What You Will Learn
- Introduction to Machine Learning concepts
- Supervised vs. Unsupervised Learning
- Familiarization with Datasets
- Data Exploration and Analysis
- Machine Learning Libraries
Duration
8 weeks
Level
Beginner To Intermediate
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 to logistic Regression
- Introduction to logistic Regression
- Confusion matrix.precision,recall,f1-score
- Bias , Variance trade off
Support Vector Machine
- Introduction to Support Vector Machine
- Implementation
- Kernel and RBF function
- Evaluation
K-Nearest Neighbor
- Introduction to KNN
- Implementation
- Evaluation
Naive Bayes Classifier
- Introduction to Naive Bayes
- Implementation of Naive Bayes
- Evaluation
Decision Trees
- Introduction to Decision Tree
- Entropy and Information Gain
- Implementation
- Evaluation
Clustering
- Introduction to Clustering
- K-means,Hierarchical Clustering
- Implementation
Project Work
- Implementing Logistic Regression on MNIST dataset
- House Price prediction
- Text Classfication