Course Curriculum

Introduction to Predictive Analysis 00:09:00
Random Forest and Extremely Random Forest 00:11:00
Dealing with Class Imbalance 00:07:00
Grid Search 00:09:00
Adaboost Regressor 00:08:00
Predicting Traffic Using Extremely Random Forest Regressor 00:02:00
Traffic Prediction 00:07:00
Detecting patterns with Unsupervised Learning 00:05:00
Clustering 00:07:00
Clustering Meanshift 00:04:00
Clustering Meanshift Continues 00:06:00
Affinity Propagation Model 00:05:00
Affinity Propagation Model Continues 00:05:00
Clustering Quality 00:05:00
Program of Clustering Quality 00:07:00
Gaussian Mixture Model 00:04:00
Program of Gaussian Mixture Model 00:08:00
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
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
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
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

Overview

Learning AI with Python gives you a strong, practical foundation in artificial intelligence, one of the fastest-growing fields in tech today. This course takes you through real AI techniques using Python, starting with predictive analysis and machine learning models like random forests. You’ll explore unsupervised learning, clustering, and classification methods, along with logic programming and heuristic search techniques used to solve complex problems. The course also covers natural language processing, showing you how machines understand and work with human language. Whether you’re new to AI or want to build on existing Python skills, this AI with Python course gives you hands-on knowledge you can apply straight away.

Learning Outcomes

  • Build predictive models using techniques like random forests and regression
  • Handle class imbalance and use grid search to improve model performance
  • Apply clustering methods to detect patterns within unlabelled data
  • Use classification techniques, including logistic regression and support vector machines
  • Apply logic programming to solve problems and analyse relationships
  • Use heuristic search techniques to tackle constraint-based problems
  • Process and analyse human language using natural language processing techniques

Will I receive a certificate of completion?

Upon successful completion, you will qualify for the UK and internationally-recognised CPD accredited certification. You can choose to make your achievement formal by obtaining your PDF Certificate at the cost of £9 and Hard Copy Certificate for £15.

Why study this course

It doesn’t matter if you are an aspiring professional or absolute beginner; this course will enhance your expertise and boost your CV with critical skills and an accredited certification attesting to your knowledge.

The Learn AI with Python is fully available to anyone, and no previous qualifications are needed to enrol. All Cambridge Open Academy needs to know is that you are eager to learn and are over 16.

Certification:​

Once you have successfully completed this course, you can order a certificate as proof of your achievement.

A PDF certificate is available for £5.99. You can also request a hardcopy certificate for £9.99.

Delivery of the hardcopy certificate is free within the United Kingdom. For international learners, an additional delivery charge will apply based on the destination country.

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