Name: Learn AI with Python
Price: 25 GBP

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Curriculum

Overview:

Welcome to “Learn AI with Python”! This course is your gateway to mastering Artificial Intelligence (AI) concepts and techniques using the Python programming language. With AI revolutionizing industries worldwide, this course empowers you to harness the power of Python to build intelligent systems and algorithms. From machine learning to deep learning and natural language processing, you’ll explore a wide range of AI applications, equipping you with the skills to tackle real-world challenges and drive innovation.

Interactive video lectures by industry experts
Instant e-certificate and hard copy dispatch by next working day
Fully online, interactive course with Professional voice-over
Developed by qualified first aid professionals
Self-paced learning and laptop, tablet, smartphone friendly
24/7 Learning Assistance
Discounts on bulk purchases

Main Course Features:

Comprehensive coverage of AI fundamentals, algorithms, and libraries in Python
Hands-on projects and coding exercises to reinforce learning
Exploration of machine learning techniques, including supervised and unsupervised learning
Implementation of neural networks and deep learning models with TensorFlow and Keras
Introduction to natural language processing (NLP) for text analysis and sentiment analysis
Guidance on deploying AI models and integrating them into applications
Real-world case studies and examples to illustrate AI concepts in practice
Access to a supportive online community for collaboration and assistance

Who Should Take This Course:

Aspiring data scientists and AI enthusiasts looking to kickstart their career in AI
Python developers interested in expanding their skill set to include AI and machine learning
Students and professionals seeking to leverage AI for solving real-world problems

Learning Outcomes:

Master AI concepts and techniques using Python programming
Develop machine learning models for classification, regression, and clustering tasks
Build and train neural networks and deep learning models for various applications
Perform text analysis and sentiment analysis using natural language processing (NLP)
Deploy AI models and integrate them into web applications or other systems
Enhance problem-solving skills by applying AI algorithms to real-world datasets
Debug and optimize AI models for improved performance and accuracy
Stay updated with the latest advancements and trends in AI and machine learning.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.

Course Curriculum

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