What should you know before starting a Machine Learning project?

Start by articulating clear objectives and comprehending the nuances of the problem domain. Ensure data quality and relevance, make judicious algorithm selections, and invest in robust preprocessing and feature engineering. Take into account ethical implications, prioritize model interpretability, and devise plans for scalability. This thoughtful approach establishes the groundwork for a well-executed and prosperous machine learning endeavor.

Machine Learning teams need to understand the benefits - tangible or intangible- by accepting a new request. If there are several ongoing projects, is it worth the labor and rescheduling costs to take it up? Assuming the project can indeed be taken up- what would define project success? For instance, a wellness startup with an innovative line of preservative-free products would be interested in a machine learning solution for automated package inspection. Any tearing could cause the product to spoil. A success metric could be the number of damaged packages. The lower this is, the better. By understanding the priority and benefits of requests, smoother project management may be achieved without the nightmares of project creep.

I will also add, before starting working on your ML project, you should know your limitations and boundaries (e.g you are good in CV and you are working on NLP). And clearly define what you wants to achieve with your project. A projects start to solve a problem, that's how goal is defined.

Defining the project goal, scope, expected benefits, and the applicability/justification for using machine learning to address it is paramount. This ensures that, (i) solving the problem is worthwhile and feasible, and (ii) the problem is understood well-enough to determine an appropriate solution. It's easy to forget that some problems can be solved with much simpler and deterministic solutions, without the explicit need for ML. Finally, its always worth determining any needs for domain-specific knowledge. Maybe its because I work in a highly challenging field (AISec), but one cannot overstate the value of domain knowledge: quite simply, it can focus you on what really matters fast and short-circuit you to the most effective approach!

Before diving into a Machine Learning project, remember: know your data (like, really know it), set clear goals (what's the point?), and pick the right algorithm (choose wisely, my friend). Also, keep it simple, no need for rocket science. Good luck! ?? #MachineLearning101 #SimpleAndSmart

An essential aspect of my process is exploratory data analysis (EDA). My approach begins with selecting random subsets from datasets, minimizing size while retaining statistical integrity. Utilizing Jupyter notebooks, I load these subsets (often time pandas data frames suffice) for efficient, live iteration. This is followed by visual inspections, including plot generation and correlation matrix analysis of numerical columns. The aim is to develop an intuitive understanding of the data that enables you to plan your next stages in an informed manner. Oftentimes I find myself workshopping the simplest possible version of my desired solution on these data slices as an almost form of practice.

Understanding and cleaning the data is probably going to be 70% of your time in the ML journey! Your algorithm's performance is as good as the data you provide it. This means dealing with outliers, imbalanced classes, avoid biases, noisy labels, organizing the dataset in an easily accessible format and feature exploration. In the world of deep learning, feature exploration might take on a smaller role, but it is nevertheless crucial in helping understand why the model would provide a certain output for a given set of input data. Feature and dimensionality reduction tools like TSNE or UMAP, might help inform the particular manifold on which the data lies.

After you have had observed whether it is a continuous or discrete data, you need to know how to import it for training and testing. Firstly, you need to explore different features of the dataset, what are the datatype present, whether there are any null values or missing values, you need to observe the statistical measures of the dataset like mean median count standard deviation, check whether there is skewness or not .

Before embarking on the construction of the model, it is necessary to understand its data, what each data represents, the value of each data, what it would imply if it did not exist With knowledge in the field, Internet research, EDA. It takes time, be patient.

Gather and assemble the relevant data sources that will serve as the basis for your machine learning model. Assess the quality of the collected data by identifying and removing any errors, inconsistencies, missing values, or outliers. Perform exploratory data analysis (EDA) to gain insights into the data's structure, distribution, and relationships between variables. Extract meaningful features from the data that are relevant to the machine learning task. Prepare the data in a format suitable for machine learning algorithms. Select the most relevant features that contribute significantly to the machine learning task.

In the realm of machine learning, the choice of tools plays a pivotal role in shaping the entire workflow, from data preprocessing to model development and deployment. For deep learning tasks, TensorFlow and PyTorch are two dominant frameworks that have shaped the landscape. TensorFlow, developed by Google, boasts a comprehensive ecosystem with tools like TensorBoard for model visualization and TensorFlow Extended (TFX) for production deployment. PyTorch, favored for its dynamic computational graph, provides a more intuitive interface for researchers and practitioners. The choice between TensorFlow and PyTorch often depends on the specific requirements of the task at hand and individual preferences in terms of syntax and design philosophy.

Most ML and deep learning libraries are readily available in the Python programming language. The nature of python enables faster prototyping and iterations over ideas. Most light weight traditional ML algorithms can be trained without a GPU, but deep learning models are faster to train in GPUs, smaller projects can use the Google colab environment or other cloud based solutions without investing on dedicated hardware.

Selecting the appropriate tools is the third step in initiating a machine learning project. These tools encompass software and hardware crucial for project implementation. It's essential to align these tools with your project's unique requirements, personal preferences, and financial constraints. With an array of options ranging from programming languages, frameworks, libraries to platforms, consider factors like scalability, compatibility, security, and the availability of support and documentation to ensure a seamless workflow.

In the machine learning kitchen, use tools like Pandas, NumPy, and Scikit-learn as your prep chefs. Matplotlib and Seaborn are your visual guides, ensuring data freshness. TensorFlow and PyTorch act as master chefs crafting models. Jupyter Notebooks are your interactive recipe book, and Excel or Google Sheets keep things organized. This meticulous preparation ensures top-quality inputs, minimizing biases and errors for a successful machine learning "dish."

Constructing models is a complex and intricate process that demands meticulous attention to various elements, including algorithm selection, training, tuning, validation, etc. In the realm of supervised learning, when faced with imbalanced data, the application of the SMOTE technique becomes imperative to rectify the imbalance. This involves generating synthetic samples to balance the dataset, creating a more representative input for subsequent training. Subsequently, this balanced data is fed into the appropriate model. The phase encompassing training, tuning and validating the models involves iterative experimentation and evaluation, ensuring optimal performance of the models.

Beyond accuracy, the interpretability of machine-learning models helps in several ways: 1. Error Correction: Clear models allow for identifying and fixing biases or mistakes, enhancing their effectiveness. 2. Decision Explanation: In critical fields like healthcare, explaining model decisions is crucial for trust and accuracy. 3. Data Focus: Knowing which data influences predictions the most aids in more efficient model building. Choosing the proper methods and careful data preparation are key. Tools like LIME and SHAP are helpful for this purpose. Balancing accuracy with interpretability ensures machine learning models are practical but also transparent and reliable.

Selection of models is a pivotal step in your ML lifecycle of solving a problem statement. Improper selection will lead to inaccurate learning of the model and we will be iterating with different parameters and metrics with less or no improvement. Considering the following while building models will be fruitful. - Quality of data is important to decide between unsupervised and supervised learning, highly unclean, imbalanced data will not be ideal for supervised learning. - Choice of metrics is vital as imbalanced data will need to look for metrics like F1 score, recall over accuracy. - Selection of algorithm to overcome underfitting or overfitting issues. (eg: ensemble, bagging, boosting techniques). - Monitoring of model for tuning.

Train, fine-tune, and validate these models to optimize their accuracy, precision, and recall. This phase requires iterative experimentation and evaluation to ensure the models perform effectively and align with your project's goals.Nowadays, there are numerous models available that facilitate model comparison. In the current landscape, there exists a myriad of models designed to facilitate model comparison, aiding in the evaluation and selection of the most suitable model for a given task.

If you understand the nature of your dataset, for example via TSNE or UMAP, it came give you an idea of the model complexity required to solve the problem. For example, you might find that building a simple SVM or a linear classifier can achieve good performance than spending resources on training large dep learning model. On the other hand, if you have access to a large labelled dataset and need to spend many hours hand crafting features, then it is a good idea to start with a deep learning architecture that has already been tried for a similar task previously. if your task and dataset is completely novel, then developing a custom deep learning architecture would be the approach. Quality of the data and labels are crucial in all cases.

Testing results in machine learning is a critical step to evaluate the performance and generalization capabilities of a trained model. Proper testing helps ensure that a machine learning model can make accurate predictions on new, unseen data. Here are key concepts and practices related to testing results in machine learning: 1. Train-Test Split 2. Cross-Validation 3. Evaluation Metrics 4. Confusion Matrix 5. ROC Curve and AUC 6. Overfitting and Underfitting 7. Hyperparameter Tuning 8. Model Interpretability 9. Deployment Considerations 10. Handling Imbalanced Classes

Establish clear and measurable evaluation metrics to assess the performance of your models. These metrics should align with the project's goals and reflect the nature of the problem being solved. Divide the available data into three separate datasets: training, validation, and testing sets. Train the machine learning model using the training data. Monitor the model's performance on the validation set to identify overfitting or underfitting and adjust hyperparameters if necessary. Evaluate the trained model's performance on the testing dataset, which should be completely new and have not been used for training or validation purposes. Analyze the model's predictions and performance metrics to identify any biases or limitations.

To ensure the accuracy of your model, it's crucial to conduct tests. These tests help distinguish situations like overfitting or bias through controlled scores, enabling a clearer understanding of your model's performance.

Testing results on new data is not just a procedural step; it's the litmus test for the real-world applicability of your models. Beyond metrics, understanding the context in which your models operate is crucial. It's an iterative process where refining assumptions and addressing limitations lead to more robust and reliable models. Continuous testing is a commitment to adaptability in the dynamic landscape of data science, ensuring that models stay effective and aligned with evolving needs.

Determining whether a model is successful or not is not an easy task. Calculating benchmarks is a crucial step but not enough. Understanding the nature of problem and defining the success criteria accordingly is the key of success determination at the end of the day.

There are a lot cloud based and non cloud based solutions you can use to deploy your model. While cloud based solutions are more robust, they sometimes can be a little complex and take longer to execute On the other hand, tools like streamlit are great non cloud based solutions for quick poc, however they come with greater risk of security and dependabilty

I like to first quickly deploy my models on a Python-based server framework like Flask, FastAPI, or even a Streamlit dashboard to get a sketch of what you want your final product to be. Then, if you need to, you can build a more customizable front end using a javascript framework like React or Vue which I like. Most cloud platforms allow you to deploy apps with CI/CD integration with a Github or Bitbucket repository, and also allow you to version control your models so that whenever you get new data you can retrain your models to get better performance.

Before starting any machine learning project, you have to understand the problem statement or business use case properly. You have to be crystal clear about what type of problem you are solving and what the pros and cons of using a machine learning model. Now the next thing is you have to know at least a little bit about domain knowledge because it's an important factor while doing feature engineering, and data cleaning data transformation because sometimes lack of domain knowledge can lead to affect the quality of data that you will use to train the model.

Before You Dive In: 5 Essential Points for Your ML Project Define the Goal: What problem are you solving and why? Clear goals guide your project and ensure it delivers value. Gather the Data: Access and understand the data you'll use. Ensure it's relevant, clean, and sufficient. Know Your Tools: Choose the right algorithms and frameworks for your project. Consider your technical expertise and resources. Team Up and Learn: Collaborate with others and seek guidance from mentors. Learning is key in this rapidly evolving field. Test and Iterate: Don't expect perfection in the first try. Continuously test, refine, and improve your model for optimal results.

You might be trying to solve and engineering problem be spending a lot on Ai If you don’t have enough data most of projects models will not be efficient You have to mostly define the whole architecture, DATA DRIFT and Ensemble has to be planned very much .

It is essential to form the problem correctly before coming to a solution. Example we need to define if our problem is a classification problem or a regression problem. Another big consideration is availability of data. Having a reliable feature pipeline can play a Huge role in overall performance.

Before starting a machine learning project, you also need to take the domain into consideration.You must have a minimum of knowledge in the field in which you work, and the issues and implications of each issue. This will allow the engineer to make better decisions For example, the constraints in the field of medicine and agriculture or automobiles are not the same, the business issues are not the same.