Train Machine Learning Models
- Offered byCoursera
Train Machine Learning Models at Coursera Overview
Duration | 28 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Train Machine Learning Models at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 5 in the CertNexus Certified Data Science Practitioner
- Intermediate Level Understand data science concepts, experience with programming languages (Python), libraries (NumPy,pandas) and database querying languages (SQL).
- Approx. 29 hours to complete
- English Subtitles: English
Train Machine Learning Models at Coursera Course details
- This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems.
- To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.
Train Machine Learning Models at Coursera Curriculum
Prepare to Train a Machine Learning Model
Course Intro: Train Machine Learning Models
Machine Learning
Machine Learning Algorithms
Algorithm Selection
Iterative Tuning
Bias and Variance
Model Generalization
The Bias'Variance Tradeoff
Holdout Method
Parameters
Hypothesis and DOE
Hypothesis Testing
A/B Tests
p-value
Confidence Interval
Overview
Cross-Validation
Guidelines for Training Machine Learning Models
Additional Hypothesis Testing Methods
Guidelines for Testing a Hypothesis
Preparing to Train a Machine Learning Model
Develop Classification Models
Logistic Regression
Multinomial Logistic Regression
k-Nearest Neighbor (k-NN)
Support-Vector Machines (SVMs)
Naïve Bayes
Decision Tree
Customer Retention Example Tree
Pruning
Ensemble Learning and Random Forests
Gradient Boosting
Hyperparameter Optimization
Evaluation Metrics
Classification Model Performance
Confusion Matrix
Accuracy, Precision, Recall, and Specificity
Precision'Recall Tradeoff and F? Score
Receiver Operating Characteristic (ROC) Curve
Learning Curve
Overview
Guidelines for Training Logistic Regression Models
Guidelines for Training k-NN Models
Guidelines for Training SVM Classification Models
Guidelines for Training Naïve Bayes Models
CART Hyperparameters
Guidelines for Training Classification Decision Trees and Ensemble Models
Guidelines for Tuning Classification Models
Guidelines for Evaluating Classification Models
Developing Classification Models
Develop Regression Models
Linear Regression
Linear Regression in Machine Learning
Matrices in Linear Regression
Normal Equation
Regression Using Decision Trees and Ensemble Models
Forecasting
Autoregressive Integrated Moving Average (ARIMA)
Cost Function
Regularization
Gradient Descent
Grid/Randomized Search for Regression
Mean Squared Error (MSE) and Mean Absolute Error (MAE)
Coefficient of Determination
Overview
Guidelines for Training Linear Regression Models
Guidelines for Training Regression Trees and Ensemble Models
Guidelines for Training Forecasting Models
Regularization Techniques
Guidelines for Tuning Regression Models
Guidelines for Evaluating Regression Models
Developing Regression Models
Develop Clustering Models
k-Means Clustering
Hierarchical Clustering
Latent Class Analysis
Clustering Hyperparameters and Tuning
Evaluation Metrics for Clustering
Elbow Point
Cluster Sum of Squares
Silhouette Analysis
When to Stop Hierarchical Clustering
Overview
Guidelines for Training k-Means Clustering Models
Guidelines for Training Hierarchical Clustering Models
Guidelines for Tuning Clustering Models
Guidelines for Evaluating Clustering Models
Developing Clustering Models
Apply What You've Learned
Train Machine Learning Models at Coursera Admission Process
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