Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Python for Machine Learning: The Complete Beginner’s course has been specially designed to help learners gain a good command of Python for Machine Learning, providing them with a solid foundation of knowledge to become a qualified professional.
Through this Python for Machine Learning: The Complete Beginner’ course, you will gain both practical and theoretical understanding of Python for Machine Learning: that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.
Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.
Learning outcome
Familiar yourself with the recent development and updates of the relevant industry
Know how to use your theoretical knowledge to adapt in any working environment
Get help from our expert tutors anytime you need
Access to course contents that are designed and prepared by industry professionals
Study at your convenient time and from wherever you want
Implementation in Python: Importing libraries & datasets
00:03:00
Implementation in Python: Splitting data into Train and Test Sets
00:01:00
Implementation in Python: Pre-processing
00:02:00
Implementation in Python: Training the model
00:01:00
Implementation in Python: Results prediction & Confusion matrix
00:02:00
Logistic Regression vs Linear Regression
00:02:00
Section 08: Clustering
Introduction to clustering
00:01:00
Use cases
00:01:00
K-Means Clustering Algorithm
00:01:00
Elbow method
00:02:00
Steps of the Elbow method
00:01:00
Implementation in python
00:04:00
Hierarchical clustering
00:01:00
Density-based clustering
00:02:00
Implementation in python
00:04:00
Implementation of k-means clustering in Python
00:01:00
Implementation in Python: Importing the dataset
00:02:00
Visualizing the dataset
00:02:00
Defining the classifier
00:02:00
3D Visualization of the clusters
00:03:00
Number of predicted clusters
00:02:00
Section 09: Recommender System
Introduction S9
00:01:00
Collaborative Filtering in Recommender Systems
00:01:00
Content-based Recommender System
00:01:00
Implementation in Python: Importing libraries & datasets
00:03:00
Merging datasets into one dataframe
00:01:00
Sorting by title and rating
00:04:00
Histogram showing number of ratings
00:01:00
Frequency distribution
00:01:00
Jointplot of the ratings and number of ratings
00:01:00
Data pre-processing
00:02:00
Sorting the most-rated movies
00:01:00
Grabbing the ratings for two movies
00:01:00
Correlation between the most-rated movies
00:02:00
Sorting the data by correlation
00:01:00
Filtering out movies
00:01:00
Sorting values
00:01:00
Repeating the process for another movie
00:02:00
Section 10: Conclusion
Conclusion
00:01:00
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