What you'll learn:
- Master Machine Learning
- Performing Ideal Preprocessing
- Understand Feature Engineering
- Understand Feature Selection
- Know the Best Way to Evaluate Models
- Analyse Models and Overcome its Challenges
- Hyperparameters Tuning
- Make Accurate Predictions
- Work with Real-World Data
If you're a machine learning specialist looking to transition into real-world AI applications, this comprehensive course will be your ultimate guide. By teaching you how to scale up your machine learning model to achieve the best performance, you'll learn everything you need to advance your model to the next stage.
This course is designed for both beginners with some programming experience and experienced developers who want to make the leap to Data Science. Throughout this course, you'll gain valuable knowledge and practical skills that will empower you to excel in your AI career.
Key features of the course include:
A strong foundation in machine learning concepts and algorithms, providing you with the necessary theoretical background to build and optimize your models.
Practical, hands-on experience with popular machine learning frameworks, such as TensorFlow, PyTorch, and Scikit-learn, enabling you to implement and fine-tune your models effectively.
Insights into deploying your machine learning models in real-world applications, from web services to mobile applications, ensuring that your models are ready to be utilized and make a meaningful impact.
Strategies for dealing with common challenges in the field, such as handling imbalanced datasets, addressing overfitting, and optimizing hyperparameters, equipping you with the tools needed to tackle any obstacles that may arise.
Comprehensive support from expert instructors and a thriving online community, providing you with the resources and connections necessary for your continued growth and success in the field.
By the end of this course, you'll have a thorough understanding of machine learning principles, practical experience with state-of-the-art tools and techniques, and the confidence to apply your newfound knowledge to real-world AI applications. Whether you're a beginner looking to launch a rewarding career in Data Science or an experienced developer eager to expand your skill set, this course will provide you with the resources and guidance you need to excel in the rapidly evolving world of AI.
You'll learn the machine learning, AI, and data mining techniques real employers are looking for, including:
Handling Missing Values
Label Encoder
One-Hot Encoder
Normalization
Standardization
Binarization
Principal Analysis Component (PCA)
Manual Feature Engineering
Automatic Feature Engineering
Feature Selection
Model Evaluation
Confusion Matrix
Precision and Recall
F1-score and Fbeta-score
Area Under Curve (AUC)
Overfitting vs Underfitting
Cross-Validation
Analyzing Learning Curves
Hyperparameters Tuning
