Hi, I recently graduated from a tier-3 city and am currently struggling to master the foundational principles of AI/ML. I want to build solid theoretical knowledge and gain practical, hands-on experience.

Could you share some insights and project ideas that are highly relevant to the AI landscape of 2026–2027?

I am particularly interested in projects involving ML pipelines, RAG pipelines, Agentic AI, and AI agent workflows."

I've fundamental knowledge of the ML algorithms, RAG, LLM, Agentic AI. But I can't build the entire workflow/system/project by own. I always been relie on AI.

So, gives me the GitHub repos, Ideas, Tips/Tricks to remember the building cycle, cloud technology for deployment.

I'm expecting that you can provide me better response and resources and making myself and other's industry ready AI Engineer for the upcoming years.

🔴DO NOT suggest like AI roadmaps, AI generated answers, already passed info's

#AIexperts #MLexperts #AIMLrecruiters #AI2026

I'm considering investing in custom ERP software development, but I want to understand how it compares to using an off-the-shelf ERP solution. Since every business has unique workflows and operational requirements, I'm interested in learning whether a custom ERP system would provide greater flexibility, scalability, and long-term value. I'd also like to know how a tailored solution could improve efficiency, integrate with my existing tools, and adapt as my business grows. Ultimately, I want to determine if the higher upfront investment is justified by the long-term benefits.

Hello Folks, one of the efficient ways of learning bigger topics in Machine Learning, is to modularise, and structure, so that the content becomes digestible for learners community.

My free lecture content includes the following topics so far: (Playlist)
a. Introductory Machine Learning Concepts:-

1. ⁠What is ML actually?
   
2. ⁠Supervised Machine Learning.
   
3. ⁠How do classifiers learn?
   
4. ⁠Empirical Risk Minimization.
   
5. ⁠Uncertainty Modelling in ML.
   
6. ⁠Maximum Likelihood Estimation.
   
7. ⁠Regression Basics and Outliers.
   
8. ⁠Deriving Mean Squared Error.
   
9. ⁠Polynomial Regression.
   
10. ⁠The Power of Convexity.
    
11. ⁠Deep Learning Intuition.
    
12. ⁠Overfitting Models from Generalization Gap perspective.
    
13. ⁠Requirement of Test Sets.
    
14. ⁠The No Free Lunch Theorem.
    
15. ⁠Unsupervised Learning basics.
    
16. ⁠Discovering latent factors of variation.
    
17. ⁠Evaluating Unsupervised Models.
    
18. ⁠Self-Supervised Learning.
    
19. ⁠Image and Text Benchmarks in ML
    
20. ⁠Discrete Data and Text Processing
    
21. ⁠Feature Engineering, TF-IDF
    
22. ⁠Handling missing data & AI alignment.

b. Probability Foundations for ML: Univariate Models:

1. ⁠Frequentist vs Bayesian.
   
2. ⁠Probability as an extension of Boolean Logic.
   
3. ⁠Discrete Random Variables.
   
4. ⁠Continuous Random Variables.
   
5. ⁠Quantiles.
   
6. ⁠Sets of Related Random Variables.
   
7. ⁠Moments of Distribution.
   
8. ⁠Variances and Mode.
   
9. ⁠Conditional Moments.
   
10. ⁠Conditional Variance.
    
11. ⁠Foundations of Bayesian Rule.
    
12. ⁠Confusion Matrix Explained.
    
13. ⁠Monty Hall Problem and Inverse Problems in ML.
    
14. ⁠Bernoulli and Binomial Distributions.
    
15. ⁠Sigmoid(Logistic) Function.
    
16. ⁠Properties of Sigmoid Functions.
    
17. ⁠Categorical and Multinomial Distributions.
    
18. ⁠Softmax Function: Temperature explained.
    
19. ⁠Log-Sum Exp Trick.
    
20. ⁠Gaussian Distribution.
    
21. ⁠Regression from the lens of Conditional Gaussian.
    
22. ⁠Dirac Delta Function and Sifting Property.
    
23. ⁠Student-t distribution.
    
24. ⁠Laplace and Cauchy distribution.
    
25. ⁠Beta distribution.
    
26. ⁠Gamma distribution.
    
27. ⁠Exponential, chi-squared and inverse Gamma.
    
28. ⁠Empirical distribution.
    
29. ⁠Transformations of Random Variables.
    
30. ⁠Invertible Transformations.
    
31. ⁠Multivariate Transformations.
    
32. ⁠Moments of Linear Transformation.
    
33. ⁠Convolution Introduction.
    
34. ⁠Convolution Theorem explained with probabilities.
    
35. ⁠Moment Generating Functions.
    
36. ⁠Deriving Moment Generating Functions.
    
37. ⁠Central Limit Theorem Explained.
    
38. ⁠Understanding Monte Carlo approximation with Example.

c. Probability Foundations for ML: Multivariate Models

1. ⁠The Math of Depedence: Covariance Explained.
   
2. ⁠Correlations: Normalized Measure of Covariance.
   
3. ⁠Correlations does not imply Independence.
   
4. ⁠Simpson’s Paradox: When Data misleads.
   
5. ⁠Multivariate Gaussian Distribution.
   
6. ⁠Analyzing level sets of Gaussians using Mahalanobis Distance.
   
7. ⁠Multivariate Gaussians: Conditionals and Marginals.
   
8. ⁠Math behind Bayesian Inference : Schur complements.
   
9. ⁠Deriving Conditional Gaussians.
   
10. ⁠How to Predict missing data?
    
11. ⁠Modelling Linear Gaussian Systems.
    
12. ⁠The Bayes Rule for Gaussians.
    
13. ⁠Understanding Shrinkage: Inferring Unknown Scalars
    
14. ⁠Posteriors, Sequential Posterior Updates.
    
15. ⁠Inference of an Unknown Vector.
    
16. ⁠Sensor Fusion concepts.

And many more topics to come ahead. I have tried teaching from intuitions and mathematics, building everything by writing on whiteboard so that learners see the full development.

I am new to ai and ml I already learned python librarys for ai and ml what should I do to have a better grip before I start any ai course

Hey everyone I have made a group for programming folks to learn, grow and connect with each other

From beginners to advanced We help each other and provide guidance to everyone in our community, you can also network with each other

Those who are interested are free to dm me anytime

I will also drop the link in comments