Machine Learning & Deep Learning - Apply into practice
1. Introduction
Machine learning is so popular and widen use today that you probably use it dozens of times a day without knowing it.
For example, searching on the web, speech recognition, translation, self-driving cars, etc. This course will help you to review math knowledges relate to Machine Learning, algorithms and the most effective of machine learning techniques, and gain practice implementing them in the real case.
More importantly, you will learn not only the theoretical underpinnings of learning, but also gain practical and best practices in projects about Machine Learning/Deep Learning.
2. Training Targets
Share knowledge and share experience from R&D and projects of ML/DL
3. Knowledge and Skills gained after the course
Gain knowledge of algorithms and apply to the real business case to support for business, help them make the right solutions and quickly to reduce cost in the competition environment.
4. Career Opportunities
Machine learning and AI is a big trend today and in the future so you will have more opportunities to work with the companies, projects about ML.
5. Training Period
- Training timline: 16 sessions
- The Schedule is Saturday - Sunday, from 09:00 to 11:00
- Fee: 3.500.000 VNĐ
6. Target
You want to work in Big Data, AI, IoT Technology, R&D and projects of ML/DL
7. Requirements
- Have knowledge about Python
- Linear algebra
- Probability & Mathematic statistics is an advantage.
8. Content
| Topic |
Agenda |
| Introduction AI & Machine Learning |
- The concept of AI, Machine Learning
- Key terminology
- Simple Workflow
- Steps in developing a Machine Learning application |
| Software Installation |
- Introduction
- Jupytor Notebook, Python, Anaconda, Virtual Environment
- Introduce to Data visualization
- Practice |
| Feature Engineering |
- Introduction
- Feature Extractor
- Feature Selection
- Dimensionality Reduction: PCA, t-SNE ...
- Feature Scaling and Normalization
- Practice |
| Softmax Regression |
- Introduction
- One-vs-rest model
- Softmax function
- Softmax with python
- Exampe with Simulated data
- Practice |
| Overfit/Underfit |
- Introduction
- Validation
- Regularization
- Other methods
- Practice |
| Gradient Descent Section |
- Introduction
- Gradient Descent for function 1 variable
- Example
- Learning rate
- Gradient Descent with multiple variants
- Level set
- Speed of conver by algorithms
- Momentum
- Nesterov accelerated gradient
- Batch Gradient Descent
- Stochastic Gradient Descent (SGD)
- Example with Linear Regression |
| Supervised Learning |
|
| K-Nearest Neighbors |
- Explain Algorithm
- General approach to kNN
- Handwriting Recognition System
- Demo |
| Linear Regression |
- Introduction & Defines
- Predicted error
- Lost function
- Find optimal point
- Problems can be solved by Linear Regression
- Practice |
| Decision Trees |
- Introducing decision trees
- Measuring consistency in a dataset
- Using recursion to construct a decision tree
- Plotting trees in Matplotlib
- Practice |
| Logistic Regression |
- Introducing
- Example
- Model Logistic Regression
- Lost function
- Find optimal point
- Example with python
- Practice |
| Binary Classifiers Problems |
- Introduction
- Binary Classifiers for Multi-class Classification problems
- One-vs-one
- Hierarchical
- Binary coding
- one-vs-rest and one-hot coding
- Neural Networks Form
- Gender problem based on face image
- Practice |
| Naive Bayes Classifier |
- Introduction
- Probability review for Machine Learning
- Multinomial Naive Bayes
- Examples
- Practice |
| Recommendation |
- Introduction Asociation Rules/Apriori/FPGrowth
- Content-Based Filtering
- User-user collaborative Filtering
- Item-item Collaborative Filtering
- Practice |
| Peceptron Learning |
- Introduction
- Problem
- Perceptron
- Example
- Practice |
| Multilayer Perceptron |
- Introduction
- PLA for basic logic functions
- Layers
- Units
- Weights and Biases
- Activation functions
- Backpropagation
- Example
- Practice |
| Unsupervised Learning |
|
| Kmeans Clustering |
- Mathematics
- Lost Function
- Algorithm to optimize Lost Function
- Demo with MNIST data - hand-writer
- Practice |
| Dimention Reduction |
- Introducion
- Algorithms
- Practice |
| Reinforcement Learning |
|
| Reinforcement Learning |
- Introduction
- Concept and workflow
- Base algorithms
- Practice |
| Deep Learning |
|
| Introduce Deep Learning |
- Introduction
- Concept and workflow
- Base algorithms
- CNN algorithm explain & Approach
- Practice |
| RNN & LSTM |
- Explain Algorithm
- General approach to RNN
- Practice |
| Exercise |
- Final Exercise |
9. Introduce Trainer
Nguyên Trần AI
Have experience in applying AI, Machine Learning, and Deep Learning to solve real problems in various domains: NLP, image, and speech signal ... Take responsibility for many roles:
- Consult project solution
- Manage technical team
- Develop function and deploy to production
- Take part in R&D and training activities
- Communicate with customers to advise solutions
Hình thức thu học phí:
- Chuyển khoản: Vietcombank: Số tk: 0071003095741, Lê Thị Bích Hòa, chi nhánh Kỳ Đồng, Quận 3, Tp.HCM
- Đóng trực tiếp tại: 122, Đường B2, Phường An Lợi Đông, Quận 2, Thành Phố Hồ Chính Minh, Gặp chị Việt. Phone/Zalo: 0379887449 (9:00AM – 6:00PM)
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