Repository files navigation 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 Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles
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
Principal Component Analysis (PCA)
Simple Linear Regression | Multiple Linear Regression
Gradient Descent | Polynomial Regression | Bias Variance Trade-off
Ridge, Lasso, ElasticNet Regression
Logistic Regression | Decision Trees | Ensemble Learning
Voting, Bagging, Random Forest | AdaBoost | Gradient Boosting
Support Vector Machines (SVM) | Naive Bayes | XGBoost
K-Means Clustering | Hierarchical Clustering | DBSCAN
Handling Specific Challenges
Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE
Hyperparameter Tuning in Random Forest and Gradient Boosting
Introduction to Stacking and Blending Ensembles
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
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