## Basic Python Webinar

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

Lesson 2
-    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: Student

Lesson 2: Student

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Lesson 4: Student

Lesson 5: Student

Course Material: 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
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
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
(2) Near-Miss
- Oversampling
(1) SMOTE
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: Student

Lesson 2: Student

Lesson 3: Student

Lesson 4: Student

Lesson 5: Student

Lesson 6: Student

Course Material: 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: Student

Lesson 2: Student

Lesson 3: Student

Course Material: 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: Student

Lesson 2: Student

Course Material: 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
• 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:

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Lesson 2: Student

Lesson 3: Student

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Lesson 5: Student

Lesson 6: Student

Course Material: 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: Student

Lesson 2: Student

Lesson 3: Student

Lesson 4: Student

Lesson 5: Student

Lesson 6: Student

Course Material: 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: Student

Lesson 2: Student

Lesson 3: Student

Lesson 4: Student

Lesson 5: Student

Course Material: Student