AI Programming with Python
(639)
52 hours
Beginner
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.
Your Path to Career Success
Chart your path to a $200k+ career in tech
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
AI Programming with Python(639)
52 hours
Beginner
Skills Covered
Generative AI Awareness, Text generation, Attention mechanisms, GPT, Hugging Face, Transformer neural networks, Foundation Model Concepts, Word embeddings, PyTorch, Natural language processing, NLP transformers, Logistic regression, Deep learning framework proficiency, Classification models, Feedforward neural networks, Deep learning, Transfer learning, Training neural networks, Neural network basics, Basic PyTorch, Gradient descent, Perceptron, Neural network mechanics, Backpropagation, Python package management, Pandas, Pip, Anaconda, matplotlib, Jupyter notebooks, NumPy, Python packaging, Python functions, Basic Python, Python methods, Text processing in Python, Functional Python, Boolean expressions, Python operators, List comprehension, Python syntax, Python data types, Python best practices, Python variables, Control flow in Python, Python Certified Entry-Level Programmer, Python string methods, Python exception handling, Built-in Python functions, Python function definition, Python data structures, Python collections
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Introduction to Machine Learning with TensorFlow(275)
49 hours
Intermediate
Step 2
Introduction to Machine Learning with TensorFlow(275)
49 hours
Intermediate
Skills Covered
Naive bayes classifiers, Gaussian mixture models, Model evaluation, Support vector machines, Decision trees, Single linkage 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, Spam detection, Logistic regression, Professional presentations, Hyperparameter tuning, Training neural networks, NumPy, Backpropagation, Overfitting prevention, Deep learning fluency, TensorFlow, AI algorithms in Python, K-means clustering, Gradient descent, Linear regression
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Introduction to Machine Learning with Pytorch(250)
49 hours
Intermediate
Step 3
Introduction to Machine Learning with Pytorch(250)
49 hours
Intermediate
Skills Covered
Naive bayes classifiers, Gaussian mixture models, Model evaluation, Support vector machines, Decision trees, Single linkage 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, Spam detection, Logistic regression, Professional presentations, Hyperparameter tuning, Gradient descent, AI algorithms in Python, Training neural networks, NumPy, Backpropagation, Overfitting prevention, Deep learning fluency, K-means clustering, PyTorch, Linear regression
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AWS Machine Learning Engineer Nanodegree(56)
94 hours
Intermediate
Step 4
AWS Machine Learning Engineer Nanodegree(56)
94 hours
Intermediate
Skills Covered
Neural network basics, Sagemaker jumpstart, Machine learning framework fundamentals, 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, 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, Vpc, Hyperparameter tuning
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Deep learning is driving advances in artificial intelligence that are changing our world. To join this field, start by learning Python fundamentals and neural networks, move on to core machine learning concepts, and then apply deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.
Steps To Become A Deep Learning Engineer
- AI Programming with Python
(639)
52 hours
Beginner
Step 1
AI Programming with Python(639)
52 hours
Beginner
Skills Covered
Generative AI Awareness, Text generation, Attention mechanisms, GPT, Hugging Face, Transformer neural networks, Foundation Model Concepts, Word embeddings, PyTorch, Natural language processing, NLP transformers, Logistic regression, Deep learning framework proficiency, Classification models, Feedforward neural networks, Deep learning, Transfer learning, Training neural networks, Neural network basics, Basic PyTorch, Gradient descent, Perceptron, Neural network mechanics, Backpropagation, Python package management, Pandas, Pip, Anaconda, matplotlib, Jupyter notebooks, NumPy, Python packaging, Python functions, Basic Python, Python methods, Text processing in Python, Functional Python, Boolean expressions, Python operators, List comprehension, Python syntax, Python data types, Python best practices, Python variables, Control flow in Python, Python Certified Entry-Level Programmer, Python string methods, Python exception handling, Built-in Python functions, Python function definition, Python data structures, Python collections
Learn More - AWS Machine Learning Engineer Nanodegree
(56)
94 hours
Intermediate
Step 2
AWS Machine Learning Engineer Nanodegree(56)
94 hours
Intermediate
Skills Covered
Neural network basics, Sagemaker jumpstart, Machine learning framework fundamentals, 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, 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, Vpc, Hyperparameter tuning
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Deep Learning(979)
61 hours
Intermediate
Step 3
Deep Learning(979)
61 hours
Intermediate
Skills Covered
GAN Training Optimization, Generative adversarial networks, U-net, Generative AI Fluency, Implementing GAN Training Optimization, Implementing Diffusion Model Sampling, Implementing Generator Networks, Deep Convolutional GAN Architecture, Latent Space Concepts, Implementing Diffusion Models, Implementing Discriminator Networks, Conditional Image Generation, Adversarial Training Concepts, GenAI Architectures, Implementing Deep Convolutional GANs, Diffusion Models, Implementing GAN Training, Implementing Conditional GANs, Recurrent neural networks, Implementing Word Embeddings, Transformer neural networks, Word embeddings, Implementing Attention Mechanisms, Generative AI Evaluation, Implementing Recurrent Neural Networks, Hugging Face, Implementing Sequence-to-Sequence Models, Generative AI Evaluation Implementation, Sequential text analysis, Sequence-to-Sequence Models, NLP models, Implementing Tokenization, Attention mechanisms, Tokenization, Implementing Transformer Fine-Tuning, Implementing Data Augmentation Pipelines, Implementing Neural Style Transfer, Computer vision fluency, Data augmentation, Semantic image segmentation, Implementing Object Detection, Convolutional kernels, Convolutional Layer Configuration, Deep learning techniques, Convolutional neural networks, Implementing Convolutional Layers, Implementing Convolutional Autoencoders, Autoencoders, Implementing CNN Pooling Layers, CNN Architecture Evolution, CNN Pooling Concepts, Transfer learning, Implementing Advanced CNN Training, Implementing Image Segmentation, Feature visualization, Implementing Convolutional Neural Networks, Object detection, Implementing CNN Transfer Learning, Neural network basics, Backpropagation, Implementing Feedforward Neural Networks, Deep learning model optimization, Model evaluation, Deep learning fluency, Implementing Neural Network Forward Propagation, Deep learning models, Data pre-processing for ML, Hyperparameter tuning, Neural network activation, Neural network mechanics, Model performance metrics, Deep Learning Model Diagnostics, Perceptron, Neural Network Loss Function Concepts, Implementing Neural Network Activation Functions, Implementing Deep Learning Data Pipelines, Feedforward neural networks, Training neural networks, Implementing Deep Learning Model Diagnostics, Implementing Neural Network Loss Functions, Deep learning
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Artificial intelligence is expected to be a $60 billion industry by 2025. Learn AI skills in specialized fields like computer vision, natural language processing, deep reinforcement learning, or core AI algorithms. Each of these programs covers advanced topics, building on your existing skills in programming, deep learning, and machine learning.
Steps To Become An Artificial Intelligence Specialist
Computer Vision(478)
37 hours
Advanced
Step 1
Computer Vision(478)
37 hours
Advanced
Skills Covered
Slam, Object detection, Object localization, Attention mechanisms, Feature matching, Long-short term memory networks, Yolo algorithm, Model training, Convolutional neural networks, Neural network memory, Image caption generation, PyTorch, Image classification, Data augmentation, K-means clustering, Feature visualization, Convolutional kernels, Object recognition, Hough transforms, Neural network basics, Feature embeddings, 2d image transforms, Neural network activation, Feature detection, Object tracking, Recurrent neural networks, Facial recognition
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Natural Language Processing(309)
53 hours
Advanced
Step 2
Natural Language Processing(309)
53 hours
Advanced
Skills Covered
Machine translation, Alexa skill creation, Attention mechanisms, Fasttext, Voice user interfaces, Alexa skill deployment, Feature extraction, Information extraction, Information retrieval, Text summarization, Image caption generation, Word2vec, NLP models, Question answering systems, Glove, Speech recognition
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Deep Reinforcement Learning(357)
83 hours
Advanced
Step 3
Deep Reinforcement Learning(357)
83 hours
Advanced
Skills Covered
Value-based reinforcement learning, Stochastic policy gradients, Reinforce algorithm, Exploration-exploitation dilemma, Markov decision processes, Multi-agent training, Markov games, Alphazero, Policy optimization algorithms, Evolutionary algorithms, Monte carlo policy gradients, Generalized advantage estimation, Prioritized experience replay, Deep q-networks, Double deep q-networks, Dueling deep q-networks, Multi-armed bandit problems, Bellman equation, Policy-based reinforcement learning, Continuous functions, Monte carlo methods, Dynamic programming
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Artificial Intelligence(217)
40 hours
Advanced
Step 4
Artificial Intelligence(217)
40 hours
Advanced
Skills Covered
Optimization algorithms, Likelihood function, Minimax search, Bayesian networks, First order logic, Constraint propagation, Constraint satisfaction problems, Part of speech tagging, Basic probability, Ibm watson, Viterbi algorithm, Text pre-processing, Baum-welch algorithm, Time-series analysis with ML, State space search, Multi-agent training, Simulated annealing, A* search algorithm, Uninformed search, Search algorithms, Hill climbing, Search implementation in Python, Informed search, Automated planning problem definition, Propositional logic, Planning algorithms, Automated planning heuristics, Planning graphs, Backtracking search, AI algorithms in Python, Hidden markov models, Uniform cost search, Algorithmic problem solving, Breadth-first search, Heuristic evaluations, Depth-first search
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Data-driven traders are now responsible for more than 30% of all U.S. stock trades by investors (or about $1 trillion USD worth of investments). Learn artificial intelligence by building programming and linear algebra skills, then learn to analyze real data and develop financial models for trading.
Steps To Become A Quantitative Analyst
- AI Programming with Python
(639)
52 hours
Beginner
Step 1
AI Programming with Python(639)
52 hours
Beginner
Skills Covered
Generative AI Awareness, Text generation, Attention mechanisms, GPT, Hugging Face, Transformer neural networks, Foundation Model Concepts, Word embeddings, PyTorch, Natural language processing, NLP transformers, Logistic regression, Deep learning framework proficiency, Classification models, Feedforward neural networks, Deep learning, Transfer learning, Training neural networks, Neural network basics, Basic PyTorch, Gradient descent, Perceptron, Neural network mechanics, Backpropagation, Python package management, Pandas, Pip, Anaconda, matplotlib, Jupyter notebooks, NumPy, Python packaging, Python functions, Basic Python, Python methods, Text processing in Python, Functional Python, Boolean expressions, Python operators, List comprehension, Python syntax, Python data types, Python best practices, Python variables, Control flow in Python, Python Certified Entry-Level Programmer, Python string methods, Python exception handling, Built-in Python functions, Python function definition, Python data structures, Python collections
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AI Trading Strategies96 hours
Advanced
Step 2
AI Trading Strategies96 hours
Advanced
Skills Covered
Automated plan optimization, Backtesting, Feature engineering, Financial analysis with AI, Quant workflow, Unsupervised machine learning, Model drift, Hyperparameter tuning, Data pre-processing, Deep reinforcement learning, Finance metrics, Data cleaning, Exploratory data analysis, Trading signals, Reinforcement learning fundamentals, Basic supervised machine learning, Basic unsupervised learning
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Popular Artificial Intelligence Programs
- AI Trading Strategies
96 hours
Advanced
- AI Programming with Python
(639)
52 hours
Beginner
- Deep Learning
(979)
61 hours
Intermediate
- Deep Reinforcement Learning
(357)
83 hours
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- AWS Machine Learning Engineer Nanodegree
(56)
94 hours
Intermediate
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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