Implementing Machine Learning
This professional learning program bridges statistical theory and modern deep learning practice. You’ll work through three specialized modules—each building genuine capability in regression analysis, neural network architecture, and computer vision—to deploy real machine learning systems.
What You Will Learn
The first module establishes statistical fundamentals through regression analysis. You’ll learn to model relationships in data, validate predictions, and interpret results with mathematical rigor. This foundation is non-negotiable for understanding why neural networks work.
The second module introduces neural network architecture and deep learning frameworks. You’ll build networks from first principles, understand backpropagation, and train models on real datasets. This is where statistical thinking meets computational scale.
The third module applies deep learning to image classification using convolutional neural networks. You’ll learn feature extraction, pooling, regularization, and deployment strategies for production computer vision systems.
Analyzing Data with Statistical Regression
5 lessons covering linear regression, feature engineering, model validation, and prediction interpretation for real-world datasets.
Introducing Neural Networks and Deep Learning
8 lessons building neural network fundamentals, activation functions, loss optimization, and practical training strategies with modern frameworks.
Classifying Images with Convolutional Neural Networks (CNNs)
4 lessons on CNN architecture, convolution mechanics, transfer learning, and deployment of production-grade image classification models.
Complete this program and you’ll have the technical depth to architect, train, and deploy machine learning systems that work.
Curriculum
- 3 Sections
- 17 Lessons
- Lifetime
- Chapter 6: Analyzing Data with Statistical Regression5
- 1.1Analyzing Systems Using Regression10 mins
- 1.2Linear Regression: Fitting Lines to Data10 mins
- 1.3Polynomial Regression: Fitting Polynomials to Data10 mins
- 1.4Binary Logistic Regression: Classifying Data into Two Categories10 mins
- 1.5Multinomial Logistic Regression: Classifying Data into Multiple Categories10 mins
- Chapter 7: Introducing Neural Networks and Deep Learning8
- Chapter 8: Classifying Images with Convolutional Neural Networks (CNNs)4
Instructor
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