It's true, from last 2 weeks I working to train llm model for conversation chat bot, but the training model is simple 10 minutes code, but it won't work as expected
Hi, I’m 25 years old and currently working as a Java backend developer with nearly 3 years of experience. I’m looking to transition into the field of AI development, specifically focusing on generative AI technologies. My goal is to build generative AI applications that solve real-world problems and create impactful solutions. That’s how I came across your TH-cam channel! I really admire your work, and I was hoping you could provide some guidance. From a career perspective, how should I begin this transition into AI and generative AI development? Your advice would mean a lot to me, and I’d be truly grateful for your help!
Firstly Learn the Basic Mathematics(Linear algebra, calcus, statistics & probability ), Python(Your Java knowledge will help to transistion into different programming) because it has AI library(numpy, pandas and sckitlearn) to easy your hectic work, ML concept and understand the math behind in each of the concepts, DL algorithms and undertand the usecase of each algorithms, error reducing and validation. This is basic concept to move into GEN AI. then you can explore different LLM models, NLP, transformer etc.... Statistical and Mathematical Foundations Probability and Statistics - Descriptive and inferential statistics - Probability distributions (Normal, Poisson, Binomial) - Hypothesis testing - Confidence intervals - Central Limit Theorem - Bayesian vs. Frequentist approach - A/B testing methodology Mathematics - Linear Algebra - Matrix operations - Eigenvectors and eigenvalues - Vector spaces - Calculus - Derivatives and gradients - Optimization techniques - Gradient Descent - Regularization techniques Machine Learning Algorithms: Supervised Learning - Linear Regression - Logistic Regression - Decision Trees - Random Forests - Gradient Boosting (XGBoost, LightGBM) - Support Vector Machines (SVM) - K-Nearest Neighbors (KNN) Unsupervised Learning - K-Means Clustering - Hierarchical Clustering - Principal Component Analysis (PCA) - t-SNE - DBSCAN Deep Learning - Neural Network architectures - Convolutional Neural Networks (CNN) - Recurrent Neural Networks (RNN) - Long Short-Term Memory (LSTM) - Transformer models - Basics of TensorFlow & PyTorch Python Programming Skills: Core Python: - Data structures (lists, dictionaries, sets) - List comprehensions - Lambda functions - Decorators - Error handling Data Science in Python: - NumPy - Array operations - Vectorization - Pandas - Data manipulation - Groupby operations - Merging and joining datasets - Scikit-learn - Model training and evaluation - Preprocessing techniques - Pipeline creation - Matplotlib & Seaborn for data visualization Practical Interview Preparation Tips: 1. Build a strong portfolio on GitHub 2. Practice coding on LeetCode and HackerRank 3. Participate in Kaggle competitions 4. Understand the business context of your models 5. Practice explaining complex concepts simply 6. Be prepared to discuss model selection, bias-variance tradeoff, and model evaluation metrics Key Interview Topics to Master: - Feature engineering - Model evaluation (precision, recall, F1-score) - Overfitting and underfitting - Cross-validation techniques - Handling imbalanced datasets - Model interpretability - Basic software engineering principles for data scientists
Yep, ML is more about making something work with explainable approach and it doesn't make sense if we just write it off within 3 lines of code. Consider checking out my course on Core ML on youtube for free.
@AyushSinghSh so what should I actually do, because everywhere they do train() fit() and done. Btw, I am 13 years(learned python, libraries,linear algebra, probability and statistics,will learn ML next) just like you. And many people say that getting ML job as a fresher is not possible, then how did you get one
Make production grade systems or scale it via Self-host or try Timescale Cloud for free here: github.com/timescale/pgai
❤👍 just now thought of learning GenAI and here it is
It's true, from last 2 weeks I working to train llm model for conversation chat bot, but the training model is simple 10 minutes code, but it won't work as expected
Glad to hear it :)
what i have to learn first ML or Gen AI or Python
Start with genai basics :) see where you’re at
Real Ayush is back💥✨
YESS
Hi, I’m 25 years old and currently working as a Java backend developer with nearly 3 years of experience. I’m looking to transition into the field of AI development, specifically focusing on generative AI technologies. My goal is to build generative AI applications that solve real-world problems and create impactful solutions.
That’s how I came across your TH-cam channel! I really admire your work, and I was hoping you could provide some guidance. From a career perspective, how should I begin this transition into AI and generative AI development?
Your advice would mean a lot to me, and I’d be truly grateful for your help!
Firstly Learn the Basic Mathematics(Linear algebra, calcus, statistics & probability ), Python(Your Java knowledge will help to transistion into different programming) because it has AI library(numpy, pandas and sckitlearn) to easy your hectic work, ML concept and understand the math behind in each of the concepts, DL algorithms and undertand the usecase of each algorithms, error reducing and validation.
This is basic concept to move into GEN AI.
then you can explore different LLM models, NLP, transformer etc....
Statistical and Mathematical Foundations
Probability and Statistics
- Descriptive and inferential statistics
- Probability distributions (Normal, Poisson, Binomial)
- Hypothesis testing
- Confidence intervals
- Central Limit Theorem
- Bayesian vs. Frequentist approach
- A/B testing methodology
Mathematics
- Linear Algebra
- Matrix operations
- Eigenvectors and eigenvalues
- Vector spaces
- Calculus
- Derivatives and gradients
- Optimization techniques
- Gradient Descent
- Regularization techniques
Machine Learning Algorithms:
Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Gradient Boosting (XGBoost, LightGBM)
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- t-SNE
- DBSCAN
Deep Learning
- Neural Network architectures
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
- Transformer models
- Basics of TensorFlow & PyTorch
Python Programming Skills:
Core Python:
- Data structures (lists, dictionaries, sets)
- List comprehensions
- Lambda functions
- Decorators
- Error handling
Data Science in Python:
- NumPy
- Array operations
- Vectorization
- Pandas
- Data manipulation
- Groupby operations
- Merging and joining datasets
- Scikit-learn
- Model training and evaluation
- Preprocessing techniques
- Pipeline creation
- Matplotlib & Seaborn for data visualization
Practical Interview Preparation Tips:
1. Build a strong portfolio on GitHub
2. Practice coding on LeetCode and HackerRank
3. Participate in Kaggle competitions
4. Understand the business context of your models
5. Practice explaining complex concepts simply
6. Be prepared to discuss model selection, bias-variance tradeoff, and model evaluation metrics
Key Interview Topics to Master:
- Feature engineering
- Model evaluation (precision, recall, F1-score)
- Overfitting and underfitting
- Cross-validation techniques
- Handling imbalanced datasets
- Model interpretability
- Basic software engineering principles for data scientists
i want a referal to join as a python developer so can anyone help me in this situation
Companies Asking Masters and Research Publications what to do for that as belonging from decent college get easily the job of avg 12lpa ????
Bro, you once said writing three line of code in jupyter is not ML. Can you please explain a bit
Yep, ML is more about making something work with explainable approach and it doesn't make sense if we just write it off within 3 lines of code. Consider checking out my course on Core ML on youtube for free.
@AyushSinghSh so what should I actually do, because everywhere they do train() fit() and done.
Btw, I am 13 years(learned python, libraries,linear algebra, probability and statistics,will learn ML next) just like you. And many people say that getting ML job as a fresher is not possible, then how did you get one
these 5 hours is worth it
❤️❤️
bro wherewrer you
When will you bring a course in hindi ?
Soon
💥✨