Learn AI with Python
Learn AI with Python
- Interactive video lectures by industry experts
- Developed by qualified first aid professionals
- Instant e-certificate and hard copy dispatch by next working day
- Fully online, interactive course with audio voiceover
- 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
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.
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
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. 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
Introduction of Project 
00:03:00
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 00:05:00Affinity 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 with 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 00:06:00Shift Reduce Parsing 00:08:00
Student Ratings & Reviews
