Topics covered are:
- Values and Variables
- Expressions and Arithmetic
- Conditional Execution
- Iteration
- Functions
- Objects
- Data Structure (List, Dictionary, tuple, Set)
- Handling Exceptions
- Custom Classes
- Class Design: Composition and Inheritance
- Filter and select data
- Treat missing values
- Remove duplicates
- Concatenate and transform data
- Group and aggregate data
- Create standard line, bar, and pie plots
- Define plot elements
- Format plots
- Create labels and annotations,
- Create visualizations from time series data
- Construct histograms, box plots, and scatter plots
- Use NumPy arithmetic
- Generate summary statistics
- Summarize categorical data
- Parametric methods
- Non-parametric methods
- Transform dataset distributions
- Line of Best-Fit
- Decision Tree
- Factor Analysis
- Gradient Decent
- K-fold (Best Model Selection)
- K-means Clustering
- KNN
- Linear Regression
- Multiple Regression
- Logistic Regression
- Logistic multiclass
- Naive Bayes Classifier
- One Hot Encoder
- PCA
- Random Forest
- Regression Pickle
- Support Vector Machine
Neural Networks
Modeling human brain, learning paradigms, artificial neurons, neural networks applications, feed forward neural networks, optimization, the learning algorithm, Error calculation, Gradient calculation, Back propagation, building networks, XOR problem, iris datasets, handling datasets
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
- Email spam filtering using Bayesian text classifier
- Digit Recognition (digital or handwritten), (CNN)
- Image recognition using Tensor flow and Keras (ANN )
- Movie review classification (ANN)
- Stock market or Forex analysis (RNN)