A step towards machine learning at accionlabs
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This document provides an overview of machine learning including definitions of common techniques like supervised learning, unsupervised learning, and reinforcement learning. It discusses applications of machine learning across various domains like vision, natural language processing, and speech recognition. Additionally, it outlines machine learning life cycles and lists tools, technologies, and resources for learning and practicing machine learning.
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A step towards machine learning at accionlabs
- 1. Episode #1: ML bootcamp A first step towards Machine Learning Shabinesh Sivaraj @shabinesh Chetan Khatri @khatri_chetan
- 2. What’s Machine Learning? It is a subfield of AI concerned with algorithms that allow computer to learn from examples and experience.
- 4. Machine Learning - The Frontier in Artificial Intelligence Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Deep Learning /Deep Neural Network
- 5. Applications of Machine learning Vision Natural Language Speech Conversational Dialog Agent
- 6. What Can be done with Machine learning !
- 12. Mobile first to AI first World !
- 19. “Mobile first to AI First” Rule User Interaction model is fundamentally changes, all product applies Machine Learning / Deep learning i.e AI
- 20. Mobile first to AI First People are taking the best experiences and demanding they receive the same or better everywhere. - Jim Lyski, Chief Marketing Officer - Carmax.
- 21. Mobile first to AI first
- 24. Use cases with Machine learning ❏ Recommendation - Personalize User experience. ❏ Natural Language Processing - Understanding linguistics. ❏ Financial Trading - Predicting Stock market. ❏ Marketing and Sales - Target Audience. ❏ Healthcare - Detection of Diabetic Eye Disease, Assisting Pathologists in Detecting Cancer, Dermatology diseases classification. Accuracy on DNA Sequencing, Drug discovery and material science. ❏ Sentiment Analysis - to understand customer’s view on product. ❏ Self-Driving Car - to avoid accident due to driver’s mistake. ❏ Object Detection - to Detect which object is there at Image / Video. ❏ Object Segmentation - to classify all the image / objects from image / videos. ❏ Dialog Agent - to ask question and get answer from specific branch of corpus. ❏ Search - to provide the information based on customer behavior. ❏ Fraud Detection - Ex. Spam Filtering And a lot ….
- 25. Data Analytics Lifecycle ● Understand the Business ● Understand the Data ● Cleanse the Data ● Do Analytics the Data ● Predict the Data ● Visualize the data ● Build Insight that helps to grow Business Revenue ● Explain to Executive (CxO) ● Take Decision ● Increase Revenue
- 26. Machine Learning Life cycle 1. Data Quality (Removing Noisy, Missing Data) 2. Feature Engineering 3. Choosing Best Model: " based on culture of Data, For ex. If continues data-points go with Linear Regression , If categorical binomial prediction requires then go with Logistic Regression, For Random sample of data(Feature randomization) and have better generalization performance. other like Gradient Boosting Trees for optimal linear combination of trees and weighted sum of predictions of individual trees." Try from Linear Regression to Deep Learning (RNN, CNN) 4. Ensemble Model (Regression + Random Forest + XGBoost) 5. Tune Hyper-parameters(For ex in Deep Neural Network, Needs to tune mini-batch size, learning rate, epoch, hidden layers) 6. Model Compression - Port model to embedded / mobile devices using Compress matrices(Sparsify, Shrink, Break, Quantize) 7. Deploy to Embedded device.
- 28. Tools & Technologies ...
- 29. Machine learning - Thought Process
- 30. FUD? Learning methods ● 30 days challenges ● Learn by doing it - Just do it ● Try understanding the math behind it. ● Write blogs ● Join local meetup groups ● Conferences ● Read people’s code, Believe in Open Source Contribution.
- 31. The Mathematics Matrix of life Linear Algebra Probability of you learning Slope of Calculus
- 32. Minimalistic learning path ● Pick a language-Python or R ● Start Data Analysis ○ R: dplyr, tidyr, stringr, reshape2 ○ Python: Numpy, Pandas ● Data Visualization ○ R: ggplot ○ Python: Matplotlib ● Statistics: ○ http://www-bcf.usc.edu/~gareth/ISL/ ○ https://www.openintro.org/stat/textbook. php?stat_book=os ● Learn to build models: ○ Learning techniques ○ Algorithms ● Linear Regression ● Logistic Regression ● Decision Trees ● KNN (K- Nearest Neighbors) ● K-Means Clustering ● Market Basket Analysis (Associative Rule Mining) ● Naïve Bayes And a lot !
- 33. Resources ● https://www.kaggle.com ● https://www.udacity.com ● https://www.coursera.org ● https://ocw.mit.edu ● https://www.edx.org ● https://see.stanford.edu/C ourse/CS229 ● http://colah.github.io/ ● http://distill.pub/ And a lot !
- 34. 1st month Language, Practice 2nd month Data Analysis tools and Linear Algebra, practice 6th month Data visualization, Probability distribution, practice 10th month Statistics, learning techniques, Algo. 12th month Practice, practice, practice
- 35. Homework 1. No more Windows, it must be Linux. 2. Be ready with Environment - Python 2.7 and Anaconda suite. Download URL 3. Make your hands dirty with Linear Algebra and Calculus. 4. For Upcoming sessions, Attendees are advised to learn basics of Python before attending the workshop. At the bare minimum, attendees should be knowing Sections 1 through 5.1 in this book: http://anandology.com/python-practice-book/