AI Engineer Roadmap – How to Learn AI in 2025

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Summary

The AI Engineer Roadmap provides a comprehensive guide for aspiring AI professionals to evolve their skills in anticipation of 2025's demands. Covering essential topics like mathematics, machine learning, deep learning, and large language models, the roadmap highlights the areas necessary for mastering AI engineering. This detailed guide outlines the intersection of software engineering, machine learning, and data science to build and deploy AI systems. It emphasizes real-world applications across industries such as healthcare, finance, and entertainment, making it a critical tool for those striving for a successful AI career.

Highlights

AI engineering bridges the gap between software engineering, ML, and data science. 🤖
The roadmap guides you through becoming a world-class AI engineer. 🗺️
It covers essential math, including calculus and linear algebra, for AI expertise. 📚
Skills in data science are crucial for AI model optimization and ethical development. 🔧
Explore real-world applications in diverse fields, from healthcare to entertainment. 🎨

Key Takeaways

Embark on an AI journey by mastering essentials like math, ML, and deep learning! 💡
AI engineering is about integrating models to solve real-world problems. 🌍
High demand for AI engineers makes it a lucrative career path. 💼
AI's impact spans healthcare, finance, and more, offering diverse opportunities. ✨
Prepare for 2025's AI landscape by focusing on large language models and generative AI. 🚀

Overview

AI engineering is becoming a vital field, merging software engineering, machine learning, and data science to build systems that solve real-world problems. The AI Engineer Roadmap by freeCodeCamp.org is designed to transform an aspirant from beginner to expert, arming them with skills needed by 2025. This includes foundational mathematics, machine learning, deep learning, and large language models. It underscores AI's pivotal role in various industries, creating new pathways and opportunities for engineers.

Learning AI engineering involves a mix of theoretical knowledge and practical skills. Mathematics like calculus and linear algebra form the core, while game theory and statistics enhance model understanding. Data Science skills ensure that AI applications are relevant and ethical. The roadmap highlights how these elements fit together in the broader tech ecosystem, ensuring success across multiple industries, from finance to healthcare, entertainment to autonomous vehicles.

The AI engineering landscape for 2025 promises exciting developments with large language models and generative AI at its core. For those willing to engage with comprehensive learning and real-world applications, the journey requires rigorous study, practical projects, and an understanding of AI ethics. It's a challenging but rewarding path, leading to substantial career opportunities and contributing to the technological revolution. This roadmap provides the strategic guidance needed to navigate this evolving field.

Chapters

00:00 - 00:30: Introduction to AI Engineer Roadmap The chapter 'Introduction to AI Engineer Roadmap' outlines a journey from core fundamentals to advanced AI implementations, covering essential mathematics, machine learning, deep learning, and large language models. It is designed to equip individuals with the necessary skills to succeed as an AI engineer by 2025. This roadmap caters to both beginners and those upgrading their skills, providing hands-on experience and industry-relevant insights, courtesy of Lunar Tech.
00:30 - 01:00: Overview of Course and Its Importance The chapter titled "Overview of Course and Its Importance" introduces the significance of the AI Engineering Road Map for 2025. The course is presented by D Vasan from Lunar Tech, highlighting its relevance at the intersection of technology and innovation. The introduction sets an enthusiastic tone, emphasizing the transformative impact of AI engineering in the current era.
01:00 - 02:30: Defining AI Engineering This chapter delves into the concept of AI Engineering, exploring what it entails and its role within the broader fields of AI and data science. The focus is on laying out a step-by-step roadmap to becoming a world-class AI engineer, emphasizing the necessary skills and knowledge required to succeed in the industry.
02:30 - 06:00: Applications of AI Engineering This chapter explores the transformative impact of AI engineering across various industries. It will help you differentiate between essential skills and those that are beneficial but not critical. The chapter also provides a step-by-step guide to mastering the necessary skills, outlining essential topics to ensure you become job-ready in the field of AI.
06:00 - 07:30: Career Opportunities and Salaries The chapter titled 'Career Opportunities and Salaries' delves into the fundamentals of AI engineering. It begins by addressing the basics of what AI engineering entails. This session promises to be full of unique insights and practical advice that are not readily available elsewhere, setting up the reader to gain new knowledge on the topic as the chapter progresses.
07:30 - 15:00: Essential Skills for AI Engineers The chapter 'Essential Skills for AI Engineers' discusses the role of AI engineers in designing, building, and deploying AI systems to address real-world problems. It emphasizes the intersection of software engineering, machine learning, and data science, highlighting how AI engineers operationalize models developed by data scientists to function effectively in practical applications.
15:00 - 21:30: Mathematics for AI Engineering The chapter titled "Mathematics for AI Engineering" discusses how advanced AI models are utilized in real-world applications to process data, make decisions, and provide actionable insights. An example given is in the healthcare sector, where a data scientist creates a machine learning model for tumor detection in X-rays. The AI engineer enhances this model by integrating it into hospital systems to ensure it operates in real-time and functions reliably under various conditions.
21:30 - 30:00: Statistics for AI Engineering The chapter titled 'Statistics for AI Engineering' delves into the integration of various disciplines including data science principles, system design, optimization, machine learning, and deep learning, which collectively form the field of AI Engineering. It emphasizes that AI engineering transcends the mere construction of models; it is about ensuring that these models effectively solve real-world problems and provide tangible value to businesses or public enterprises. The chapter underscores the significance of AI engineering in the contemporary technological landscape.
30:00 - 35:00: Data Science Skills for AI Engineers The chapter titled 'Data Science Skills for AI Engineers' explores the intersection where cutting-edge data science research meets practical industry implementation, focusing on AI engineering. It highlights the importance of bridging the gap between research and actual engineering across various industries, with a specific example of AI's impact in healthcare, where AI systems are utilized for transformative purposes.
35:00 - 45:00: Traditional Machine Learning Skills This chapter delves into the various applications of traditional machine learning skills across different sectors. It highlights the role of AI in healthcare, specifically in analyzing medical images, predicting patient outcomes, assisting doctors in drug discovery, and patient care. AI engineers are responsible for building systems that are scalable, reliable, and efficient for real-world application. The chapter then shifts focus to the finance sector, where AI plays a crucial role in fraud detection and algorithmic trading by processing massive amounts of financial data in real-time. The engineers in this field focus on developing solutions that can handle the demands of the financial industry.
45:00 - 55:00: Deep Learning Skills This chapter discusses the application of AI in creating secure, efficient, and real-time systems to handle sensitive information, with a focus on facial recognition technology in retail and e-commerce platforms like Amazon. AI is used to personalize recommendations, optimize pricing, and manage inventory in these platforms. It also highlights how AI engineers develop algorithms and systems to enhance user experiences in entertainment sectors, including streaming platforms like Netflix, which use AI for personalized content delivery.
55:00 - 65:00: Python and Advanced Programming Skills This chapter discusses the impact of tools like Dolly and chatbot CHBT on content creation, indicating a shift in how creators produce content. It also highlights the role of AI in autonomous vehicles, specifically self-driving cars, where AI is crucial for navigation, object detection, and decision-making. AI engineers are essential in designing algorithms and hardware integrations that ensure these autonomous vehicle systems are safe and reliable.
65:00 - 84:00: Generative AI and Large Language Models The chapter discusses the impact and growing importance of AI engineering across various industries. It emphasizes that AI engineering is not just for tech; it is relevant in sectors like healthcare, finance, tech, defense, and other creative industries. This versatility and applicability are why AI engineering is gaining popularity and is expected to become one of the most important professions in the coming decade.
84:00 - 558:10: Final Remarks on AI Engineering AI engineering salaries are highly competitive, ranging from 80k to 180k for mid-level engineers in the US and going up to 750k for senior roles.

AI Engineer Roadmap – How to Learn AI in 2025 Transcription

00:00 - 00:30 this AI engineering road map takes you from core fundamentals to Advanced AI implementations it covers essential mathematics machine learning deep learning and large language models providing you with the exact skills needed to thrive as an AI engineer in 2025 whether you're starting fresh or upgrading your skills this road map offers a clear path to success with hands-on experience and Industry relevant insights T from lunar Tech
00:30 - 01:00 developed this course imagine being at the Forefront of one of the most transformative fields of our time where technology meets Innovation and changes the world welcome to the AI engineering road map of 2025 my name is D Vasan from lunar Tech and I'm absolutely exciting to be here with you today to dive into this highly requested topic together we will will
01:00 - 01:30 explore everything that you need to know to navigate this exciting world of artificial intelligence and AI engineering to set yourself up for success in this field in this video we are going to break down the step-by-step road map for becoming a worldclass AI engineer here is what we are going to cover first we will Define what AI engineering is and how it feds into this broader ecosystem of AI and data science next we will explore the real world
01:30 - 02:00 applications of AI engineering showcasing its really strong power transformative impact across different Industries then we will dive into the must have versus nice to have skills helping you to understand exactly where to focus your efforts and your time finally we will go to step-by-step process so the skill sets that you need to master outlining the essential topics to help you become a job ready AI
02:00 - 02:30 engineer this session is packed with unique insights and practical tips that you won't find any URS so stay tuned without further Ado let's get started so let's start with the basics what is AI engineering AI engineering is
02:30 - 03:00 this practice of Designing building and deploying AI systems that solve real world problems it sits in this intersection of software engineering machine learning and data science and here is how it fits into this broader Tech world and the ecosystem so the data scientists often focus on analyzing data or predicting something or developing models AI Engineers take these models and make them work in the real world
03:00 - 03:30 settings and with much more advanced models they create systems that process data make decisions and deliver actionable insights for example in the healthcare a data scientist might develop a machine learning model to detect the tumors in x-rays an AI engineer brings this to the next level he ensures that the model is integrated into Hospital Systems runs in real time and works reliably under different conditions also AI Engineers they work
03:30 - 04:00 with much more advanced models like deep learning models or neural network based models so data science principles system design optimization machine learning deep learning is what all combines into one place which is AI engineering it's not just about building models it's about making sure that those models actually solve problems and deliver value for the business or this public Enterprise and that's why AI engineering is such a critical role in today's Tech
04:00 - 04:30 ecosystem it's where this Cutting Edge research meets the Practical industry impactful implementation so bridging this gap between the research and the actual engineering so um AI engineering isn't just limited to one field it's changing Industries all over the world let's look actually at some of the examples how AI engineering is making an impact first up is the healthcare so AI systems are used
04:30 - 05:00 to analyze medical images predict patients outcomes and also assist the doctors in the drug Discovery or the patient care AI engineers build the systems to ensure that those are scalable reliable and efficient for real world use next up is the finance from fraud detection to aloric trading AI processes massive amount of financial data in real time engineers in this field they focus
05:00 - 05:30 on creating secure efficient and realtime systems that can handle this sensitive information real time like FR detection in the retail and e-commerce in the platforms like Amazon they use AI to personalize recommendations optimize pricing and manage inventory AI Engineers they design algorithms and systems that drive this experiences next up is the entertainment of course the streaming platforms like Netflix they rely on AI for personalized content
05:30 - 06:00 recommendations jna tools like Dolly and chatbot chbt are changing now how the creators produce content next up is the autonomous vehicles so self-driving cars they depend on AI for navigation object detection and decision making AI Engineers they are the ones who design this algorithms and Hardware integration to make this autonomous Vehicle Systems safe and reliable so these examples are
06:00 - 06:30 just few of them and they show how different and impactful AI engineering is so whether you are passionate about health care Finance Tech defense or any other creative industry there is a place for you in this field and that is actually why the AI engineering is so popular this day and it's going to be one of the most independent Professionals in the next decade there are many Industries and companies who are currently Hing when it comes to the
06:30 - 07:00 salaries for AI Engineers those are highly competitive just 40 ENT roll they start around 80 up to 120k at least for the midlevel engineers this is uh 120k to 180k in us and where senior roles this can take all the way from 200 up to 750k in the US dollar so let's now get into the actual skill set that you must
07:00 - 07:30 know in order to become an AI engineer and here I'm talking about becoming a worldclass well-rounded real AI engineer not just someone who does promp engineering real AI engineer not just someone who does promp engineering and without knowing these different models uh just uses them but actually becomes someone who will create new algorithms who will create their own unicorns or will become an AI and without knowing these different models uh just uses them
07:30 - 08:00 but actually become someone who will create new algorithms who will create their own unicorns or will become an AI engineer that works at this uh large Cutting Edge companies like open AI Tesla meta and many other Cutting Edge startups so first up is of course the mathematics mathematics is a Fiel when it comes to traditional machine learning all the way to the most Cutting Edge AI that you see nowadays so um when it comes to mathematics there are different
08:00 - 08:30 topics from this field that you must know not the entire universe of mathematics or the super advanced stuff but really the fundamentals and um these are selected topics from different uh levels so you cannot just say first level of University or second level of University of that specific study no it's a combination of these different levels from this different fields and studies that you need to combine in one place learn it such that you can move on
08:30 - 09:00 on to the next page and today I will tell you which are those in a more detail such that you are left with a specific topics for you in mind to learn mathematics if you decided to do a self-study and become an self faced AI engineer on your own so first up is the high school mathematics in here um you can understand doing basic divisions how to solve an equation with uh squared unknowns so for example a square plus something you are able to uh calculate
09:00 - 09:30 the discriminant to find the solutions to that equation you know this different um geometric um terms like what is sinus what is cosine what is tangent what is cotangent uh the Pythagorean theorem um basically all the topics from the high school all the way to the last level next up is the uh linear algebra of course linear Al ra comes usually from
09:30 - 10:00 the second uh year of econometric study or applied mathematical and statistical studies and this field is really important for understanding not just the traditional machine learning but also the Deep learning which is really important and it's a more advanced type of ml that powers today's most cutting gge applications including the GPT models the Transformers Etc so if you want to know and understand the cycle of n networks the training how it's being
10:00 - 10:30 optimized and how this entire neural networks structure works then you must understand linear algebra so when it comes to linear algebra let me tell you specifically what I mean not the entire linear algebra but really to understand the norm of a vector this understanding of vector and matrices the cartisian coordinate system that comes from um the high school but then here is also very relevant to understand where the vector are how you can position the vectors in
10:30 - 11:00 the cian coordinate system understand this idea of Norm versus alal and distance the uh Pythagorean theorem here again the orthogonality um you also need to understand the vectors and operations so foundations of the vector the special vectors unit vectors um and also uh the idea of dot product the application of the dot product the C squares equation also you need to understand the matrices
11:00 - 11:30 and the solving of the linear systems using this idea of matrices so here you need to have the foundations of linear systems and matrices you need to uh be able to add matrices multiply them to compute a DOT product between matrices or between Matrix and a vector um also understanding of ging reduction the reduced ulum form the row reduced ulum form the no space the c space the rank
11:30 - 12:00 the full rank this all will be foundation for you to understand how this their networks work um if you truly want to understand um the different deep learning and AI models you also need to have a good basis when it comes to linear transformation and matrices so this algebraic lows for matrices uh including how um it actually works how you can uh solve a system with the linear equations multiple of them using
12:00 - 12:30 these different Transformations so what is for example the transpose of a matrix what is the inverse of a metrix and apply these different uh rows and the rules from linear algebra uh also what is the determinant how you can calculate it what are the properties of determinant the transpose of matrices I believe I just mentioned and then you also need to understand some topics from Advanced linear algebra like uh the projections of vectors um the gr Schmid
12:30 - 13:00 process the infamous process that you um need to understand uh the metrix factorization really important not just for the Deep learning but also for the traditional machine learning or the things like metrix uh factorization that is used in the recommender systems so uh this part is also very important to understand the QR de composition ion values igon vectors uh which is really important for understanding the principal comp quasis and dimensionality reduction also the
13:00 - 13:30 igon de composition which is based on igon values and igon vectors and understand the singular value the composition or the SVD which is really important part as part of traditional machine learning so um this is what uh you need to know when it comes to the linear algebra and if you are looking for that one place to learn linear algebra then uh last year uh we have published an entire 26 plus hour course that covers
13:30 - 14:00 all these topics in one place it was quite a popular course uh and highly demanded one and you can get also a certification once you completed so check out this course the fundamental s linear algebra uh at the lunch. to also uh go through all these topics uh follow it study it practice it and then get also a certification next up when it comes to mathematics Beyond um the linear algebra
14:00 - 14:30 and the um High School mathematics you also need to understand calculus this one is really important as well uh you will need to have an understanding what are the gradients what are the derivatives how you can calculate derivatives how you can calculate the integrals not just with one n but with two variables basically so double integrals um how you can uh use this uh derivatives and integrals when comes to optimization this uh concept of the
14:30 - 15:00 slope and uh optimization of the models using the gradients first order gradient and second order gradient in the context of it how you can adjust the parameters for better accuracy and um just a traditional calculus one and some calculus 2 so um this is um no-brainer when it comes to AI not just for advanced AI but for the traditional machine learning learning for understanding these different models
15:00 - 15:30 you must know calculus next up is the game theory not the entire universe of Game Theory not all the topics but there are some topics from Game Theory which usually comes from third year of econometrical or ply mathematical studies is something that you must know think about NES equilibrium or the mean Max strategy or this um um this game where um competing is actually resulting in worse outcome than
15:30 - 16:00 collaborating so uh this idea of NES equilibrium is really important for understanding one of the foundational generative AI models which is the generative adversarial networks so for understanding one of this Genna models you will need to also have this uh couple of topics from game theory in place all right so that's about the mathematics um and here I'm also not mentioning this foundational geometry topics which is usually also covered as
16:00 - 16:30 part of high school so once again the sign cosine the tangent how to work with with the different um angles the 90° angle what are these different values for different angles and this common notation with the pi so what the pi represents the radians Etc once you comfortable with this mathematical topics the next topic that I would suggest you to study is the statistics statistics is very important when it com comes to becoming a well-rounded AI
16:30 - 17:00 professional to understand the um idea of predicting the next word but all the way to the very basic machine learning uh having this basics of Statistics will be very helpful to you so here is the list of topics that I would suggest you to study when it comes to statistics so first up of course understanding this concept of probabilities to know what the probabilities are what is its concept uh why it is used for this
17:00 - 17:30 concept of probability distribution functions the PDFs the cumulative distribution functions or the cdfs and also um to understand uh what is this idea of sample why we use sample um versus population um this idea of having a representative sample work with the data so understanding for example what are the random variables what is this idea of experiment uh what are the probabilities um the uh criteria and
17:30 - 18:00 qualities of probabilities what is the PDF or the probability distribution function uh what is the cumulative uh distribution function this uh basic statistics like the mean the median the variance the standard deviation the mode um and also how they can be calculated this um idea of covariance and correlation what is the difference between correlation and cation uh understanding um how these different
18:00 - 18:30 statistics can be used to describe your data and to tell a story about your data and um also this idea of Sample versus population why we use sample um and why we um are unable for example to deal with a population um and how this becomes relevant when it comes to this entire universe of data science um also understanding the bias theorem the
18:30 - 19:00 different rules when it comes to the probabilities like the conditional probability the idea of Independence between different random variables um then I get into some Bic probability distribution functions especially the normal distribution function the baroli distribution function this idea of boli Trials the binomial distribution function what is this connection between bomal distribution function and the binomial distribution function how it is used in these different concepts like
19:00 - 19:30 tossing a coin so basic statistics basically uh also understand uh the idea of uh linear regression and ordinary Le squares what are these different uh conditions and assumptions that this ordinary squares is making when calculating and optimizing these different um parameter estimates this idea of estimation versus um the unknown parameter the idea of error terms the
19:30 - 20:00 error terms versus residuals um and also this concept of gas Mark of theorem how it is used um and this comes usually from econometrics and the idea of parameters what are the properties of parameters like the bias of a parameter the consistency and the efficiency and this is again tied back to the gas Mark of theorem uh also the understanding of confidence interal will be really important in your career in the field of science and AI the idea of
20:00 - 20:30 95% confidence interval how it's calculated what is this idea of um calculating this interval the lower bound and the upper bound what it means another very important topic from statistics is this idea of hypothesis testing why we need hypothesis testing the idea of null um uh hypothesis the alternative hypothesis how you set up these experiments why it is important why we even need it the concept of statistic iCal significance is very
20:30 - 21:00 important how to calculate type one error type two error what is the difference between them what is false positive what is false negative uh the statistical test like the student T Test the F test Anova test uh the uh two sample T Test the two sample normal test there are so many test that um it would that um can be studied in this field of Statistics but there are a couple of of them that I uh selected and um I would
21:00 - 21:30 also provide you the links to that and you can also check them out and I would highly suggest you to study them also this concept of P value is um very uh essential uh also this uh calculation of the P value how you can use it how to interpret it its limitations and also this concept of inferential statistics so blows like the central limit theorem the of large numbers how it is used when it comes to
21:30 - 22:00 this uh experiments and this is tied back to the uh normal distribution function one of the most INF famous distribution function that you must know as an AI engineer next up we have the dimension reduction techniques like the principal component analysis or the factor analysis and you can also add here the panical correlation nysis so a CCA so if you are looking for that one place that in organized way can help you
22:00 - 22:30 to refresh your memory or to study all this in one place then you can also check out our fundamentals to statistics course because we are covering there all these different topics which is a prerequisite and it's a must for you to know before you get into the next level in your AI engineering Journey so once you're comfortable with the mathematics and statistics you are ready to move on to the next step in your journey of becoming an AI engineer the next skill set is the skills of data science so as
22:30 - 23:00 an AI engineer you really need to have a good data science skills without good data and without understanding whether you even have a good data or not and applying your data science skills um any of other skills won't matter because um it's this phrase that is really uh easy to remember you can have a great AI model but if you put a garbage in you will get a garbage out and that uh what you put into your AI model is your data
23:00 - 23:30 if your data is a trashy is a bad data and sometimes you don't even know that you are dealing with B data because you don't have the data science skills then it doesn't matter how much effort or how much money you will put in your um AI model how much gpus you will use or um how big your data will be if your data quality is a bad one to understand these data skills you will need to have a data science skills so what I mean by that so
23:30 - 24:00 when it comes to um AI models they like to work and they are performing good if they are dealing with the clean data your AI models also need to use a meaningful data a relevant one and also as an AI engineer you are responsible for the um for the ethical side of your model and for that your data should be uh unbiased as well so um as an AI engineer you will need to understand how to clean data how to Source data how to
24:00 - 24:30 collect it if you don't have an AI engineer next to you and also how to pre-process data and here I mean identifying the uh Missing data in your database to understand what is the mechanism behind it is it missing a trandom is is it missing not a trandom because this will then define whether you can impute the data so you can fill in this missing data what kind of techniques you can use to fill in this missing data or maybe to drop it all together to understand whether you have
24:30 - 25:00 uh anomalies in your data outliers how you can use statistical and other techniques to find this outliers in your data and to remove it or maybe adjust it this concept of normalization you will need to have a good understanding how you can filter your data how you can um group your data um tell story about your data before you even get into the model development section and how to uh split your data to have the skills of um
25:00 - 25:30 following the cycle of data preparation data evaluation and also using the data as an input for your model whether it's a machine learning deep learning or an advanced generative AI model also understanding how to uh visualize your data is really important as a data scientist you usually learn the um exploratory data analysis and how you can use these different tools includ including Python and simple libraries
25:30 - 26:00 like Seaburn and metli to visualize your data and as a data science skill uh this is a must to also identify outliers to identify certain Trends and also to tell a story about your data so this is the basically the pre-work that you need before you get into any moral development if you want to do everything properly and as a professional you also need to understand uh Fe engineering skills which also is a data science
26:00 - 26:30 skill so understanding how you can create new variables so sometimes for example you have multiple variables but it's not good enough because you just need one and it's usually a combination of this multiple variables and by understanding how you can combine different variables in your database in one place and uh create one single variable is what we are referring as a feature engineering so you engineer the features that then you can use as an
26:30 - 27:00 input to your machine learning or your deep learning or your AI model in general so this is about the data science knowing data signs uh will be um will set you for Success when it comes to AI engineering career next up is the infamous traditional machine learning so without understanding traditional machine learning there is no way to beable arounded AI engineer um if you don't want to be in this
27:00 - 27:30 position where for every single problem you use neural networks use you waste your company's money on the gpus or uh you spend a lot of time on using complex models that while you can use a simple machine learning models if you don't understand this then you can never become this AI engineer that uh looks at problems not just from a research perspective but also from business or Enterprise perspective so um that's why I always suggest to
27:30 - 28:00 First Master the traditional machine learning and then only get into the next point so here what I mean by traditional machine learning I mean to um understand this concept of classification regression supervised learning unsupervised learning these different algorithms that fall under these categories like uh linear regression logistic regression decision trees uh bagging boosting XG boost uh light GBM
28:00 - 28:30 GBM and uh many other models including unsupervised models like K means hierarchy Cloud string or DB scan in which cases which of your models you can use the idea is that once a PM or a business leader comes to you and tells you this vague business problem you as an AI engineer you will need to quickly uh be able to figure out whether you are dealing with a classification problem regression problem maybe an unsupervised
28:30 - 29:00 learning program and you will also need to have this uh quick understanding okay I'm going to use most likely this models in order to solve that problem and being able to understand this will be really important before you move on to any advanced moral uh studying so um Beyond understanding the algorithms and if I believe if I remember correctly those are about 23 or
29:00 - 29:30 24 algorithms from traditional machine learning understand their mathematics behind the statistics behind it what are their benefits what are their disadvantages because in each of these categories you also need to understand how each of these models work and um have this understanding that for this type of problems for example when you have a lot of missing data you can use that model because it's more stable or if you are dealing with a data that follows normal distribution then you will then you can better use another type of model cuz for each of this
29:30 - 30:00 classification regression or other type of problems you will have many options and it's up to you as an AI engineer to short list them and also from that to filter out which one you will use so beside this you also need to understand how you can evaluate a tradition machine learning model what is this common cycle of the training testing validation what are these different sampling techniques or resampling techniques uh what is bootstrapping what is cross viation what
30:00 - 30:30 is kold cross viation or leave one out cross viation and also to understand what are the different evaluation metrics depending on your problem you can use in order to evaluate your model for example what is the difference between using the mean absolute um error versus the mean squared error in which cases you can use which one or are or the root mean squared error or um how you can evaluate a model that is in the field of classification it is the F1
30:30 - 31:00 score um or it's the fbaa score which is more General version of the F1 score should you use recall should you pay more attention to the Precision Etc so uh understanding also when to use machine learning when to use uh just rule based approach will be also important for you as an AI engineer so um that is about machine learning if you want to uh Master the field of machine learning and everything that I just mentioned in one place you can also
31:00 - 31:30 check out our fundamental to machine learning course where we cover everything that you must know in order to become a well-rounded machine learning specialist you can also get a certification from lunatech Once you complete your machine learning course so once you are comfortable with mathematics statistics and the traditional machine learning next up is studying the Deep learning deep learning is at the heart of the Modern Art artificial intelligence especially when
31:30 - 32:00 it comes to generative AI so all these different Cutting Edge tools like the chat gbt The Dol Sora or the um different applications the um self driving cars the uh robots humanik robots they are all based on narrow networks and narrow networks is this fundamental part when it comes to deep learning think of the deep learning as more advanced machine learning where the models are able to study better uh with
32:00 - 32:30 a larger amount of data and this big data that uh the size of which increased more and more in the last decade made the evolution of the deep learning more possible so when it comes to the Deep learning what I mean exactly is that you need to understand how the Deep learning differs from the traditional machine learning you need to understand the architecture of neural networks uh and how it works the concept of neurons the
32:30 - 33:00 perceptor this uh um in a simple way to be able to understand the structure of neural networks the activation functions what it means this difference between different activation functions um and also understand in which cases to use what this idea of hidden layers input layer output layer um how they are related to the performance of neural network um you also need to understand the concept of for forward PA backward pass the idea of B propagation what the
33:00 - 33:30 B propagation algorithm does the idea of loss function how you can calculate the loss function for a neural network also how the training of neural network works so how it starts from the input then it goes to the forward path then does the uh the loss calculation the back propagation Etc and also what is this idea behind it and how using each of these different making each of these different decisions like the activation function or the uh different
33:30 - 34:00 optimization algorithms how it will be impacting the performance of your deep learning model also understanding the different optimization algorithms like the gradient descent stochastic gradient descent the RMS prop uh the momentum SGD Etc and of course the Adam or the adamw these different algorithms will be really important for you to understand how the Deep learning models are being trained and optimize uh beside that you also need to
34:00 - 34:30 understand the concept of Ving radiant problem the exploring radiant problem um also understand um this different um computational graphs that are being used in order to represent uh NE networks um also um how you can evaluate the performance of neural networks how you can use the cross entropy um and um being able to understand these different um optimization technique makes the concept of mini badge gradient descent
34:30 - 35:00 is also important and the difference between bch gradient descent mini BGE gradient descent stochastic um gradient descent uh understand the concept of Haitian uh why Haitian is is being used what it means to have a faster versus better performing neural network um understand also this batch normalization layer normalization what is the difference beside between them understand the concept of residual connections and also
35:00 - 35:30 what is uh gradient clipping cavier initialization basically how you can initialize your neural network models of course um when I meant the fundamentals of neural networks I definitely meant also understanding what is the bias what is the weights uh what it means to train a neuron Network the role of improving these weights and also you need to understand the ways you can solve these different problems like how to solve a Venum gradient problem how to solve an exploding gradient problem um and also
35:30 - 36:00 um these different techniques to combat the overfitting what it means to have an overfitting this comes from traditional machine learning but also in the Deep learning it's still a problem and also understand how you can use drop out what is drop out uh what is this difference between random Forest versus drop out what is the U L1 regularization L2 regularization versus Dropout the difference between them what is this idea of course of that dimensionality and what is this
36:00 - 36:30 difference between discriminative versus degenerative models what are out hand coders uh what is this idea of reconstruction um error the um nonlinear counterpart of PCA basically when it comes to Outer encoders um understand the architecture Behind these different sorts of deep learning models the um Infamous uh Ann CNN RNN GNN grus lstms
36:30 - 37:00 what is the difference between the lstm and RNN why they have been created what are their problems what are their um good points and also how you can train these different algorithms and for what kind of problems you can use what is p convolution what is pooling what is tried uh when it comes to a computer vision uh and in which cas cases you can use what uh where you can use RNN where
37:00 - 37:30 you can use CNN um and also the generative adversarial Network so we are getting more towards the generative AI but I will tell you in a bit about that so this is basically about the theory when it comes to deep learning everything that I just mentioned and having these fundamentals in place will be really important for you in order to go on to the next step which is understanding how you can use the traditional machine learning and traditional deep learning in the real
37:30 - 38:00 case problems which means uh learning Python and learning Advanced python um the AI Frameworks like the py Tor and tensor flow but not just that also to understand a basic data structures and algorithms in Python and um beyond the python for data science also to understand how you can train a machine learning model in Python how you can train a deep learning model in Python uh how you can uh do a visualization in
38:00 - 38:30 python or how you can filter your data prepare your data clean it basically everything where it comes to data science machine learning and deep learning its practical implementation is happening in a programming language and that's where the python one of the most popular programming languages come in handy and my suggestion would be to learn next the python to understand how you can um uh create uh lists variables
38:30 - 39:00 how you can load data different sorts of data whether those are images text audio how to work with dates how to filter your data how to group your data how to visualize it training a machine learning model training um deep learning model how to make use of uh pytorch which is a deep learning framework in python as well as tanor flow this idea of tensors and how you can be Feld you can train and deploy machine learning and deep
39:00 - 39:30 learning model using python if you are looking for that one place to learn all what I just described make sure to check the lunch. page because we have an entire course when it comes to python for data science which would be a great starting point for you to Learn Python specifically for AI and data science and also get a certification in that field and if you want to H check out all these different Topics in one place when it comes to learning make sure to check our deep learning in preparation course
39:30 - 40:00 which covers all these topics that I just mentioned in a unique Q&A way all right so once you have done that you have your mathematics statistics machine learning deep learning and python as well as advanced python in one place you are ready to move on to the next step and here of course I'm talking about the generative AI so when it comes to generative AI this by the way also includes the large language models here is what I would suggest you to learn in
40:00 - 40:30 order to call yourself a worldclass AI engineer generative AI is one of the most in demand skill set when it comes to the 2024 and 2025 in this AI race in the companies like open AI the entropic Google meta Tesla xai all these different um companies that are at the Forefront of AI Revolution they all are based on these different usages of
40:30 - 41:00 generative AI CH GPT is based on generative AI the Del is based on generative Ai and um the gini the perplexity the autonomous cars these days all of the cutting get AI Tools in one way or the other are based on generative AI That's why if you are an AI engineer with the specialization in generative AI this will be your year all right so let's now talk about what exactly it takes to become a generative
41:00 - 41:30 AI based AI engineer so first up you need to understand the AI foundations and you need to understand um where you can apply generative AI before you get into the theoretical part so understanding also the moral development cycle when it comes to generative Ai and training techniques will be really important because different type of generative AI Foundation models can be trained and optimized in different ways understanding different foundational
41:30 - 42:00 generative AI models will also be very important think of like the generative adval networks this concept of the um G and D uh so basically the generator discriminator what are their roles what they are doing how the Game Theory comes here in place the um where you can apply generative aterial networks the mathematics and statistics behind it the mean Max Theory the Nasha equilibri room and also this concept of uh mode collapse which is a problem for
42:00 - 42:30 generative adversarial networks and how you can train and optimize these gens in order to create new data for example synthetic data Etc so the next topic that I would highly suggest you to study is this variational out and coders this is yet another Foundation generative AI model that I would suggest you to study to understand its mathematics behind it its uh statistics behind it the um idea of this difference between plain out
42:30 - 43:00 hand coders and variational outand coders which is a matter of difference between discriminative and generative uh type of models architecture behind it the KO Divergence the elbow how you can train a variational out and code or this um all the way to the idea of reparameterization trick um and how you can apply variation out encoder in practice of course next topic that I would suggest you to study is of course the Transformers so Transformers they
43:00 - 43:30 are the um mother of all the uh current Cutting Edge large language models when you hear about Lama from met when you hear about gpts from open AI or the cloud Sonet from anthropic all these different large language models at the base of them is the Transformers Transformers are at the heart of large language models and no knowing their history their evolution understanding
43:30 - 44:00 what is the difference between Transformers versus the rnns and stms from Deep learning understanding what is that the what was the reason of them being invented compared to the RNN and stms the idea of embeddings the positional encodings how you can calculate them the attention mechanism the self attention mechanism the curies keys and values the single-headed tension multi-headed tension understanding this entire AR architecture of Transformers step by step the mathematics and statistics
44:00 - 44:30 behind it how you can calculate these different parts how you go from the input embeddings positional encodings all the way to the logits which are the outputs of the Transformers will be really important for you as an innovator to understand because then you will be able to understand this more uh variated versions of the Transformers because GPT models from open AI which power the chat GPT for example are A variation of Transformer model and given that this uh
44:30 - 45:00 modern models like llama the gpts or other ones um they are never published in its entirety so you won't know for example what is the real architecture of GPT 403 uh which some call it also the first AGI model um for that I would highly suggest you to go to the basics and to instead understand the Transformers once you understand the Transformers you also understand the cycle of pre-training so
45:00 - 45:30 what it means to pre-train a Transformer model do it by hand from scratch instead of just using um pytorch Library this will help you to completely understand this um foundational language model once you are done with your Transformers I would highly suggest you to uh get into the uh next topic which is the large language models when it comes to large language models uh this is yet uh
45:30 - 46:00 entirely a different um Universe um and for understanding this and calling yourself an expert in LMS uh you have quite a journey to go through but I will make your life a bit easier and I will give you the step-by-step process and a specific skill set that you can um learn in order to um Master the field of large language models so you can start with understanding uh what are the language
46:00 - 46:30 models this idea of engrams the the concept of predicting the next word and then um how this um large language models have been um evolved over time um what is their unique sites um also understand what are the key large language models like I just mentioned the gpts from open AI the Llama from meta the Falcon the bird from Google and also the Gemma from Google um the cloud
46:30 - 47:00 Sonet from entropic these are different uh very popular open-sourced or close- sourced large language models that you can use as part of your AI engineering journey and understanding these differences understanding how it relates back to the Transformer models will be foundational for you also understand this concept of Open Source and closed Source what are these different tools that you can use when it comes to open source of course I'm referring to the hugging phase and um other platforms
47:00 - 47:30 that you can use in order to um use large language models and generative AI in a more efficient and cost uh um efficient way so understanding the uh foundations of large language models will be really important for you like the attention mechanisms that I just mentioned the language models the engrams uh the architectural Transformers the type of architecture you um you will meet in the field of LMS
47:30 - 48:00 like the encoder base or decoder base or a combination of them um the concept of tokenization from NLP the embeddings how you can calculate each of these parts of the Transformers if you want to be someone who understands these large language models you want to know how to uh tweak them how to edit them and also um in an intelligent way to use them in your job or create a platform so um you also need to uh be
48:00 - 48:30 aware how you can prepare your data for your large language model application like with the machine learning or deep learning if you don't have the data science skills then it's just a matter of wasting your time and money because then uh this concept of garbage in and garbage out still holds um even if you have the most Cutting Edge LM model in front of you if you don't know how to clean your data how to deal with with unstructured data how to prepare it and then injust it into this AI model then
48:30 - 49:00 this AI model will be uh performing poly that's why understanding this uh prompt uh templates this different type of models these different structures will be uh Paramount for you next up I would suggest you understand the cycle of pre-training large language Model fine-tuning A large language model prompt engineering reinforcement learning evaluating and optimizing in it so understand what it means to pre-train a large language model how do those
49:00 - 49:30 large companies use um masked language modeling or Auto regressive language modeling in order to pre-train large language model and then uh maybe do an example uh do a pre-training from scratch uh in order to get a taste of what it's like to pre-train a large language model what are the different scaling clows what is the output of pre-trained large language model and what are the different Downs stream tasks that you need to be familiar when it comes to large language models
49:30 - 50:00 because large language models can be used for classification it can be um for uh predicting the next word uh for different tasks instruction based approach for each of them the downstream tasks is different which means that the model and the way the model is trained is also different and also when it comes to the large language models you need to definitely know how to fine tune a large language model so fine-tuning on a
50:00 - 50:30 single task fine tuning on a multitask instruction model um how to perform uh parameter efficient fine-tuning or the PFT and here of course I'm talking about understanding in detail the mathematics the linear algebra behind Laura the chlora so the quantized version of Laura how to prepare your data for finetuning what are the steps behind fine F tuning what is reinforcement learning so on supervised fine tuning
50:30 - 51:00 supervised fine tuning um and uh definitely um also experience um when it comes to fine tuning can you for example fine tune large language model on your own do you know how to use these different models Etc once you are done with the pre-training and fine-tuning I would also suggest you to understand and learn this idea of reinforcement learning with human feedback not too detail uh you can start with the basics uh just to understand this uh concept of the rhf or
51:00 - 51:30 the reinforcement learning with human capital and why is that that we use this in order to make our moral smarter then uh once you are done with that I would highly suggest you also to understand uh this prompt engineering so what it's like to work with language model and to make it smarter once it's already pre-trained and fine-tuned in here um I would suggest you to um to look
51:30 - 52:00 into the best practices for prompt engineering how to do effective prompting prompt optimization and also how you can apply prompt engineering along with the fine-tuning in order to create AI agents once you are done with that I would suggest you next to go into the topics overx so to understand what are the concepts of the retrieval argumented uh generators and systems the vector databases integrated DRS with Gen how to
52:00 - 52:30 fine-tune with retrieve data this concept of Lama index um agentic RS Etc so once you are done with this I would suggest as a final last two topics for you the first one is the evaluation and optimization of large language models the mastering of the LMS to understand how you can use uh quantization knowledge distillation pruning how you can use um Alm Ops to
52:30 - 53:00 not just um train LM but also productionize it using topics like um tools like longchain flask Etc and also understand how you can evaluate a large language model there are different benchmarks different data sets that you can use in order to properly evaluate and compare a specific large language model to other world known Universal large language models so so finally I would say as an AI engineer you are the
53:00 - 53:30 first person responsible for the ethical and safe creation and usage of your AI models that's why we all have a joint responsibility to understand the AI ethics the principle of ethical AI the bias in AI the privacy and data security in AI to understand this AI act from Europe to understand the gdpr act and then understand the the regulations and the governance so um I hope this didn't overwhelm you if you have a good guidance and if you um have everything
53:30 - 54:00 in one place it will take on average 3 to 6 months for you depending on where you are and whether you already have the prerequisites in order to become a world- class AI engineer so AI engineering is all about solving real problems not just the theoretical knowledge being able to understand all the theory the foundational knowledge along with the implementation of each of these different topics ICS in the reality will be really important for you to become a job ready professional a
54:00 - 54:30 world class tenic AI engineer who knows the foundations and the actual implementation by Bridging the Gap between research and the industry application for that you will need a well-rounded comprehensive training as on one hand and also on the other hand the Practical implementation with projects with ready resume to be able to start applying or create your own up CU it's all about innovation in this field
54:30 - 55:00 and it takes a lot of effort and motivation to combine this different um skill set in one place but this also comes with a reward on one hand yes you need to put a lot of effort in order to become an AI engineer but on the other hand this is one of the most rewarding and most in demand careers for the next decade so if you are serious about becoming a world-class AI engineer then you can also Al apply to our AI engineering boot camp at Lun Tech to get
55:00 - 55:30 everything in one place not just surface level knowledge but the theory the actual projects that you can also put on your resume those are cutting Gage projects and as a results you will be able to call yourself a real AI engineer and Lear that dream dream job or to become a Founder for a unicorn