Showing posts from August, 2020

Motivation and Machine Learning (Lesson 3) Part 1

One of Machine Learning's most important process is model training. This is the process in which we transform data into trained ML models, hence its importance.  Before we train our model, it  is important we master data handling,  data preparation and data management because proper data is is a key ingredient for successful ML models. Issues like high bias, classification problems, poor performance are often related to problems on the data itself. So it's really crucial to feed proper, accurate, clean and high quality data into our machine learning models for training. Model training is a core process in Machine learning that allows us to build, train and check the quality of ML models.  Data wrangling is the process through which we clean data, restructure it and enrich the data to transform it into a format that is much more suitable for the training process of Machine Learning Algorithms. ********* Managing data for machine learning work on Azure needs us to understand 2

Motivation and Machine Learning - Part 3

Days 10-12 2.25(learning functions):  ML is a process for learning functions and models are specific representations of those functions gotten from training data. Y=f(x) + e Where y is output, x is input, f is function and e is the irreducible error. ML algorithms learn from a target function F that describes the mapping. 2.26: parametric and non-parametric algorithms Based on the size and structure of a function ML algorithms try to learn, they can be classified into parametric and non-parametric. parametric: these algorithms maps into a known functional form. It starts by assuming a  form, then learning its coefficients based on that form. non parametric: these algorithms do not make assumptions regarding the mapping between input and output data, so they are free to learn any functional form from the data. Benefits of parametric functions: simpler to understand, faster, easy interpretation and requires less training data. Disadvantages; it's highly constrained, limited com

Motivation and Machine Learning - Part2

Day 5 Learnt about Tabular Data - data simply arranged in tabular format like in an Excel spreadsheet with rows, columns and cells where they intersect. Rows describe a single observation, product or entity Columns describe the properties or features of the item. Column values can be continuous (countable numeric values that can take any value) or discrete(categorical) values which have a limited range and needs to be converted. Cells represent single value in row and column intersection. In machine learning we ultimately work with numbers specifically vectors. So everything that isn't numbers like the categorical variables, text, pictures, videos, audio inputs are eventually converted to array of numbers. Day 6 Revision - 2.8 Scaling Data and 2.9 Encoding categorical data The point of scaling Data is transforming it to fit within some range or scale say 0 - 1 or 1-100. It doesn't affect the algorithms because every value is scaled same way. It can speed up the training process

Motivation and Machine Learning - Part1

  Been a long while here. Been up and running as usual, working my butt off to make a living and see how I can contribute to making the world a better place the little way I can.  So a lot has happened since the last time I wrote here and i am grateful for everything. I got selected for the Phase 1 of a Machine Learning challenge course sponsored by Microsoft in collaboration with Udacity and it's been great so far. My network of international friends and acquaintances really grew by a significant percentage and I have had the opportunity to share some of m knowledge and skills with lots of people around the world as a student leader. Machine Learning is really awesome stuff that is poised to create a lot of opportunities in today's world while topping over a lot of traditional/manual processes. And I think you should pay attention it. That's what i have been doing. And I ave been thinking of a way to connect ML/AI to Motivating and inspiring the best they can be. How can