Skip to content

README.md

Latest commit

 

History

History
81 lines (47 loc) · 2.29 KB

README.md

File metadata and controls

81 lines (47 loc) · 2.29 KB
Raw

100 Days of Machine Learning

Introduction to Machine Learning

  • What is Machine Learning?
  • AI Vs ML Vs DL for Beginners in Hindi

Types of Machine Learning

  • Batch Machine Learning | Offline Vs Online Learning
  • Online Machine Learning | Online Learning | Online Vs Offline Machine Learning
  • Instance-Based Vs Model-Based Learning

Challenges and Applications

  • Challenges in Machine Learning | Problems in Machine Learning
  • Application of Machine Learning | Real Life Machine Learning Applications

Machine Learning Development Life Cycle

  • Machine Learning Development Life Cycle | MLDLC in Data Science

Data Science Roles

  • Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles

Data Handling

  • What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning
  • Handling Data: CSV files, JSON/SQL, Fetching Data From API, Web Scraping
  • Understanding Your Data | Exploratory Data Analysis (EDA)
  • Feature Engineering | Feature Scaling | Encoding Categorical Data
  • Handling Date and Time Variables | Handling Missing Data | Outlier Detection and Removal

Machine Learning Techniques

  • Machine Learning Pipelines | Column Transformer | Function Transformer
  • Binning, Binarization, Discretization
  • Handling Mixed Variables | Curse of Dimensionality

Dimensionality Reduction

  • Principal Component Analysis (PCA)

Regression

  • Simple Linear Regression | Multiple Linear Regression
  • Gradient Descent | Polynomial Regression | Bias Variance Trade-off
  • Ridge, Lasso, ElasticNet Regression

Classification

  • Logistic Regression | Decision Trees | Ensemble Learning
  • Voting, Bagging, Random Forest | AdaBoost | Gradient Boosting
  • Support Vector Machines (SVM) | Naive Bayes | XGBoost

Clustering

  • K-Means Clustering | Hierarchical Clustering | DBSCAN

Handling Specific Challenges

  • Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE

Hyperparameter Tuning

  • Hyperparameter Tuning in Random Forest and Gradient Boosting

Miscellaneous

  • Introduction to Stacking and Blending Ensembles

Additional Resources

  • Installation: Anaconda, Jupyter Notebook, Google Colab
  • End to End Toy Project | How to Frame a Machine Learning Problem
  • Readying Data for Modeling: Pandas Profiling, Data Cleaning Techniques