The amount of time that you should give to each part of the road map can majorly depend on your availability, attention, goals, background and the depth of knowledge you want to acquire. The first step is to learn a programming language and you should not spend more than two months learning a programming language and coding on it. Next, you should be clear with the fundamentals of machine learning that is statistics, linear algebra and calculus. You can spend up to 2 months learning these fundamentals. Self-study might be difficult because for machine learning you necessarily need a guide therefore if you plan to take up an online course make sure it is around 5 to 6 months depending on the depth of the course you wish to take up for yourself. Once you think that you are almost ready, take up a project and work on it for the next 6 to 12 months. Use this period to involve yourself in community discussions, workshops and internships so that you can put to work all the knowledge and skills that you gained from the online courses. In the end, to brush up your skills you can spend a few hours every month reading research papers to keep you up to date with the latest advancements in technology and machine learning. Before you even start a project one thing that you should be crystal clear with is your problem statement. Defining the problem you want to take up to solve and what input and output will you be giving and getting becomes crucial as the whole project relies on this data and this problem statement. Once you decide what your problem statement is and what you want the model to take as input, you can now move on to collecting and processing your data. Once you have collected the data you are now expected to process it to make it clean, properly formatted and transformed into a format that can be used. The next step is to choose a model. Machine learning algorithms are many but you should be careful and make a wise and calculated decision on which machine learning algorithm you want to use among SVM, clustering, KNN, CNN etc. Once you have decided on your model you move on to the next step where you train the model with the data that you have collected and pre-processed. Your collected and preprocessed data should now be used by you to fit it inside a model. You trained the model but now what !! Now is the time when you evaluate the accuracy of the model. The accuracy of the model can be evaluated by using appropriate matrices and techniques like confusion matrix, chi-square, ROC curve, root mean square error etc. Once you are done with the evaluation you can move on to the next step which involves improving the model by adjusting hyperparameters, changing the architecture and even trying to use different models to be able to conclude which model works best for you. Now that you have deployed the model and even found ways to make it an improved version all that is left to do is maintain the model's accuracy and communicate the results of the project clearly and concisely including the limitations of the model to provide future directions to the project. To know about how to apply for an internship click on the link below.
Published on: May 13, 2023
#machinelearning, #internship, #resume, #websites, #feedback, #review, #employer, #legit
#project, #algorithms, #evaluation, #maintainence, #self-study, #onlinecourses, #linearalgebra, #statistics
Published: May 13, 2023
Author: Dipti Vatsa
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