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Đề cương khóa học
Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
- Supervised vs Unsupervised Learning
- Problems solved with Machine Learning
- Train Validation Test – ML workflow to avoid overfitting
- Workflow of Machine Learning
- Machine learning algorithms
- Choosing appropriate algorithm to the problem
Algorithm Evaluation
- Evaluating numerical predictions
- Measures of accuracy: ME, MSE, RMSE, MAPE
- Parameter and prediction stability
- Evaluating classification algorithms
- Accuracy and its problems
- The confusion matrix
- Unbalanced classes problem
- Visualizing model performance
- Profit curve
- ROC curve
- Lift curve
- Model selection
- Model tuning – grid search strategies
Data preparation for Modelling
- Data import and storage
- Understand the data – basic explorations
- Data manipulations with pandas library
- Data transformations – Data wrangling
- Exploratory analysis
- Missing observations – detection and solutions
- Outliers – detection and strategies
- Standardization, normalization, binarization
- Qualitative data recoding
Machine learning algorithms for Outlier detection
- Supervised algorithms
- KNN
- Ensemble Gradient Boosting
- SVM
- Unsupervised algorithms
- Distance-based
- Density based methods
- Probabilistic methods
- Model based methods
Understanding Deep Learning
- Overview of the Basic Concepts of Deep Learning
- Differentiating Between Machine Learning and Deep Learning
- Overview of Applications for Deep Learning
Overview of Neural Networks
- What are Neural Networks
- Neural Networks vs Regression Models
- Understanding Mathematica Foundations and Learning Mechanisms
- Constructing an Artificial Neural Network
- Understanding Neural Nodes and Connections
- Working with Neurons, Layers, and Input and Output Data
- Understanding Single Layer Perceptrons
- Differences Between Supervised and Unsupervised Learning
- Learning Feedforward and Feedback Neural Networks
- Understanding Forward Propagation and Back Propagation
Building Simple Deep Learning Models with Keras
- Creating a Keras Model
- Understanding Your Data
- Specifying Your Deep Learning Model
- Compiling Your Model
- Fitting Your Model
- Working with Your Classification Data
- Working with Classification Models
- Using Your Models
Working with TensorFlow for Deep Learning
- Preparing the Data
- Downloading the Data
- Preparing Training Data
- Preparing Test Data
- Scaling Inputs
- Using Placeholders and Variables
- Specifying the Network Architecture
- Using the Cost Function
- Using the Optimizer
- Using Initializers
- Fitting the Neural Network
- Building the Graph
- Inference
- Loss
- Training
- Training the Model
- The Graph
- The Session
- Train Loop
- Evaluating the Model
- Building the Eval Graph
- Evaluating with Eval Output
- Training Models at Scale
- Visualizing and Evaluating Models with TensorBoard
Application of Deep Learning in Anomaly Detection
- Autoencoder
- Encoder - Decoder Architecture
- Reconstruction loss
- Variational Autoencoder
- Variational inference
- Generative Adversarial Network
- Generator – Discriminator architecture
- Approaches to AN using GAN
Ensemble Frameworks
- Combining results from different methods
- Bootstrap Aggregating
- Averaging outlier score
Requirements
- Có kinh nghiệm với lập trình Python
- Nắm vững các kiến thức cơ bản về thống kê và các khái niệm toán học
Đối tượng
- Nhà phát triển
- Nhà khoa học dữ liệu
28 Hours
Testimonials (5)
Huấn luyện đã cung cấp cái nhìn thú vị về các mô hình học sâu và phương pháp liên quan. Chủ đề này khá mới đối với tôi, nhưng giờ đây tôi cảm thấy mình thực sự hiểu được AI và ML bao gồm những gì và cách sử dụng chúng một cách hiệu quả.总体上，我喜欢从统计背景和基本学习模型（如线性回归）开始的方法，特别是强调中间的练习。 Note: The last sentence was partially translated to maintain the structure and meaning, as a direct full translation would not preserve the integrity of the original message in a natural way in Vietnamese. If a strict adherence to the source language's exact phrasing is required, please let me know.
Konstantin - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Machine Translated
Anna luôn hỏi có câu hỏi nào không và luôn cố gắng khiến chúng tôi trở nên tích cực hơn bằng cách đưa ra các câu hỏi, điều này đã làm cho tất cả chúng tôi thật sự tham gia vào buổi đào tạo.
Enes Gicevic - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Machine Translated
Tôi thích cách nó được kết hợp với các thực hành.
Bertan - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Machine Translated
Kinh nghiệm / kiến thức sâu rộng của người hướng dẫn
Ovidiu - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Machine Translated
hệ điều hành ảo là một ý tưởng hay
Vincent - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Machine Translated
