Data Science and Machine Learning Services
Starting at
$
5
/hrAbout this service
Summary
FAQs
Which natural language processing techniques and models do you normally use?
I use stemming tokenization, lemmatization, word2vec, ngrams and word embedding among other techniques. Some models I use are recurrent neural networks, LSTM, bidirectional LSTM RNN among other pretrained models, transfer learning techniques and libraries.
Can you do a sentiment analysis task?
Yes I can.
Which libraries do you normally use?
I normally use numpy, pandas, tensorflow, keras, matplotlib, seaborn, sklearn & pytorch among other libraries.
Which machine learning algorithms do you normally use?
The choice of algorithm depends upon the type of task at hand. I normally use support vector machines, k-means clustering, k-nearest neighbor, decision tree classifier, random forest classifier, convolutional neural networks, support vector regression & lasso regression among others.
Which computer vision tasks can you do?
I can do image classification, image segmentation, face recognition & image processing.
Which data preprocessing techniques do you use?
Some data preprocessing techniques I normally use are standardization, scaling, normalization, handling missing values, removing duplicates, identifying outliers, feature engineering, feature extraction, feature selection, & dimensionality reduction.
Which programming languages do you use?
I use python for model development and data preprocessing.
Do you use Jupyter Notebook or Google Colab Notebook for your projects?
Depending on the type of project, I normally switch between both of the above. Google Colab is specifically helpful in deep learning tasks.
What's included
Data Preprocessing
In this critical phase, raw data undergoes meticulous cleaning to address inconsistencies, errors, and missing values, ensuring data integrity. Advanced techniques in feature engineering enhance predictive power, while feature selection ensures that only the most relevant features are retained for modeling. Data transformation ensures standardized inputs for modeling algorithms. Integration of multiple datasets and dimensionality reduction techniques optimize computational efficiency. Additionally, data splitting enables accurate evaluation of model performance. These preprocessing steps lay the foundation for robust analysis, empowering informed decision-making based on reliable insights.
Model Training and Evaluation
Following data preprocessing, machine learning models are trained on preprocessed data to learn patterns. Models are fine-tuned and evaluated using validation sets, optimizing performance metrics. Iterative refinement improves model performance, with final validation on test data ensuring reliability.
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