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Master in Artificial Intelligence & Machine Learning with Python

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Boost your career with full Master program.

Machines, robots, artificial intelligence, … are today a reality that is revolutionizing our lives. Reports estimate that many jobs will be replaced by these autonomous and intelligent systems. Hurry up and embark on the adventure: Master artificial intelligence from A to Z and machine learning with Python!

Are the financial investment and the entry requirements justified? All in all, is an Master degree worth it?

If you’re preparing to enter the competitive technological world of today, we believe it is.

So, what can you do with a Master? A Master artificial intelligence will offer you a wealth of advantages, especially when it’s from a well-regarded school. Getting a high Master salary after graduation, landing a management position, developing a strong professional network, or even becoming your own boss are just a few of the advantages of studying a Master degree abroad.

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Curriculum

Introduction to Machine Learning
What is Machine Learning
Applications of Machine Learning
Machine learning Methods
What is Supervised learning
What is Unsupervised learning
Supervised learning vs Unsupervised learning
Simple Linear Regression
Introduction to regression
How Does Linear Regression Work
Line representation
Implementation in python Importing libraries _ datasets
Implementation in python Distribution of the data
Implementation in python Creating a linear regression object
Multiple Linear Regression
Understanding Multiple linear regression
Implementation in python Exploring the dataset
Implementation in python Encoding Categorical Data
Implementation in python Splitting data into Train and Test Sets
Implementation in python Training the model on the Training set
Implementation in python Predicting the Test Set results
Evaluating the performance of the regression model
Root Mean Squared Error in Python
Classification Algorithms K-Nearest Neighbors
Introduction to classification
K-Nearest Neighbors algorithm
Example of KNN
K-Nearest Neighbours (KNN) using python
Implementation in python Importing required libraries
Implementation in python Importing the dataset
Implementation in python Splitting data into Train and Test Sets
Implementation in python Feature Scaling
Implementation in python Importing the KNN classifier
Implementation in python Results prediction _ Confusion matrix
Classification Algorithms Decision Tree
Introduction to decision trees
What is Entropy?
Exploring the dataset
Decision tree structure
Implementation in python Importing libraries _ datasets
Implementation in python Encoding Categorical Data
Implementation in python Splitting data into Train and Test Sets
Implementation in python Results prediction _ Accuracy
Classification Algorithms Logistic regression
Introduction
Implementation steps
Implementation in python Importing libraries _ datasets
Implementation in python Splitting data into Train and Test Sets
Implementation in python Pre-processing
Implementation in python Training the model
Implementation in python Results prediction _ Confusion matrix
Logistic Regression vs Linear Regression
Clustering
Introduction to clustering
Use cases
K-Means Clustering Algorithm
Elbow method
Steps of the Elbow method
Implementation in python
Hierarchical clustering
Density-based clustering
Implementation of k-means clustering in python
Importing the dataset
Visualizing the dataset
Defining the classifier
3D Visualization of the clusters
3D Visualization of the predicted values
Number of predicted clusters
Recommender System
Introduction
Collaborative Filtering in Recommender Systems
Content-based Recommender System
Implementation in python Importing libraries _ datasets
Merging datasets into one dataframe
Sorting by title and rating
Histogram showing number of ratings
Frequency distribution
Jointplot of the ratings and number of ratings
Data pre-processing
Sorting the most-rated movies
Grabbing the ratings for two movies
Correlation between the most-rated movies
Sorting the data by correlation
Filtering out movies
Sorting values
Repeating the process for another movie
Conclusion
Conclusion

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Details

Duration of the course:

The duration depends on the time that the student can allocate. On the basis of 4 hours of work per week, the course can be completed, including the report, in 3 to 4 months.

Course fees:

There are no application fees.
The course fee is only 49.99 Euros!

What are the dates for the next session?

Our campus is accessible throughout the year. You can start on any date that suits you.
You will have an entire calendar year (12 months) to complete the course.

Hi, I’m Emma F., from United States

Thanks to this Master, I landed a job in a large technology firm. I manipulate the AI every day and make a good living!

ENROLL now!!

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