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A2 - MiscellaneousResourcesForMachineLearning.html (9110, 2019-11-03)
A3 - Data Science Online Resources.pdf (163047, 2019-11-03)

# Coursework - [B] : Suggested for Complete Beginners - [Higly Optional] : Courses may be done as per your convenience and interest ## Month 1 Mathematics is the prerequisite for Machine Learning. Mathematics subject is crucial for many high demand remunerative career fields such as Computer Science, Data Science and Artificial Intelligence. - Week 1 : Linear Algebra [B] https://www.khanacademy.org/math/linear-algebra - Week 2 : Calculus [B] https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr or https://www.mathsisfun.com/calculus/ ; want theoretical notes , find it at https://the-learning-machine.com/article/machine-learning/calculus . - Week 3 : Probability [B] https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2 - Week 4 : Statistics [B] http://alex.smola.org/teaching/cmu2013-10-701/stats.html - Algorithms ( Only if you want to learn proper software development ) [ Highly optional ] This is an overview of what the students study as the subject Data Structures & Algorithm . So if you are fluent with this part , you can skip this !! https://www.edx.org/course/algorithm-design-analysis-pennx-sd3x ## Note : * Please try to finish the monthly targets of Month 1 as soon as possible , so that you can get ample amount for time for exploring the courses in Month 2 . They are rock strong courses to work upon which will require a lot of commitment , time and patience !! * If you like to get theoretical notes for Mathematical concepts of Month 1 , view it in the Miscellaneous Section . * It`s not mandatory to do all the courses mentioned above , if you are quite fluent with these parts , you are free to skip them out and move on to Month 2 targets. * There are no weekly assignments for the coursework , if you want to do the assignments , check for them in the respective course links shared above . ## Month 2 - Introduction to python for data science [B] https://www.datacamp.com/courses/intro-to-python-for-data-science - Want to dive deeper into Data Visualization & Pre-Processing ? Look into Data Visualization & Pre-Processing section in miscellaneous resources . [ Highly optional ] - Machine Learning A-ZTM: Hands-On Python & R in Data Science [B] Download the course from - https://drive.google.com/drive/u/0/folders/1BtLftZRWdDnj6uc0vxNgynMvd7o9ZfCV - Want to explore the field of Deep Learning ? See the Deep Learning Section in miscellaneous resources . [ Highly optional ] - Want to explore the field of Natural Language Processing [ NLP } ? See the Natural language Processing Section in miscellaneous resources . [ Highly optional ] - See how ML codes are written and made to work at - > https://github.com/maykulkarni/Machine-Learning-Notebooks or https://github.com/GokuMohandas/practicalAI/blob/master/README.md . [ Highly optional ] - Find useful resources here at https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md . [ Highly optional ] ## Note : * No weekly commitments have been set for this month as completing the courses of Month 2 requires immense concept clearing and that highly varies from person to person . Do try to finish the courses patiently with all doubts getting cleared as these form the basics for ML applications . * If you are not able to finish the courses within Month 2 , it`s all okay . Do take your time in Month 3 and then start with projects . The coursework is independent as per your comfort . * There are no weekly assignments for the coursework , if you want to do the assignments , check for them in the respective course links shared above . ## Month 3 ( Month of Projects ) - Beginners Section [B] : Brush your basic concepts and revise them to start doing projects Titanic Dataset Iris Dataset Stock Price Prediction Stores Sales Forecasting Housing Price Prediction ## Guide for Beginner Projects: First of all see Below 2 videos to get an idea on how to make projects of Data Science and Machine Learning And then Move to Kaggle for Making your own project.Its is Good if you Make Minimum 2-3 Projects on your own. - Titanic Survivor : https://www.youtube.com/watch?v=fS70iptz-XU&t= - Credit Card Fraud Detection : https://www.youtube.com/watch?v=gCWBFyFTxVU ## Intermediate & Advanced Section - Learn libraries like Opencv , Tensorflow , SkLearn 1 ) Natural Language Processing : MNIST Handwritten Digit Classification , Twitter Sentiment Analysis 2 ) Email Spam Classifier 3 ) Fraud Detection System 4 ) Computer Vision : Face Recognition , Face Detection ## Note : * It`s not necessary to do all the projects mentioned above , you may choose them as per your comfort zone and commitment . Apart from that , you can also choose a project not mentioned above . Main motive is to do things , independent of the sources !! * Beginners , if you are fluent with the concepts by Month 2 end , you can consider picking up any 2 projects from the beginners section and then try to pick a project from Intermediate & Advanced Section . * If you are completing the courses mentioned ( in Month 2 ) still in Month 3 , then no worries . Complete the courses fully and patiently , then try doing the projects . * Details of the projects like what to do , how to do , datasets required etc. will be shared by the mid / end of Month 2 . Some more projects may be added and projects currently mentioned above may be removed as per the majority demands of the members . ## To all the members of 100 Days Of ML Telegram Group regarding the coursework , This is an independent coursework mainly designed for the members of the telegram group for 100 Days of Machine Learning initiated by Ayon Roy . All the members are free to move forward by making commitments to this Coursework , however it is not a compulsion to follow the specific path mentioned in the coursework ; you are free to utilize & commit your time for next 100 Days in the field of Machine Learning . I know that being a student / working professional , sometimes it may not be possible for you to devote 1 hour on a daily basis , due to exams and other life goals like friends , families etc. and its quite cool to keep learning ML alongside these . But the main point is that “ PATIENCE , COMMITMENT IS THE KEY TO SUCCESS “ , keeping this in mind ; you are requested to move forward . Its not compulsory that you start the course from 1/1/19 only , you may start later due to exams or any unavoidable conditions ; the group members are here to help you even after the suggested end of 100 Days Of ML i.e. 10th April , 2019 . But do understand that this initiative prompts you to devote 100 hours in total and I hope you all can do this as per your convenience . Don`t rush as per the coursework or other group members , take your time and dive into Machine Learning patiently and with full confidence . I along with mentors and other members are here to help you along the journey , but do consider asking your doubts only after searching for the same on Google and still if you face any difficulty , then we are here for your support ! Don`t worry about the monthly / weekly deadlines , try to understand the concepts well !! Take your time !!

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