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Learn how to make a genuine difference in your life by taking our popular Learn AI with Python Course. Our commitment to online learning and our technical experience has been put to excellent use within the content of these educational modules. By enrolling today, you can take your knowledge of Learn AI with Python to a whole new level and quickly reap the rewards of your study in the field you have chosen.
We are confident that you will find the skills and information that you will need to succeed in this area and excel in the eyes of others. Do not rely on substandard training or half-hearted education. Commit to the best, and we will help you reach your full potential whenever and wherever you need us.
Please note that Learn AI with Python provides valuable and significant theoretical training for all. However, it does not offer official qualifications for professional practice. Always check details with the appropriate authorities or management.
By completing the training in Learn AI with Python, you will be able to significantly demonstrate your acquired abilities and knowledge of Learn AI with Python. This can give you an advantage in career progression, job applications, and personal mastery in this area.
This course is designed to provide an introduction to Learn AI with Python and offers an excellent way to gain the vital skills and confidence to start a successful career. It also provides access to proven educational knowledge about the subject and will support those wanting to attain personal goals in this area. Full-time and part-time learners are equally supported, and the study periods are entirely customisable to your needs.
Once you have completed all the modules in the Learn AI with Python course, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.
Show off Your New Skills with a Certification of Completion
The learners have to successfully complete the assessment of this Learn AI with Python course to achieve the CPD accredited certificate. Digital certificates can be ordered for only £10. Learners can purchase printed hard copies inside the UK for £29, and international students can purchase printed hard copies for £39.
Section 01: Introduction | |||
Introduction to Predictive Analysis | 00:09:00 | ||
Random Forest and Extremely Random Forest | 00:11:00 | ||
Section 02: Class Imbalance and Grid Search | |||
Dealing with Class Imbalance | 00:07:00 | ||
Grid Search | 00:09:00 | ||
Section 03: Adaboost Regressor | |||
Adaboost Regressor | 00:08:00 | ||
Predicting Traffic Using Extremely Random Forest Regressor | 00:02:00 | ||
Traffic Prediction | 00:07:00 | ||
Section 04: Detecting patterns with Unsupervised Learning | |||
Detecting patterns with Unsupervised Learning | 00:05:00 | ||
Clustering | 00:07:00 | ||
Clustering Meanshift | 00:04:00 | ||
Clustering Meanshift Continues | 00:06:00 | ||
Section 05: Affinity Propagation Model | |||
Affinity Propagation Model | 00:05:00 | ||
Affinity Propagation Model Continues | 00:05:00 | ||
Section 06: Clustering Quality | |||
Clustering Quality | 00:05:00 | ||
Program of Clustering Quality | 00:07:00 | ||
Section 07: Gaussian Mixture Model | |||
Gaussian Mixture Model | 00:04:00 | ||
Program of Gaussian Mixture Model | 00:08:00 | ||
Section 08: Classifiers | |||
Classification in Artificial Intelligence | 00:03:00 | ||
Processing Data | 00:09:00 | ||
Logistic Regression Classifier | 00:03:00 | ||
Logistic Regression Classifier Example Using Python | 00:07:00 | ||
Naive Bayes Classifier and its Examples | 00:11:00 | ||
Confusion Matrix | 00:04:00 | ||
Example os Confusion Matrix | 00:06:00 | ||
Support Vector Machines Classifier(SVM) | 00:05:00 | ||
SVM Classifier Examples | 00:08:00 | ||
Section 09: Logic Programming | |||
Concept of Logic Programming | 00:11:00 | ||
Matching the Mathematical Expression | 00:07:00 | ||
Parsing Family Tree and its Example | 00:09:00 | ||
Analyzing Geography Logic Programming | 00:05:00 | ||
Puzzle Solver and its Example | 00:06:00 | ||
Section 10: Heuristic Search | |||
What is Heuristic Search | 00:06:00 | ||
Local Search Technique | 00:09:00 | ||
Constraint Satisfaction Problem | 00:09:00 | ||
Region Coloring Problem | 00:05:00 | ||
Building Maze | 00:07:00 | ||
Puzzle Solver | 00:09:00 | ||
Section 11: Natural Language Processing | |||
Natural Language Processing | 00:06:00 | ||
Examine Text Using NLTK | 00:04:00 | ||
Raw Text Accessing (Tokenization) | 00:11:00 | ||
NLP Pipeline and Its Example | 00:07:00 | ||
Regular Expression with NLTK | 00:05:00 | ||
Stemming | 00:07:00 | ||
Lemmatization | 00:06:00 | ||
Segmentation | 00:06:00 | ||
Segmentation Example | 00:03:00 | ||
Segmentation Example Continues | 00:04:00 | ||
Information Extraction | 00:09:00 | ||
Tag Patterns | 00:03:00 | ||
Chunking | 00:09:00 | ||
Representation of Chunks | 00:05:00 | ||
Chinking | 00:07:00 | ||
Chunking wirh Regular Expression | 00:08:00 | ||
Named Entity Recognition | 00:06:00 | ||
Trees | 00:07:00 | ||
Context Free Grammar | 00:03:00 | ||
Recursive Descent Parsing | 00:06:00 | ||
Recursive Descent Parsing Continues | 00:06:00 | ||
Shift Reduce Parsing | 00:08:00 |
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