The School Of
Artificial Intelligence
AI is one of the most transformational and fastest-growing technologies of our time. Our School of Artificial Intelligence offers AI training and machine learning courses as well as programs focusing on deep learning, computer vision, natural language processing, and AI product management.
Learning Paths by Job Title
Machine learning is becoming a fundamental skill as software development is entering a new era. This path will enable you to start a career as a machine learning engineer. First learn the fundamentals of programming in Python, linear algebra, and neural networks, and then move on to core machine learning concepts.
Steps To Become A Machine Learning Engineer
Step 1
Skills Covered
NumPy, matplotlib, Vector visualization, Python data types, Control flow, Python operators, Training neural networks, Backpropagation, PyTorch, AI algorithms in Python, Gradient descent, Limits of functions, Integrals, Derivatives, Implicit differentiation, Chain rule application, Systems of linear equations, Linear algebra, Matrix multiplication, Python exception handling, Lambda expressions, Code debugging, Python function definition, List comprehension, Generators, Variable scope, Iterators, Built-in Python functions, Pip, File i/o, User input handling, Python data structures, Loops, Docstrings, Python scripting, Transformer neural networks
Learn MoreStep 2
Skills Covered
Naive bayes classifiers, Gaussian mixture models, Model evaluation, Support vector machines, Decision trees, Single linkage clustering, K-means clustering, Dimensionality reduction, Market segmentation, Cluster models, Principal component analysis, Independent component analysis, Dbscan, Convolutional kernels, scikit-learn, Perceptron, Categorical data visualization, Statistical modeling fundamentals, Chart types, Quantitative data visualization, Linear regression, Spam detection, Logistic regression, Professional presentations, Hyperparameter tuning, Training neural networks, NumPy, Backpropagation, Overfitting prevention, Deep learning fluency, TensorFlow, Gradient descent, AI algorithms in Python
Learn MoreStep 3
Skills Covered
Naive bayes classifiers, Gaussian mixture models, Model evaluation, Support vector machines, Decision trees, Single linkage clustering, K-means clustering, Dimensionality reduction, Market segmentation, Cluster models, Principal component analysis, Independent component analysis, Dbscan, Convolutional kernels, scikit-learn, Perceptron, Categorical data visualization, Statistical modeling fundamentals, Chart types, Quantitative data visualization, Linear regression, Spam detection, Logistic regression, Professional presentations, Hyperparameter tuning, Gradient descent, AI algorithms in Python, Training neural networks, NumPy, Backpropagation, Overfitting prevention, Deep learning fluency, PyTorch
Learn MoreStep 4
Skills Covered
Neural network basics, Deep learning fluency, Sagemaker jumpstart, Machine learning framework fundamentals, Hyperparameter tuning, Feature engineering, Machine learning fluency, Cloud resource allocation, AWS lambda, Distributed model training with sagemaker, Sagemaker training jobs, Transformer neural networks, Sagemaker debugger, Image classification, Training neural networks, Deep learning model optimization, Transfer learning, PyTorch, Model deployment with sagemaker, Convolutional neural networks, Text classification, Model performance metrics, AI business context, Machine learning use cases, Data loading with sagemaker, Amazon elastic compute cloud, Vpc, Sagemaker feature store, Cloud security in AWS, Cloud cost management, Sagemaker logs, Cloud performance management, AWS storage services, Training data manifest files, Sagemaker autoscaling, Sagemaker processing, Sagemaker batch transform jobs, Sagemaker clarify, Machine learning pipeline creation, Sagemaker pipelines, Model monitoring, Sagemaker model endpoints, AWS step functions, Sagemaker model monitor, Amazon s3, Model training, Linear models, Xgboost, Autogluon, Pandas, Sagemaker studio notebooks, Tree-based models, Sagemaker ground truth, Machine learning lifecycle, Dataset annotation, Machine learning dataset fundamentals, scikit-learn, Automated machine learning, Sagemaker data wrangler
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Artificial Intelligence Across Industries
Manufacturing
- Artificial Intelligence is used for predictive maintenance, quality control, supply chain optimization, robotics, and process automation.
- It helps manufacturers optimize production, reduce downtime, and improve product quality.
Telecommunications
- Artificial Intelligence is employed in customer service chatbots, network optimization, fraud detection, and predictive analytics.
- It enhances customer support, improves network performance, and identifies potential issues in real-time.
Energy
- Artificial Intelligence is used for energy grid optimization, predictive maintenance of infrastructure, demand response management, and renewable energy forecasting.
- It enables efficient energy usage, reduces costs, and supports sustainable practices.
Healthcare
- Artificial Intelligence is used in medical imaging analysis, drug discovery, personalized medicine, patient monitoring, and disease diagnosis.
- It helps healthcare professionals make more accurate diagnoses, predict disease outcomes, and enhance patient care.
Programs Co-created With Artificial Intelligence Leaders.
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