AI ML Python Deep Learning
Instructor-led training in AI ML Python Deep Learning. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
This intermediate-level course dives into the core concepts and practical applications of Artificial Intelligence, Machine Learning, and Deep Learning using Python. Participants will gain hands-on experience building, training, and deploying intelligent models to solve complex real-world problems and drive data-driven innovation.
What you'll learn
- You will be able to confidently implement machine learning algorithms using Python and popular libraries.
- You will be able to design and train various types of neural networks for different data types.
- You will be able to preprocess and prepare diverse datasets for AI and ML model training.
- You will be able to evaluate model performance and apply techniques to optimize their accuracy and efficiency.
- You will be able to understand the mathematical and statistical foundations behind key AI and ML concepts.
- You will be able to build and apply Convolutional Neural Networks (CNNs) for image recognition tasks.
- You will be able to develop Recurrent Neural Networks (RNNs) for sequential data processing like natural language.
- You will be able to deploy basic machine learning models into practical applications.
Curriculum
6 modules · outline is indicative and can be tailored to your team.
1Foundations of AI, ML, and Deep Learning
- Introduction to AI, Machine Learning, and Deep Learning
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement
- Key components of a Machine Learning project pipeline
- Setting up your Python environment (Anaconda, Jupyter Notebooks)
- Introduction to NumPy and Pandas for data manipulation
2Essential Machine Learning Algorithms
- Linear and Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVMs)
- K-Nearest Neighbors (KNN) and K-Means Clustering
- Model evaluation metrics (accuracy, precision, recall, F1-score)
3Deep Learning with Keras and TensorFlow
- Introduction to Neural Networks and Perceptrons
- Activation functions and loss functions
- Building your first neural network with Keras
- Training neural networks: optimization, backpropagation, epochs
- Regularization techniques (dropout, L1/L2)
4Convolutional Neural Networks (CNNs)
- Understanding image data and convolutions
- Architecture of CNNs: convolutional layers, pooling layers, fully connected layers
- Building and training CNNs for image classification
- Transfer learning and pre-trained models (e.g., VGG, ResNet)
- Data augmentation techniques for image datasets
5Recurrent Neural Networks (RNNs) and Sequence Models
- Introduction to sequential data and time series
- Basic RNN architecture and limitations
- Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks
- Applications in Natural Language Processing (NLP) and time series prediction
- Embedding layers for text data
6Model Deployment and Best Practices
- Hyperparameter tuning and cross-validation
- Introduction to model deployment strategies
- Ethical considerations in AI and ML
- Monitoring and maintaining models in production
Prerequisites
Participants should have a foundational understanding of Python programming, including data structures and control flow, along with basic knowledge of statistics and linear algebra.
Who should attend
This course is ideal for data scientists, machine learning engineers, software developers, and analysts looking to deepen their expertise in AI and Deep Learning.
Certification
Frequently asked questions
What is the delivery format for this course?
This is an instructor-led, live-online course, providing real-time interaction with the instructor and fellow participants.
How long is this course?
The duration of the course is typically spread over several days or weeks, with specific timings detailed in the course schedule.
Will I receive a certificate upon completion?
Yes, upon successful completion of the course, you will receive an Skilvi course-completion certificate.
Are there any exams or certifications associated with this course?
While this course does not directly lead to a vendor certification, it provides foundational knowledge for various industry certifications. Exam vouchers are available through authorized channels on request.
What are the prerequisites for this course?
A foundational understanding of Python programming, including data structures and control flow, along with basic knowledge of statistics and linear algebra.
Do I need to bring my own software or tools?
All necessary software and tools, primarily Python with relevant libraries, will be provided or guided for installation as part of the course setup.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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