:02

engineer positions nowadays majority of

play28:04

them require you to have some exposure

play28:07

to Lang chain framework so for this also

play28:11

I have a playlist where we have covered

play28:13

all the Lang chain fundamentals and we

play28:15

have built three projects three llm

play28:18

projects which you can use to learn as

play28:22

well as you can put those projects on

play28:24

your resume obviously with some

play28:25

customizations remember that in this 8

play28:28

months you have learned all the

play28:30

fundamental skills but that doesn't mean

play28:32

you have become an expert AI engineer

play28:35

the learning for AI is continuous so

play28:37

many things are happening every day

play28:40

therefore from week 33 onwards you'll be

play28:43

working on more and more projects you'll

play28:45

be working on building online

play28:47

credibility through Linkedin Kel uh and

play28:50

then you'll be applying into jobs and if

play28:52

you have prepared with sincerity you'll

play28:54

definitely get a job because there is a

play28:56

huge boom and there is lot of demand for

play28:59

people who know AI well now I want to

play29:01

share tips for Effective learning as

play29:04

well because there is lot of things that

play29:06

you have to learn and you want to make

play29:08

sure that you spend less time and learn

play29:11

effectively there are some rules for

play29:13

Effective learning for example you spend

play29:16

less time in consuming tutorials you

play29:18

spend more time in digesting

play29:20

implementing and sharing nowadays people

play29:22

do reverse they spend more time in

play29:24

watching videos and for digestion they

play29:26

spend less time it should be other way

play29:28

around if you're spending 1 hour in

play29:30

studies maybe 20 minutes or 30 minutes

play29:32

you spend in uh watching the tutorials

play29:35

and remaining time you spend in

play29:37

digesting then you implement you write

play29:39

some code and you share it with your

play29:41

friends group learning is very important

play29:43

when it comes to sharing in our Discord

play29:46

uh server you will see partner and group

play29:48

finder Channel where people say Okay I

play29:51

want to learn data science uh who wants

play29:54

to partner with me and this way people

play29:56

make groups and then they have weekly

play29:58

Zoom calls where they check progress of

play30:01

each other you know it's like a going to

play30:03

gym with bunch of friends if you go

play30:04

alone you will get bored but if you go

play30:06

in group you will stay motivated that's

play30:08

it folks I wish you all the best once

play30:10

again check video description for the

play30:13

PDF the entire PDF is included here all

play30:16

the learning resources are free I wish

play30:18

you all the best if you have any

play30:19

question Post in the comment box below

play30:22

if you like this video please share it

play30:24

with your friends we are putting a lot

play30:25

of hard work in making this video videos

play30:27

so if you can share it with your friends

play30:30

or if you like it is going to help us a

play30:32

lot thanks for

play30:39

watching