- Datatypes
- Difference between '==' and 'is'
- Some useful built-in functions
- List comprehension
- Lambda function
- Unpacking
- More about mutable and immutable datatypes
- Why we use Numpy?
- Creating and inspecting array
- Indexing and slicing
- Operators
- Mask and filter
- Copy vs view
- Array manipulation
- Understanding axes
- Broadcasting
- Sparse matrix
- What is Pandas?
- Series, dataframe and index
- Data indexing and selection
- Operators
- Doing some stuffs!
- Datatype
- Change datatype
- Describe
- Rename columns
- Rename rows
- Replace values
- Finding unique values
- Deleting column
- Deleting row
- Remove duplicates
- Mask and filter
- Apply funciton to the data
- Convert to numpy array
- Handling missing data
- Aggregation and grouping
- Simple plot
- Title, Ticks, Labels, and Legends
- Colors, Markers, and Line Styles
- Subplot, Subplots and subplot2grid
- Histogram, bar, scatter, pie and heatmap
- Saving Plots to File
- What is machine learning?
- Preprocessing
- Identifying and handling the missing values
- Identifying outliers
- Encoding the categorical data
- Transforming the dataset
- Feature scaling
- Standardization
- Normalization
- Dimension reduction
- Principal Component Analysis (PCA)
- Train test split
- Feature selection
- Model selection
- Supervised learning
- Classification
- KNN
- SVM
- Random forest
- Decision tree
- Logistic regression
- Regression
- Linear regression
- Polynomial regression
- None linear regression
- Multiple linear regression
- Unsupervised learning
- Kmeans
- DBSCAN
- Semi supervised learning
- Model evaluation
- Classification
- Jaccard
- F1-score
- log-loss
- Clustering
- Sum of Squared Error (SSE) score
- Silhouette coefficient
- Prediction