Learn AI with Python

~~₦120000~~ ₦50000

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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
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. 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.

We guarantee that all our online courses will meet or exceed your expectations. If you are not fully satisfied with a course - for any reason at all - simply request a full refund. We guarantee no hassles. That's our promise to you.

Go ahead and order with confidence!

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Course Curriculum

Section 01: Introduction
	Introduction to Predictive Analysis
	Random Forest and Extremely Random Forest
Section 02: Class Imbalance and Grid Search
	Dealing with Class Imbalance
	Grid Search
Section 03: Adaboost Regressor
	Adaboost Regressor
	Predicting Traffic Using Extremely Random Forest Regressor
	Traffic Prediction
Section 04: Detecting patterns with Unsupervised Learning
	Detecting patterns with Unsupervised Learning
	Clustering
	Clustering Meanshift
	Clustering Meanshift Continues
Section 05: Affinity Propagation Model
	Affinity Propagation Model
	Affinity Propagation Model Continues
Section 06: Clustering Quality
	Clustering Quality
	Program of Clustering Quality
Section 07: Gaussian Mixture Model
	Gaussian Mixture Model
	Program of Gaussian Mixture Model
Section 08: Classifiers
	Classification in Artificial Intelligence
	Processing Data
	Logistic Regression Classifier
	Logistic Regression Classifier Example Using Python
	Naive Bayes Classifier and its Examples
	Confusion Matrix
	Example os Confusion Matrix
	Support Vector Machines Classifier(SVM)
	SVM Classifier Examples
Section 09: Logic Programming
	Concept of Logic Programming
	Matching the Mathematical Expression
	Parsing Family Tree and its Example
	Analyzing Geography Logic Programming
	Puzzle Solver and its Example
Section 10: Heuristic Search
	What is Heuristic Search
	Local Search Technique
	Constraint Satisfaction Problem
	Region Coloring Problem
	Building Maze
	Puzzle Solver
Section 11: Natural Language Processing
	Natural Language Processing
	Examine Text Using NLTK
	Raw Text Accessing (Tokenization)
	NLP Pipeline and Its Example
	Regular Expression with NLTK
	Stemming
	Lemmatization
	Segmentation
	Segmentation Example
	Segmentation Example Continues
	Information Extraction
	Tag Patterns
	Chunking
	Representation of Chunks
	Chinking
	Chunking wirh Regular Expression
	Named Entity Recognition
	Trees
	Context Free Grammar
	Recursive Descent Parsing
	Recursive Descent Parsing Continues
	Shift Reduce Parsing

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