Introduction
- Introduction to Machine Learning concepts
- Supervised vs. Unsupervised Learning
- Familiarization with Datasets
- Data Exploration and Analysis
- Machine Learning Libraries
Data Preprocessing
- Importing the Libraries
- Importing the Dataset
- Missing Data
- Numerical,Categorical Data
- Exploratory Data Analysis
- Splitting the Dataset into the Training set and Test set
- Feature Scaling
Linear Regression
- Introduction to Linear Regression
- Linear regression implementation
- Evaluate Model Performance
- Multiple Regression and Feature Importance
- Cross validation
Logistic Regression
- Introduction
- Logistic Regression Implementation
- Confusion Matrix, Precision, Recall and F1 Score
- Precision and Recall Tradeoff
Support Vector Machine
- Introduction
- Implementation
- kernel and RBF function
- Evaluation
K-Nearest Neighbor
- Introduction
- Implementation
- Evaluation
Naive Bayes Classifier
- Introduction
- Implementation
- Evaluation
Decision Trees
- Introduction
- Entropy and Information Gain
- Implementation
- Evaluation
Clustering
- Introduction
- K-means , Hierarchical clustering
- Implementation
Project work
- Logistic Regression implemenation on MNIST Dataset
- Text classification
- House Price prediction
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