Machine Learning in Practice
Designed for professionals eager to grasp machine learning essentials, blends core algorithms, practical exercises, and real-world applications to provide participants with the knowledge and confidence to apply AI methods effectively in diverse business and technical contexts.
Language
English
Why Bakkah?
Money Guaranteed
Global Accreditation
Flexible Learning
About this Course
What to Expect From This Machine Learning in Practice Course?
By the end, participants will be able to:
- Understand and explain key machine learning algorithms and their applications.
- Apply supervised and unsupervised learning techniques to real-world datasets.
- Build and evaluate basic neural networks and deep learning models.
- Split datasets effectively for training, testing, and validation purposes.
- Assess model performance using accuracy, precision, and recall.
- Develop problem-solving skills to choose suitable ML approaches for different contexts.
Who Should Enroll in this Machine Learning in Practice?
- Data analysts and professionals looking to expand into machine learning.
- Software engineers and developers seeking to integrate ML into applications.
- Business professionals interested in leveraging ML for data-driven decisions.
- Researchers and students aiming to build foundational skills in ML.
- Anyone curious about applying machine learning in practical scenarios.
What are the acquired skills from this Machine Learning in Practice?
- Understanding of core machine learning algorithms and principles.
- Ability to implement supervised and unsupervised learning techniques.
- Practical knowledge of neural networks and deep learning basics.
- Skills in data preparation, splitting, training, and testing.
- Evaluating models using accuracy, precision, and recall.
- Applying ML methods to solve real-world problems.
Learn your way, at your pace.
Get the skills you need with a flexible learning experience designed to fit your lifestyle.
Machine Learning in Practice - Self Study
Best for busy learners who need flexibility.
Accepted payment methods:
Course Inclusions
- Machine learning concepts and workflow
- Role of algorithms in AI
- Types of learning (supervised, unsupervised, reinforcement)
- Real-world ML applications
- Predictive vs pattern-based models
- Algorithm selection challenges
- Industry use cases (finance, healthcare, smart cities)
- Supervised learning concepts
- Regression vs classification
- Linear regression
- Logistic regression
- Training with labeled data
- Prediction and probability
- Model advantages and limitations
- Business and industry applications
- Hands-on regression and classification models
- Unsupervised learning concepts
- Clustering techniques
- K-Means algorithm
- Customer and behavior segmentation
- Dimensionality reduction
- Principal Component Analysis (PCA)
- Feature simplification
- Marketing and retail use cases
- Hands-on clustering and PCA
- Artificial neural networks
- Neuron, layers, and weights
- Activation functions
- Backpropagation and learning
- Deep learning concepts
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Real-world AI applications
- Training a simple neural network
- Dataset splitting techniques
- Training, validation, and testing sets
- Overfitting and underfitting
- Cross-validation
- Data leakage risks
- Model generalization
- Real-world prediction workflows
- Hands-on data splitting and training
- Model evaluation concepts
- Confusion matrix
- Accuracy, precision, and recall
- F1-score
- Imbalanced data handling
- ROC curve and AUC
- Business-driven metric selection
- Hands-on model evaluation
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