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Python & Machine Learning

Basic Python Webinar

Lesson 1
Introduction to Jupyter IDE
Introduction to Python
-    Expressions
-    Data types
-    Arithmetic operations
-    Control flows

Lesson 2
More about Python
-    Functions
-    String operations
-    Containers (list, tuple, set, dictionary)
-    Introduction to encoding
-    Manipulating dates and times (the datetime module)

Lesson 3
Python libraries for numerical computations and data analysis
-    NumPy

Lesson 4

Python libraries for numerical computations and data analysis
-    Pandas

Database management with Python
-    MySQL

Lesson 5
Visualization with Python libraries
-    Matplotlib
-    A higher-level approach: Seaborn

 Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

  Lesson 4: Staff / Student

  Lesson 5: Staff / Student

 Course Material: Staff / Student

 

Machine Learning with Python (1) Workshop

Lesson 1

  1. Introduction to Machine Learning
    1. Supervised learning, unsupervised learning and reinforcement learning
    2. Feature engineering
      - Numerical data
      - Categorical data
      - Text feature
      - Image feature
    3. Modal pipeline
  2. Naïve Bayes Classifier
    1. Conditional probability and Bayes Theorem
    2. Gaussian Naïve Bayes
    3. Multinomial Naïve Bayes

 Lesson 2

  1. Linear Regression
    1. Formulation and Gradient Descent
    2. Regression variations
      - Simple linear regression
      - Multiple linear regression
      - Basis function regression
    3. Regularization
      - Ridge, lasso and elastic net
  2. Logistic Regression
    1. Formulation and Cost function (log loss)
    2. Example on breast cancer dataset

 Lesson 3

  1. Support Vector Machine
    1. Basic Linear Algebra
    2. SVM optimization problem
      - Linear and nonlinear boundary
      - Soft margins
    3. SVM on face recognition
  2. Decision Tree and Random Forest
    1. Decision Tree
      - Terminology and mathematical expression
      - Decision boundary
    2. Random Forest
      - Classification and regression
    3. Visualizing tree models
    4. Problems with Tree-based algorithm
      - Overfitting
      - Bias on imbalance dataset

 Lesson 4

  1. Principal Component Analysis
    1. Linear algebra prerequisite
      - Orthogonal basis, eigenvectors and eigenvalues, covariance matrix
    2. Applications of PCA
      - Dimensional reduction
      - Visualization of high dimensional data
      - Noise filtering
    3. Example: combine application with SVM to improve performance on face recognition
  2. K-Means Clustering
    1. Lloyd’s algorithm
    2. Challenges of using K-means
    3. Non-linear boundary problems
      - Spectral clustering
    4. Use cases
      - Data clustering and color compression

 Lesson 5

  1. Modal Validation I
    1. Evaluation metrics for classification
      - Accuracy, precision, recall, f1 score
    2. Evaluation metrics for regression
      - MAE, MSE and coefficient of determination
    3. Training and testing
      - Splitting data
      - K-fold cross validation
      - Leave-one-out cross validation
  2. Modal Validation II
    1. Bias-variance trade-off
    2. Validation curve
    3. Learning curve
    4. Hyperparameter search
      - Grid search and random search

 Lesson 6

  1. Handling Imbalanced dataset
    1. Choosing the right metrics
    2. Resampling
      - Random sampling
      - Undersampling
       (1) Tomek Links
       (2) Near-Miss
      - Oversampling
       (1) SMOTE
       (2) ADASYN
  2. Putting it all together – survival analysis
    1. Introduction to the problem
    2. Exploratory Data Analysis (EDA)
    3. Filling missing data
    4. Feature engineering
    5. Classification with random forest
    6. Hyperparameter tuning

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

  Lesson 4: Staff / Student

  Lesson 5: Staff / Student

  Lesson 6: Staff / Student

 Course Material: Staff / Student

 

Machine Learning with Python (2) Workshop

Lesson 1
- Introduction and Terminology
- Time Series Plot
- Common Time Series Transformation

  • Log, Power, Box-Cox Transformations

- Moving Average

  • Simple Moving Average and Exponential Moving Average

- Naïve Prediction Method

  • Seasonal Naïve Method, Extrapolation

- Time Series Decomposition

- Stationarity

  • Augmented Dicky Fuller Test

 Lesson 2
- Univariate Time Series Forecast

  • Exponential Smoothing
    • Single, Double and Triple (Holt’s Winter) Exponential Smoothing
    • Damped Method
  • ARIMA Model
    • Autoregression Model, Moving Average Model, Autoregressive Moving Average Model, Non-seasonal and Seasonal ARIMA Model

 Lesson 3
- Multivariate Time Series Forecast

  • Vector Auto Regression (VAR)
    • Order selection
    • Granger’s Causality Test
    • Order of Integration
    • Durbin Watson Statistic
  • Long Short-Term Memory (LSTM)
    • Introduction to Neural Network
      • Artificial Neural Network, Deep Neural Network, Recurrent Neural Network
    • Introduction to LSTM
    • Time Series Forecast with LSTM using Keras

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

 Course Material: Staff / Student

 

Machine Learning Fundamentals Webinar

  • Introduction to Artificial Intelligence, Machine Learning and Deep Learning
  • Data Visualization with Orange
  • Data Preparation using Python
  • Business Modelling for Data Analysis
  • Supervised Learning and Unsupervised Learning
  • Classification: K Nearest Neighbors, Neural Network...
  • Clustering: K-means Clustering...

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

Course Material: Staff / Student

  

Machine Learning Techniques Webinar

  • Lesson 1 - Regression and Sequential Pattern I
    • Regression Problem Introduction
    • Overfitting and Generalization
    • Error Function
    • Linear Regression
  • Lesson 2 - Regression and Sequential Pattern II
    • Logistic Regression
    • Sequential Pattern Problem Introduction
    • Abnormal Detection
  • Lesson 3 - Classification and Neural Network I
    • Linear Classifiers and Logistic Regression
    • Overfitting and Regularization in Logistic Regression
    • Decision Trees
    • Boosting
    • Handling Missing Data
    • Naive Bayes Classifer
    • Support Vector Machines
  • Lesson 4 - Classification and Neural Network II
    • Basic Deep Neural Network
    • Convolution Neural Network
    • Generative Adversarial Network
  • Lesson 5 - Clustering
    • Nearest Neighbor Search (KNN)
    • Clustering with K-means
    • Mixture Models
  • Lesson 6 - Assoication Rules and Outlier Detection
    • Apriori Algorithm
    • Isolcation Forest
    • Minimum Covariance Determinant
    • Local Outlier Factor
    • One-Class SVM

 

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

  Lesson 4: Staff / Student

  Lesson 5: Staff / Student

  Lesson 6: Staff / Student

 Course Material: Staff / Student

 

   

Machine Learning: Using user-friendly GUI tool without coding Webinar

Lesson 1
Introduction to Machine Learning
Data Mining and Workflow
ML Technique: Linear Regression

Lesson 2
ML Technique: Classification and Clustering

Lesson 3
ML Technique: Logistic Regression and Apriori Algorithm

Lesson 4
Use of ML Technique: Image Classification
ML Technique: Naïve Bayes Classifier

Lesson 5
Use of ML Technique: Text Mining

Lesson 6
Use of ML Technique: Time Series Forecasting
Real-world Example Using ML

 

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

  Lesson 4: Staff / Student

  Lesson 5: Staff / Student

  Lesson 6: Staff / Student

 Course Material: Staff / Student

 

Deep Learning with Python Workshop

Lesson 1

Deep Learning with Tensorflow
Tensor and Operations
Linear Regression
Deep Neural Network

Lesson 2

Convolutional Neural Network (CNN)
CNN on Image Classification
Augmentation
Transfer Learning

Lesson 3

Natural Language Processing
Tokenization and Word Embedding
Transformer and BERT

Lesson 4

Time Series Forecasting
RNN and LSTM
Temporal Fusion Transformer

Lesson 5

Neural Network Hyperparameter Tuning
Keras Tuner
HParams

 

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

  Lesson 4: Staff / Student

  Lesson 5: Staff / Student

 Course Material: Staff / Student