Rating 4.88 out of 5 (4 ratings in Udemy)
What you'll learn- Describe the input and output of a classification model
- Prepare data with feature engineering techniques
- Tackle both binary and multiclass classification problems
- Implement Support Vector Machines, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Neural Networks, logistic regression models on Python
- Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score. …
Rating 4.88 out of 5 (4 ratings in Udemy)
What you'll learn- Describe the input and output of a classification model
- Prepare data with feature engineering techniques
- Tackle both binary and multiclass classification problems
- Implement Support Vector Machines, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Neural Networks, logistic regression models on Python
- Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score.
DescriptionArtificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. Supervised machine learning is the underlying method behind a large part of this. Supervised learning involves using some algorithm to analyze and learn from past observations, enabling you to predict future events. This course introduces you to one of the prominent modelling families of supervised Machine Learning called Classification. This course will teach you to implement supervised classification machine learning models in Python using the Scikit learn (sklearn) library. You will become familiar with the most successful and widely used classification techniques, such as:
Support Vector Machines.
Naive Bayes
Decision Tree
Random Forest
K-Nearest Neighbors
Neural Networks
Logistic Regression
You will learn to train predictive models to classify categorical outcomes and use performance metrics to evaluate different models. The complete course is built on several examples where you will learn to code with real datasets. By the end of this course, you will be able to build machine learning models to make predictions using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!
Happy Learning.
Career Growth:
Employment website Indeed has listed machine learning engineers as #1 among The Best Jobs in the U.S., citing a 344% growth rate and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.
