π Day 58 β Journey of Data Science
Today I learned about Static and Templates folders in Flask and how they help organize CSS, JavaScript, images, and HTML templates in web applications. ππ
#Flask#Python#WebDevelopment#DataScience#Day58
π Day 57 β Journey of Data Science
Today I learned HTML, CSS, and the basics of Flask for building web applications with Data Science and Machine Learning. ππ€π
#DataScience#Flask#Python#MachineLearning#Day57
π Day 56 β Journey of Data Science
Today I trained a Neural Network using TensorFlow on the MNIST dataset to recognize handwritten digits and make predictions. π€π§ π’
#DeepLearning#TensorFlow#MNIST#MachineLearning#Day56
π Day 55 β Journey of Data Science
Today I learned about the MNIST dataset and explored how to understand and visualize handwritten digit images for Deep Learning. π§ π
#DeepLearning#MNIST#MachineLearning#DataScience#Day55
π Day 54 β Journey of Data Science
Today I learned about the Perceptron, Deep Learning terminology, training a perceptron using Scikit-learn, compared PyTorch vs TensorFlow vs Keras, and installed TensorFlow to start building deep learning models.π€π§
#TensorFlow#DataScience
π Day 53 β Journey of Data Science
Today I started Deep Learning and learned the basics of Neural Networks and how they help solve complex Machine Learning problems. π€π§
#DeepLearning#MachineLearning#DataScience#Python#Day53
π Day 52 β Journey of Data Science
Today I learned Column Transformers, ML pipelines, training models on preprocessed data, and model persistence with Joblib. Also completed the California Housing Price Prediction project
#MachineLearning#DataScience#Python#ScikitLearn#Day52
π Day 51 β Journey of Data Science
Today I learned about handling categorical data, feature scaling, data transformation, and building pipelines in Scikit-learn to prepare data for Machine Learning models. π€π
#MachineLearning#DataScience#Python#ScikitLearn#Day51
π Day 50 β Journey of Data Science
Today I learned Stratified Shuffle Split, data visualization, and handling missing data to prepare datasets for Machine Learning. π€π
#MachineLearning#DataScience#Python#Day50
π Day 49 β Journey of Data Science
Today I worked with the California Housing dataset, performed EDA to understand the data, and created a test set for training and evaluating ML models. π€π
#MachineLearning#DataScience#Python#EDA#Day49
π Day 48 β Journey of Data Science
Today I learned about measuring prediction accuracy, explored a house price prediction example, and learned MAE and RMSE for evaluating Machine Learning models. π€π
#MachineLearning#DataScience#Python#Day48
πDay 47βJourney of Data Science
Today I learned how to train a Machine Learning algorithm on the Iris dataset. I explored the process of preparing data, training a model, and making predictions using Scikit-learn.π€π
#MachineLearning#DataScience#Python#ScikitLearn#Day47
π Day 46 β Journey of Data Science
Today I learned the Machine Learning problem-solving process and explored dataset sources like Kaggle, OpenML, UCI Repository, and TensorFlow Datasets. π€π
#MachineLearning#DataScience#Python#100DaysOfCode#Day46
π Day 45 β Journey of Data Science
Today I learned about Supervised, Unsupervised, and Reinforcement Learning. I also explored how to choose the right ML technique based on the problem and available data. π€π
#MachineLearning#DataScience#Python#100DaysOfCode#Day45
πData Science Update
Completed SQL and Probability, and recently started my Machine Learning journey. Built my first ML model using Scikit-learn and I'm currently on Day 45 of my learning journey.π€π
Forgot to post updatesπ
#DataScience#MachineLearning#Python#100DaysOfCode
π Day 34 β Journey of Data Science
Learned how to delete data from tables in SQL, manage transactions with autocommit, commit, and rollback, get the current date & time, and use constraints to maintain data integrity. ποΈπ
#DataScience#SQL#Python#100DaysOfCode#Day34
π Day 33 β Journey of Data Science
Today I learned how to select data from a table and update existing data in a table using SQL queries. These operations help in retrieving and modifying data efficiently in databases.
#Python#SQL#Databases#DataScience#100DaysOfCode#Day33
π Day 32 β Journey of Data Science
Today I learned the introduction to databases, including how to create databases and tables, modify tables, and insert data into tables. ποΈπ
#Python#SQL#Databases#DataScience#100DaysOfCode#Day32
π Day 30 β Journey of Data Science
Today I learned the basics of Web Scraping and got an introduction to HTML for extracting data from websites, which is an important part of real-world data collection. ππ
#Python#WebScraping#HTML#DataCollection#DataScience#100DaysOfCode