Top Tweets for #CodeBasics
Day 22 of my Data Science + GenAI Journey
Today I learned:
Backpropagation & weight updates
Gradient Descent & learning rates
Building Neural Networks in PyTorch
DataLoaders for efficient training
Binary & Categorical Cross Entropy
#DeepLearning #Codebasics
Day 21 of my Data Science + GenAI Journey
Today I learned: Matrix multiplication powers neural networks, Why GPUs are crucial for Deep Learning, PyTorch tensors & GPU acceleration, Derivatives, partial derivatives & the Chain Rule, Autograd. #DeepLearning #Codebasics
Day 20 of my Data Science + GenAI journey
Today I learned: Neural Networks & Deep Learning fundamentals, Neurons, Perceptrons & MLPs, Activation Functions (ReLU, Sigmoid, Softmax, Tanh), CNNs, RNNs & Transformers, PyTorch vs TensorFlow, GPUs & TPUs.#Codebasics #DeepLearning
Day 19 of my Data Science + GenAI journey
Today I learned about: MLOps, MLflow, DagsHub, Data Drift, Concept Drift, PSI & CSI and Amazon SageMaker.
#DataScience #MachineLearning #AI #GenAI #MLOps #MLflow #AWS #SageMaker #Codebasics
Day 18 of my Data Science + GenAI journey
Built an end-to-end Credit Risk Model under @codebasicshub and learned:
- Feature selection needs both statistics and domain knowledge
- In finance, model interpretability matters as much as performance
#DataScience #GenAI #Codebasics
Day 17 of my Data Science + GenAI journey
Worked on a Healthcare Premium Prediction project today under guidance of @codebasicshub
Insights
• Never rely on a single model
• Margin of error matters more than just accuracy
• Reliable>flashy metrics
#DataScience #Codebasics
Day 16 of my Data Science + GenAI journey
Today I learned:
• K-Means Clustering
• SSE & Elbow Method
• Hierarchical Clustering + Dendrograms
• DBSCAN for outlier detection & irregular clusters
#DataScience #Codebasics #GenAI #MachineLearning
Day 15 of my Data Science + GenAI journey
Today I learned:
• Feature Engineering & VIF
• ROC-AUC and model evaluation
• K-Fold & Stratified Cross Validation
• GridSearchCV vs RandomizedSearchCV
• ML Project Lifecycle & MLOps
#DataScience #MachineLearning #GenAI #Codebasics
Day 14 of documenting my Data Science + GenAI journey
Today I learned about Ensemble Learning and powerful algorithms like Random Forest, Gradient Boosting, AdaBoost, and XGBoost.
#DataScience #MachineLearning #AI #GenAI #XGBoost #LearningInPublic #Codebasics
Day 13 of my Data Science + GenAI journey
Today I learned:
• Logistic Regression
• Precision, Recall & F1 Score
• Confusion Matrix
• SVMs & Kernels
• Feature Scaling
• Sklearn Pipelines
• Naive Bayes
• Decision Trees
• SMOTE for class imbalance
#DataScience #Codebasics
Day 12 of my documented Data Science + GenAI journey
Today I learned:
• Min-Max Scaling
• MSE & R² Score
• Underfitting vs Overfitting
• One-Hot Encoding
• Polynomial Regression
• Bias vs Variance
• L1 & L2 Regularization
#DataScience #MachineLearning #AI #Codebasics
Day 11 of my Data Science + GenAI journey
Learned:
• ML vs Rule-Based Programming
• Regression vs Classification
• Supervised vs Unsupervised Learning
• Slopes, Derivatives & Partial Derivatives
• Cost Function
• Gradient Descent
#MachineLearning #AI #GenAI #Codebasics
Day 10 of my documented Data Science + GenAI journey
Today I learned:
• Type 1 Error
• Type 2 Error
• A/B Testing
• T-Test
• Chi-Square Test
Statistics is starting to feel like the foundation behind real data-driven decisions.
One step closer every day.
#AI #Codebasics
Day 9 of my Data Science + GenAI Journey
Today I learned: Z Score Table, Confidence Intervals, Z Test, P-values, One-tailed Tests and Two-tailed Tests.
Really interesting to see how statistics helps turn data into evidence-backed decisions.
#DataScience #GenAI #Codebasics
Day 8 of my Data Science + GenAI journey
Completed a market research project to identify the ideal target audience for a new credit card launch.
Insight: The 18–25 age group emerged as the strongest target segment using countplots and exploratory data analysis.#Codebasics #GenAI
Day 7 of my Data Science + GenAI journey
Today’s work:
A project to identify the target audience for launching a new credit card using transaction data
Connected SQL + CSV data, Data sanity checks, Handled null values & outliers, Feature creation
#DataScience #GenAI #Codebasics
Day 6 of my Data Science + GenAI Journey
Learned:
• Probability basics
• Addition & multiplication rules
• Conditional probability
• Bayes’ theorem
• Normal distribution
• Skewness
• Standard deviation
• Z-score & outlier detection
#DataScience #AI #GenAI #Codebasics
Day 5 of my Data Science + GenAI Journey
Today I revised some core Statistics concepts.
Topics covered:
• Descriptive vs Inferential Statistics
• Percentiles & Quartiles
• Range & IQR
• Box/Whisker Plots
• Variance & Standard Deviation
#DataScience #GenAI #Codebasics
Day 4 of my Data Science + GenAI journey
Today I learned:
Types of data
Univariate, Bivariate & Multivariate Analysis
Histograms
Scatter Plots & Bubble Charts
Pie & Bar Charts
Descriptive & Inferential Statistics
#DataScience #GenAI #Codebasics #LearnInPublic
Day 3 of documenting my Data Science + GenAI journey
Today I learned:
SQL filtering (WHERE)
Aggregate functions (SUM, AVG, COUNT)
IF and CASE WHEN
All types of joins (INNER, LEFT, RIGHT, FULL, CROSS)
#DataScience #GenAI #SQL #LearnInPublic #Codebasics
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