Don's Machine Learning

Don’s Machine Learning: a blog about data science, machine learning and data engineering.

  • Serverless Deep Learning – AWS

    What will be covered in this post: This week we will create a clothes classification service in the cloud to identify images we upload and send. We will use AWS Lambda to serve our model. We can upload an image and send the URL to Lambda, which returns our predicted class. We will use our…

  • Neural networks and deep learning

    8. Neural Networks and Deep Learning 8. Neural networks and deep learning¶ This week, we’ll learn about neural nets and build a model for classifying images of clothes 8.1 Fashion classification 8.1b Setting up the Environment on Saturn Cloud 8.2 TensorFlow and Keras 8.3 Pre-trained convolutional neural networks 8.4 Convolutional neural networks 8.5 Transfer learning…

  • Deploying Machine Learning Models

    You have created a well-tuned, performing model. Now what? People can’t access it while it sits on your PC in a Jupyter Notebook. It has to be deployed so that it can be utilized. That is what this week was all about, deploying machine learning models. Let’s first import all of the libraries we will…

  • Evaluation Metrics for Classification

    Evaluation Metrics for Classification 4. Evaluation Metrics for Classification¶ Last week we trained a model for churn. How do we know if it’s good? The fourth week of Machine Learning Zoomcamp is about different metrics to evaluate a binary classifier. These measures include accuracy, confusion table, precision, recall, ROC curves(TPR, FRP, random model, and ideal…

  • Machine Learning for Classification

    Machine Learning for Classification 3. Machine Learning for Classification¶ 3.1 Churn prediction project 3.2 Data preparation 3.3 Setting up the validation framework 3.4 EDA 3.5 Feature importance: Churn rate and risk ratio 3.6 Feature importance: Mutual information 3.7 Feature importance: Correlation 3.8 One-hot encoding 3.9 Logistic regression 3.10 Training logistic regression with Scikit-Learn 3.11 Model…

  • Machine Learning for Regression

    2022-09-14-2-Machine-Learning-for-Regression 2. Machine Learning for Regression¶ 2.1 Car price prediction project 2.2 Data preparation 2.3 Exploratory data analysis 2.4 Setting up the validation framework 2.5 Linear regression 2.6 Linear regression: vector form 2.7 Training linear regression: Normal equation 2.8 Baseline model for car price prediction project 2.9 Root mean squared error 2.10 Using RMSE on…

  • Introduction to Numpy

    Introduction to NumPy Machine Learning Zoomcamp¶ 1.7 Introduction to NumPy¶ Plan: Creating arrays Multi-dimentional array Randomly generated arrays Element-wise operations Comparison operations Logical operations Summarizing operations In [5]: import numpy as np In [6]: np Out[6]: <module 'numpy' from '/home/don/miniconda3/lib/python3.8/site-packages/numpy/__init__.py'> Creating arrays¶ In [8]: np.zeros(10) Out[8]: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) In [11]: np.ones(10)…

  • A Beginners Pandas Introduction

    Introduction to Pandas Machine Learning Zoomcamp¶ 1.9 Introduction to Pandas¶ Plan: Data Frames Series Index Accessing elements Element-wise operations Filtering String operations Summarazing operations Missing values Grouping Getting the NumPy arrays In [1]: import numpy as np import pandas as pd Data Frames¶ In [11]: data = [ ['Nissan', 'Stanza', 1991, 138, 4, 'MANUAL', 'sedan', 2000], ['Hyundai',…

  • My Journey So Far

        I have been on the quest to become proficient in data science and machine learning. I don’t have a background in computer science and have no real experience with programming. I have, at times, fiddled with programming but nothing more than the basics. I am determined to become proficient in data science. I…