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 (mardan.amir.h@gmail.com) 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