100 Days of Machine Learning Journey: Week 2 Recap
Wow, made it through Week 2 of the 100 Days of ML journey, and what an incredible week it has been! Let’s take a look back at the progress, along with the lessons learned and emotions experienced throughout this thrilling adventure.
Day 8: NLP, TensorFlow Courses, and Caesar Cipher
- Completed Ai Planet’s NLP Course and implemented text preprocessing techniques.
- Finished two TensorFlow courses and moved closer to earning my TensorFlow Professional Certificate.
- Added a new question to my Anki deck related to Lasso and Ridge regularization techniques.
- Worked on a Caesar Cipher challenge, learning the power of functions in Python.
- Wrote the fourth book in a five-part e-book series.
- Submitted photos for a virtual graduation headshot, embracing technology to celebrate this milestone.
- Reviewed Anki deck to reinforce learning.
Feeling accomplished and enthusiastic about the journey ahead.
Day 9: Mastering TensorFlow, Taming Word Embeddings, and Embracing Virtual Headshots
- Earned my TensorFlow Developer Professional Certification, marking a significant step in my ML journey.
- Added a thought-provoking interview question to Anki deck.
- Explored Word2Vec and GloVe, training my own word embeddings.
- Registered for an upcoming webinar on creating LLM applications.
- Received AI-generated headshots from tryitonai.com, amazed by what technology can do. Some were hit or miss, but the impressive ones exceeded my expectations and are perfect for my virtual graduation headshot submission (See below).
Probably the best $17 I’ve ever spent. I now have a plethora of photos to choose from (you’re given 100) to submit for the graduation slideshow at University of Maine. Feeling rather teary-eyed because the reality of graduation is setting in as I go through the photos… It’s just wild what technology can do. Like—how is it my face?—yet it’s not!
Feeling more excited than ever about the discoveries that lie ahead.
Day 10: RNNs, Slowing Down, Self-Care, and Networking
On Day 10 of my 100 Days of ML journey, I took a step back to find the right balance between pushing my limits and maintaining my well-being. Here’s what transpired:
- Delved into Recurrent Neural Networks (RNNs) and implemented a simple RNN for text generation.
- Slowed down on the Python bootcamp for deeper understanding.
- Prioritized self-care, managing anxiety with comforting music.
- Completed a Codewars challenge, enhancing problem-solving skills.
- Began working on an automation script to eliminate busy work and improve focus.
- Reached out to hiring managers and recruiters to explore job opportunities.
Learning the importance of balance between knowledge pursuit and well-being.
Day 11: Data Preprocessing, Feature Engineering, and Titanic Dataset
- Showcased the importance of data preprocessing and feature engineering using the Titanic dataset.
- Cleaned and transformed data, visualizing relationships between features and the target variable.
- Prepared the dataset for ML models, gaining insights into data preprocessing and feature engineering.
- As a diligent coder, I incorporated a simple unit test to ensure the reliability of the data preprocessing steps.
Day 12: Decision Trees, Random Forests, and Life Progress
- Explored decision trees and random forests, implementing a decision tree classifier.
- Performed a unit test to ensure classifier functionality.
- Added a new interview question to my Anki deck: “Explain how a ROC curve works.”
- Made progress in personal areas, feeling grateful and more informed.
Outside of ML, I continued my personal journey, feeling relieved by my tattoo removal progress with Removery. I’m grateful for their expertise and care. Additionally, I had an enlightening meeting with Child Find for K’s eligibility meeting surrounding an IEP. I’m now feeling much more informed about his development and the support available to him.
Excited to build on newfound knowledge and take on new challenges.
Day 13: Support Vector Machines: Classification with a Margin of Confidence
- Learned the basics of Support Vector Machines (SVMs) and implemented an SVM classifier on the Breast Cancer Wisconsin dataset using Python’s Scikit-learn library.
- Visualized the decision boundary to understand SVM’s margin of confidence.
- Performed a unit test to ensure classifier functionality.
- I had a fruitful conversation with a new connection and experienced some FOMO, which led me to reformat the challenge and add new projects for the upcoming days. I also restructured my Anki interview questions.
In other news, I began compiling my certificates and accomplishments into a ‘Smile File,’ and submitted my selected headshot and a 25-word message for my virtual graduation ceremony. It was an emotional moment, but I’m excited about the journey ahead.
Ready to explore more fascinating aspects of machine learning.
Day 14: Image Classification with Convolutional Neural Networks (CNNs)
Week two of the 100DaysOfML challenge is now complete! Today, I tackled Convolutional Neural Networks (CNNs), a powerful type of deep learning model particularly suited for image recognition tasks.
- Implemented a CNN classifier using TensorFlow and Keras, applying it to the CIFAR-10 dataset, which consists of 60,000 color images across 10 distinct classes.
- Added a new interview question to Anki deck about generative and discriminative models.
- Gave haircuts to K & R, saving money and improving my hairstyling skills.
- Introduced a “Paper of the Week” component to enhance my understanding of cutting-edge research.
- Brainstormed ideas for collaborating with a teammate for the upcoming #prompthacks23 event.
- Tackled 3 Codewars challenges, sharpening my coding and problem-solving skills.
I’m deeply grateful for the incredible learning opportunities that have come my way during this journey. I feel humbled by the progress I’ve made and the concepts I’ve grasped in such a short time.
Join me as I forge ahead into Week 3 of the 100 Days of ML journey, full of passion and determination to conquer new challenges, expand my knowledge, and make an impact in the world using machine learning!
