Artificial Intelligence

Steps To Become A Machine Learning Engineer

• 

  AI Programming with Python

  (592)

  2 months

  , Beginner

  Step 1

  

  AI Programming with Python

  (592)

  2 months

  , Beginner

  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

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• 

  Introduction to Machine Learning with TensorFlow

  (256)

  2 months

  , Intermediate

  Step 2

  

  Introduction to Machine Learning with TensorFlow

  (256)

  2 months

  , Intermediate

  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

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• 

  Introduction to Machine Learning with Pytorch

  (235)

  3 months

  , Intermediate

  Step 3

  

  Introduction to Machine Learning with Pytorch

  (235)

  3 months

  , Intermediate

  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

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• 

  AWS Machine Learning Engineer Nanodegree

  (41)

  5 months

  , Intermediate

  Step 4

  

  AWS Machine Learning Engineer Nanodegree

  (41)

  5 months

  , Intermediate

  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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