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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 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.

Curriculum

  • Introduction to Predictive Analysis
  • Random Forest and Extremely Random Forest
  • Dealing with Class Imbalance
  • Grid Search
  • Adaboost Regressor
  • Predicting Traffic Using Extremely Random Forest Regressor
  • Traffic Prediction
  • Detecting patterns with Unsupervised Learning
  • Clustering
  • Clustering Meanshift
  • Clustering Meanshift Continues
  • Affinity Propagation Model
  • Affinity Propagation Model Continues
  • Clustering Quality
  • Program of Clustering Quality
  • Gaussian Mixture Model
  • Program of Gaussian Mixture Model
  • 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
  • Concept of Logic Programming
  • Matching the Mathematical Expression
  • Parsing Family Tree and its Example
  • Analyzing Geography Logic Programming
  • Puzzle Solver and its Example
  • What is Heuristic Search
  • Local Search Technique
  • Constraint Satisfaction Problem
  • Region Coloring Problem
  • Building Maze
  • Puzzle Solver
  • 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

Frequently Asked Questions

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Course Features

  • Enrolled : 4
  • Duration : 6 hours, 24 minutes
  • Lectures : 59
  • Categories: IT & Software Personal Development
Price: ₦20000
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