TODO: This file is a work in progress
Recommended learning path is Level-1 > Level-2 > Level-3.
Here we discuss the core theory and concepts of AI/ML.
- ai_essentials.md
- how_to_learn.md
- ieee_ethics.md
- pandas_basics.md
- plots.md
- probability.md
- python_modules.md
- statistics.md
- tensorflow.md
- unit_test.md
- why_math.md
- audio.md
- bias_variance_1.md
- bias_variance_2.md
- common_mistakes.md
- confusion_matrix.md
- cross_validation.md
- data_prep.md
- data_prep_code.md
- data_science_primer.md
- dimensionality_reduction.md
- discrete_prob_dist.md
- eda.md
- feature_engineering.md
- ml_algorithms.md
- ml_crash_course.md
- performance_metrics.md
- regression.md
- sgd.md
- train_test_split.md
Here we discuss the more advanced theory and concepts of AI/ML.
- anomaly_detection.md
- cython.md
- deploy.md
- imbalanced_classification.md
- kedro.md
- mlflow.md
- mlops.md
- openml.md
- performance.md
- pipelines.md
- small_dataset.md
- smote.md
- tuning.md
- xai.md
- fft.md
- time_series_analysis.md
- time_series_decomposition.md
- time_series_forecast.md
- time_series_plots.md
- time_series_pytorch.md
- time_series_stationary.md
- time_series_tips.md
- ai_project_ideas.md
- ai_resume.md
- ai_tips.md
- datasets.md
- gan_tips.md
- linux_tools.md
- mac_tips.md
- ml_tips.md
Here we discuss the more advanced topics of AI/ML.
- av_risks.md
- cv_tips.md
- factor_analysis.md
- nlp_llm.md
- prompt_engineering.md
- rl.md
- rl_lewisu.md
- robotics.md
- tinyml.md
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