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Machine Learning for Medics

Transcript: Week 1: Intro to Python Week 2: Guided Machine Learning tutorial - classifying flowers Week 3: Independent project: cancer detection Run the following: import sys import scipy import numpy import matplotlib import pandas import sklearn # Check the versions of libraries def check_lib_version(): """ Checks the versions of various libraries used for ... :return: prints out versions of libraries ... """ print('Python: {}'.format(sys.version)) print('scipy: {}'.format(scipy.__version__)) print('numpy: {}'.format(numpy.__version__)) print('matplotlib: {}'.format(matplotlib.__version__)) print('pandas: {}'.format(pandas.__version__)) print('sklearn: {}'.format(sklearn.__version__)) checkLibVersion() Create your own cancer-detecting AI in 3 weeks 1. Intro to Python Introduction Course outline - Python: powerful in-built data analysis, graphing, machine learning, scientific toolkits, simple syntax - Function (programming): something that takes an input, processes it, and outputs - Library (programming): a set of functions that someone has put together - Python: many scientists, developers, mathematicians etc. have written many libraries for - essentially pre-written things including machine learning algorithms 1. Intro to Python: Setup - What technological challenges are companies, including medical, facing today? - Big data, machine learning, AI 1. Install python from 2. Install pycharm from or Linux cmd 3. Open pycharm and point pycharm to your python installation (File - Settings - Project - Project Interpreter - select python.exe where you installed it) 4. Install libraries numpy, scipy, matplotlib, pandas, scikit-learn, sklearn (under Project Interpreter click green plus in top right and install those libraries from the window) 1. Intro to Python: check install Data cleaning Machine Learning for Medics

Machine Learning presentation

Transcript: Machine Learning Tejveer, Faizan, Fahim, Amanjot What is Machine Learning? What is Machine Learning Machine learning is the process of algorithms that can self improve automatically through experience and using data. How Machine Learning Works How machine learning works Decision Process Error Function Model Optimization Process Supervised machine learning Unsupervised machine learning Semi-supervised learning Machine learning methods History of Machine Learning 1950 - Turing Test 1952 - Arthur Samuel's Game 1957 - Frank Rosenblatt's perceptron 1967 - Nearest Neighbor 1979 - Stanford's Cart 1985 - NetTalk 1997 - IBM's Deep Blue 1999 - University of Chicago creates cancer computer 2006 - Geoffrey Hinton coins term Deep Learning (Lead to present day) History Movies and Machine Learning Movies and Machine Learning - Throughout history, tech seen on movies helped shape today's - Metropolis by Fritz Lang - 2001 by Kubrick - Blade Runner Why is it Important in the Present? Why is it important in the present Importance of collecting data for future Various uses worldwide in different systems System Examples Speech Recognition Car self driving feature Facial Recognition General Medicine Many more examples! Machine Learning in 10 Years Where Will This Technology be in 10 Years? - Machine learning will be implemented almost anywhere - Pharmacies will implement machine learning - Prediction of diseases, discovery of drugs, and electronic health records - Car companies are already implementing machine learning in self driving cars Quantum Computing How Quantum Computing will Effect the Future of Computer Learning What is quantum computing? How will it effect machine learning? How much more better is quantum computing? How will machine learning effect the economy in the future? Economy - Market predicted to rise from 8.43 billion to around 117.19 billion - Will be implemented in self driving cars and the pharmaceutical market - Car & pharmaceutical market will increase which will cause economy to increase

Machine Learning for Business

Transcript: How and why to implement ML in your business 25/10/2019 Manja Bogicevic Chief Artificial Intelligence Officer About Who am I? 1. I am one of the first women Machine Learning Entrepreneurs in Serbia (Women in IT Awards) 2. I am on the mission to become NextForbesUnder30 (3 years to go) 3. I finished Economics and now pursuing my Micro-Masters on MIT in Boston 4. I am ex-professional tennis player and I have ran 4 half-marathons Kageera Clients Future Vision ML Workflow Level I Problem discovery Machine Learning workflow Level II Machine Learning Due Diligence Level III Gain Profit & Optimize performance Level I Problem discovery 1. Find out whether ML is good fit for your business 3. Collect Data and Define the Dataset 2. Make a plan Level II Machine Learning Due Diligence 4. Machine Learning Model evaluation 6. Continuously improve ML solution 5. Deliver ML live solution in less than 3 months Level III Gain profit and Optimize your business performance 7. Visualize & Present insights 9. Handover and make a decision 8. Long-term support Use Cases 1. Fraud detection Use cases in Finance 2. Churn prediction 3.Making portfolio management better and cheaper ML trends for Legal profession: 1. Review documents and better perform due diligence 2.Contract review 3. Predict legal outcomes The Team + + What does a great ML team look like? Consulting experience Engineer skills Data Science & Machine Learning knowledge Contact Info @manjabogicevic KAGEERA.COM Contact Details

Machine Learning Presentation

Transcript: My presentation MACHINE LEARNING by : Vinod Reddy B Thank You! Simple Introduction and History of MACHINE LEARNING INTRODUCTION IS AN ART OF COMPUTER THINKING Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. History The term machine learning was coined in 1959 by Arthur Samuel Arthur Samuel is an American IBMer and pioneer in the field of computer gaming and artificial intelligence. Conventional Programming : The approach of conventional programming is to feed the computer with a set of instructions for a defined set of scenarios. ex : C, C++, Java, JavaScript, Python, etc DIFFERENCE Difference b/w Conventional Programming v/s Machine Learning Difference Types Supervised Learning Example :Weighting Machine Unsupervised Learning Example : Cricket Scores Reinforcement Learning Real Time Examples for ML Examples or Applications Traffic prediction Virtual personal assistant Online transportation Social media services Email spam filtering Product recommendation Online fraud detection Example with Pictures Advantages of Machine Learning Merits and Demerits Fast, Accurate, Efficient. Automation of most applications. Wide range of real life applications. Enhanced cyber security and spam detection. No human Intervention is needed. Handling multi-dimensional data. Disadvantages of Machine Learning Disadvantages It is very difficult to identify and rectify the errors. Data Acquisition. Interpretation of results Requires more time and space. Conclusion Conclusion We have a simple overview of some techniques in machine learning. Furthermore, there are more and more techniques apply machine learning as a solution. In the future, machine learning will play an important role in our daily life.

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