Aftab Alam

I have done my MCA from Technical University, and my major is in data science and machine learning. I also have a certificate from Intel, as well as completed courses on Udemy and many other online platforms. And I have worked on many projects in machine learning and deep learning, such as text classification, image classification, and others.

To make our machine learning model more accurate, we can lower the number of layers or use a grid search, random search to find the best parameters or use an optimizing technique which best suits our problem.

Tune hyperparameters for a deep learning model. We can use grid search or random search to find the best parameters that work with our machine learning model to predict the best transfers so as to achieve higher accuracy. You can use libraries such as TensorFlow and scikit-learn.

When we have imbalanced data, then we can balance it, we can use imputation or such methods as mean, median, mode, or KNN algorithm that will automatically correct our dataset. If we have categorical values, then we must first convert them into numerical values. Similarly, it goes with text and images. We can detect the skewness of the data by visualization through matplotlib or seaborn. We can also detect outliers if there are any in the dataset using a box plot, which will highly affect our machine learning model's prediction, or we can use normalization and standardization techniques on our datasets. So to make all our datasets have the same characteristics, such as 0 mean and a standard deviation of 1, we can use no standard scaler from scikit-learn to standardize our data and normalize it.

Well, we work with the RNN to process sequential data. We have a problem such as the RNN model. A simple RNN can't process or remember long-range dependencies in sequential data. The RNN model is specifically designed for sequential data, and we can also do classification as well as regression with RNN. However, based on transformer models such as T5, CHEDGBT, and other models based on transformers, they can have the ability to remember long-range dependencies and provide coherent and logical answers because of the hidden cell state it maintains over every time step of input. This is the drawback that transformer models cover.

To accurately select the future that you want to take in to train our model, we can use various metrics such as correlation metrics and covariance metrics to check the dependence of features on one another, how they influence each other, and how they will influence our model. This is a part of feature engineering. And similarly, if we are working with time series, then we have to look for a date range from where the data is starting and ending. To make a time frame, perform operations such as rolling mean or rolling statistics to define the window frame and predict the upcoming answer. Feature engineering is important because our model depends only on features to provide the best transfer. We can discard outliers because they are not needed in our machine learning models. And also, we can look for columns that will not be apart for our machine learning model to predict the output, such as date columns, index columns, or name of years and their countings, and so on.

Medical embedding. I'm encoding. Encoding. Get. Each category equals an index. Apply. Data frame column. Approaching code from the apply lambda function encoding. So this is the code. This code I see here will convert category variables into numerical embeddings. It suggests it in the line data frame. The bracket column equals the data frame column dot apply where it uses a lambda function to encode features. The second parameter that we give in the encode categorical definition function will convert this column when we apply this lambda function into numerical. First, we give the data frame as the first parameter, and the column that we want to convert from categorical to numerical as the second.

Prediction service. What we did? Is it class prediction service? Decorator static method. Load model. Model path, prediction service dot model, wish service dot model, from path. Another function. Previous permission. Load model as permission. Model path of the model. Static method. Predict input feature. This mode which we are loading a model here and then another definition we are predicting features. At last, within it, model is described. Features of prediction service dot model is none. Then we predict we can predict predictions. Prediction service predict the load model. To load the model prediction service That model, we are getting model from the prediction surface, and then we are loading it from another path, another path, load model path, If prediction it's telling if prediction service. Model is none. Model is not loaded. Yes. Right. Model dot predict input features. Prediction service dot model into futures. This point is not a problem here. I'm not sure if you address it. I think it will take time for me to take this code and run it in my notebook and then find what are the potential causes for any errors. Just by looking, I think I wouldn't be able to find what the problem here is.

When I have to work with TensorFlow, specifically for image classification, first, I will need my data. If I have data, then I can start working on it. And if I do not have data, then first, I have to collect data. This can be done through a YouTube app scraping tool. Or if I have data available, then I will use that data first to check whether it is in the right format or all the data types are in the right structure. Also, I will perform operations such as whether there are null values or not. And then after checking the format of images, whether all the images that I want to train in my model are right or not. Because it sometimes happens that some formats are not supported by our model. So, that leads to an error while we are loading our images with our model. And it comes to our attention later when we have finished our model architecture. To work with images, we generally use CNNs because they work best. In CNNs, we have convolutional neural networks. They have parameters such as filters, which capture information in the images. And we can define the filter size by the help of the kernel size parameter. The number of filters we can define. The activation function we have to define in the model architecture, and the input dimension, the dimension of the image that our model will train on, which will be defined in the first layer. A model architecture in CNN will start from the input layer, then the hidden layers will come. And then in the final layer, our output layer will come. We can also use a dropout layer to drop some neurons randomly. And then we can divide our dataset into a train-test split. After dividing, we can train our model by the model's fit method. We can pass our dataset and define the number of epochs. And then after our model will successfully train on the dataset, we can predict and test on our test dataset using the model's predict method. And finally, we can check the accuracy of the model on the training dataset and testing dataset.

For transfer learning, we can use models where we don't want to create a model for ourselves because sometimes, and it often happens when we're working with large datasets. Models consume too much space and time, and it's not for everyone to train complex models on their personal laptops because it requires complex hardware architectures such as GPUs and TPUs. So when we're working with large datasets and the data are in high dimension, it's better to use pre-trained models because they don't take time to train our model. We can customize the output layers just to set as trainable is equal to true, and then we can define our input and output layers in which we want our output, such as when we're working with 10 classes, then we can define 10 classes and output in a layer. We can use the Hugging Face library for leveraging transfer learning models, and we can customize according to our needs. And it will work similarly as I've said before. First, we have to gather old data and divide the data, then train our model on our data. After successful training, we will predict our data and check its accuracy. Yes. We can use transfer learning with GPT, BERT, and many other models such as ImageNet, VGG. It's based on the type of problem we're working on, whether we're working with text data or when we're working with images or videos.

Performance. What strategies could you apply to monitor performance of production and systems? You can use version controls to manage your code and provide continuous integration or continuous deployment. We can use GitLab, AWS to track our code and update our code, fix bugs. Monitoring the performance of production. Yes. That was all. Thank you.