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Welcom to Python and machine learning course

This repository is created by Amir Mardan to maintain and preview the contents for a Python and machine learning course prepared for Amirkabir University of Technology, Tehran, Iran. Please contact me via my email ([email protected]) for your lovely feedback and suggestions.

NOTE

I will push new contents weekly

1. Introduction to Python

1.1 General programming

  • An introduction
  • Required tools
  • Variables and data types
    • Numbers in Python
    • Strings in Python
    • Booleans in Python
    • List in Python
    • Dictionary in Python
  • Operators
    • Comparison operators
    • Logical operators
    • Membership operators
    • Bitwise operators
  • Control flow
    • if statements
    • match statements
    • for statements
    • while statements

1.2 Modular programming

  • Functions
  • Lambda functions
  • Built-in functions
    • map function
    • filter function
    • enumerate function
    • zip function
  • Classes / objects

2. Introduction to NumPy

  • Creating a NumPy array
    • Creating arrays from lists
    • Special arrays
  • Attributes of arrays
  • Data Selection
    • Array indexing
    • Array slicing
    • Array view vs copy
    • Conditional selection
  • Array manipulation
    • Shape of an array
    • Joining arrays
    • Splitting of arrays
  • Computation on NumPy arrays
  • Aggregations
    • Summation
    • Minimum and maximum
    • Variance and standard deviation
    • Mean and median
    • Find index

3. Data Manipulation with Pandas

3.1 Introduction to pandas

  • Introducing Pandas objects
    • The pandas Series object
    • The pandas DataFrame object
  • Data indexing and selection
    • Data selection in Series
    • Data selection in DataFrame
  • Handling missing data
    • Detecting the missing values
    • Dealing with missing values
  • IO in pandas

3.2 Data manipulation in using pandas

  • Basic operations in pandas
  • Combining datasets
    • Concat
    • Merge
    • Join
  • Aggregation
  • Groupby
  • Vectorized string

4 Visualization

4.1 Matplotlib

  • Basic matplotlib
    • Simple matplotlib
    • Subplots
    • Object-oriented method
  • Different types of plot
    • Scatter plot
    • Bar plot
    • Histogram
    • Pie chart
    • Box Plot
    • Violin plot
  • Images with matplotlib
  • Animation using matplotlib
    • Live graph with matplotlib

4.2 Seaborn

  • Relational plots
  • Distribution plots
    • displot
    • jointplot
    • pairplot
  • Categorical plots
    • Categorical scatter plots
    • Categorical distribution plots
    • Categorical estimate plots
  • Regression plots
  • FacetGrid
  • Customization
    • Style and theme
    • Colors

5 Data Analysis and Processing

5.1 Exploratory data analysis (EDA)

  • Initial general assessment
  • Basic analysis
  • Missing data
  • Outliers
  • Correlation

5.2 Data preparation

5.3 Data Cleaning

  • Initial general assessment
    • Rows with duplicated data
    • Columns with a single value
  • Outliers
    • Standard deviation method
    • Interquartile range method
  • Missing data
    • Remove rows with missing values
    • Filling missing values

5.4 Data Transforms

  • Scaling numerical data
    • Data normalization
    • Data standardization
    • Robust scaling
  • Encode categorical data
    • Ordinal Encoding
    • One Hot Encoding
    • Dummy Encoding
  • How to make distribution more Gaussian
    • Box-Cox transform
    • Yeo-Johnson transform
    • Quantile transform

6 Classical Machine Learning

6.1 Introduction to Machine Learning

6.2 Introduction to Scikit-Learn

  • Data presentation
  • Models in Scikit-learn
    • Simple linear regression example
    • Simple classification example
    • Simple dimensionality reduction example
    • Simple clustering example
  • Hyperparameters and model validation
    • Cross validation
    • Finding the best model
    • Grid Search

6.3 Regression 1

  • Ordinary Linear Regression
  • Linear Regression With Regularization
    • Ridge Regularization
    • Lasso Regularization
    • Combined Regularization
  • A Linear Regression Project
    • Exploratory Data Analysis
    • Data Cleaning
    • Data Processing Pipeline
  • Training and Evaluation
    • Training Curve

6.4 Classification 1

  • Logistic Regression
  • Support Vector Machine
  • Random Forest Classifier

6.5 Clustering 1

  • k-Means Clustering
  • Gaussian Mixture Models
  • Evaluation Clustering Models

7. Fully Connected Neural Networks (FCNNs)

7.1 Introduction to TensorFlow

  • Graph and Session
    • Build and Perform a Graph
    • Gradient in TensorFlow
  • Tensor types in TensorFlow
    • Constant
    • Variable
  • Tensor Manipulation
    • Creating A Tensors
    • Creating Special Tensors
    • Shape Manipulation
    • Slicing
  • Operators
    • Basic Arithmetic Operators
    • Comparison Operators
    • Logical And Bitwise Operators

7.2 Introduction To Fully Connected Neural Networks

  • Neural Network From Scratch
  • Neural Network With TensorFlow