Machine Learning, Deep Learning + +AWS Sagemaker

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Embark on a transformative educational journey with our comprehensive video course on “Machine Learning, Deep Learning + AWS SageMaker” available exclusively on howtofree.org. With over 30 hours of engaging and insightful content, this course is designed to empower learners with the skills and knowledge needed to master the dynamic trio of Machine Learning, Deep Learning, and AWS SageMaker.

Explore the intricate world of machine intelligence as we delve into the fundamentals of Machine Learning and Deep Learning. Our carefully crafted lessons leverage active voice and clear explanations to guide you through the intricacies of these cutting-edge technologies. The course seamlessly integrates hands-on practice with theoretical understanding, ensuring a well-rounded learning experience.

Elevate your expertise further with a dedicated focus on AWS SageMaker, a powerful cloud machine learning platform. Learn how to harness its capabilities to build, train, and deploy machine learning models at scale. Gain practical insights into optimizing your workflow and achieving operational efficiency with AWS SageMaker.

What You Will Learn:

  • Deep Learning with Tensorflow
  • Deep Learning with PyTorch (Both Tensorflow and PyTorch covered!)
  • AWS Sagemaker
  • Data Analysis with Pandas
  • Maximizing the potential of Scikit-learn
  • Building Algorithms from scratch using Numpy
  • Model Deployment
  • Bayesian Learning with PyMC3
  • Model Diagnostics
  • Natural Language Processing
  • Unsupervised Learning
  • Natural Language Processing with Spacy
  • Time Series Modeling with FB Prophet
  • Python”

Course Content

1. Introduction

  • Introduction
  • How to tackle this course
  • Installations and sign_ups
  • Jupyter Notebooks
  • Course Material
  • Google Drive Link for All Course Material

2. Basic python + Pandas + Plotting

  • Intro
  • Basic Data Structures
  • Dictionaries
  • Python functions (methods)
  • Numpy functions
  • Conditional statements
  • For loops
  • Dictionaries again
  • Pandas –
  • Intro
  • Pandas simple functions
  • Pandas: Subsetting
  • Pandas: loc and iloc
  • Pandas: loc and iloc 2
  • Pandas: map and apply
  • Pandas: groupby
  • Plotting —
  • Plotting resources (notebooks)
  • Line plot
  • Plot multiple lines
  • Histograms
  • Scatter Plots
  • Subplots
  • Seaborn + pair plots

3. Machine Learning: Numpy + Scikit Learn

  • Your reviews are important to me!
  • — Numpy –
  • Gradient Descent
  • Kmeans pact-I
  • Kmeans part 2
  • Broadcasting
  • Scikit Learn –
  • Intro
  • Linear Regresson Part 1
  • Linear Regression Part 2
  • Classification and Regression Trees
  • CART part 2
  • Random Forest theory
  • Random Forest Code
  • Gradient Boosted Machines

4. Machine Learning: Classification + Time Series + Model

  • Diagnostics
  • Kaggle part
  • Kaggle part 2
  • Theory part 1
  • Theory pan 2 + code
  • Titanic dataset
  • Sklearn classification prelude
  • Sklearn classification
  • Dealing with missing values
  • — Time Series —
  • Intro
  • Loss functions
  • FB Prophet part 1
  • FB Prophet part 2
  • Theory behind FB Prophet
  • Overfitting
  • Cross Validation
  • Stratified K Fold
  • Area Under Curve (AIJC) Pan 1
  • Area Under Curve (AIJC) Part 2

5. Unsupervised Learning

  • Principal Component Analysis (PCA) theory
  • Fashion MNIST PCA
  • K-means
  • Other clustering methods
  • DBSCAN theory
  • Gaussian Mixture Models (GMM) theory

6. Natural Language Processing + Regularization

  • Intro
  • Stop words and Term Frequency
  • Term Frequency – Inverse Document Frequency (Tf – ldf) theory
  • Financial News Sentiment Classifier
  • NLTK + Stemming
  • N -grams
  • Word (feature) importance
  • Spacy intro
  • Feature Extraction with Spacy (using Pandas)
  • Classification Example
  • Over-sampling
  • Regularization –
  • Introduction
  • MSE recap
  • L2 Loss / Ridge Regression intro
  • MSE recap
  • L2 Loss / Ridge Regression intro
  • Ridge regression (L2 penalised regression)
  • S&P500 data preparation for Ll loss
  • Ll Penalised Regression (Lasso)
  • Ll/ L2 Penalty theory: why it works

7. Deep Learning

  • Intro
  • DL theory part 1
  • DL theory part 2
  • Tensorflow + Keras demo problem 1
  • Activation functions
  • First example with Relu
  • MNIST and Softmax
  • Deep Learning Input Normalisation
  • Softmax theory
  • Batch Norm
  • Batch Norm Theory

8. Deep Learning (TensorFlow) – Convolutional Neural Nets

  • Intro
  • Fashion MNIST feed forward net for benchmarking
  • Keras Conv2D layer
  • Model fitting and discussion of results
  • Dropout theory and code
  • MaxPool (and comparison to stride)
  • Cifar-10
  • Nose Tip detection with CNNs

9. Deep Learning: Recurrent Neural Nets

  • Word2vec and Embeddings
  • Kaggle + Word2Vec
  • Word2Vec: keras Model API
  • Recurrent Neural Nets – Theory
  • Deep Learning – Long Short Term Memory (LSTM) Nets
  • Deep Learning – Stacking LSTMs + GRUs
  • Transfer Learning – GLOVE vectors
  • Sequence to Sequence Introduction + Data Prep
  • Sequence to Sequence model + Keras Model API
  • Sequence to Sequence models: Prediction step

10. Deep Learning: PyTorch Introduction

  • Notebooks
  • Introduction v
  • Pytorch: TensorDataset
  • Pytorch: Dataset and DataLoaders
  • Deep Learning with PyTorch: nn.Sequential models
  • Deep Learning with Pytorch: Loss functions
  • Deep Learning with Pytorch: Stochastic Gradient Descent
  • Deep Learning with Pytorch: Optimizers
  • Pytorch Model API
  • Pytorch in GPUs
  • Deep Learning: Intro to Pytorch Lightning

11. Deep Learning: Transfer Learning with PyTorch Lightning
Notebooks

  • Transfer Learning Introduction
  • Kaggle problem description
  • PyTorch datasets + Torchvision
  • PyTorch transfer learning with ResNet
  • PyTorch Lightning Model
  • PyTorch Lightning Trainer + Model evaluation
  • Deep Learning for Cassava Leaf Classification
  • Cassava Leaf Dataset
  • Data Augmentation with Torchvision Transforms
  • Train vs Test Augmentations + DataLoader parameters
  • Deep Learning: Transfer Learning Model with ResNet
  • Setting up PyTorch Lightning for training
  • Cross Entropy Loss for Imbalanced Classes
  • PyTorch Test dataset setup and evaluation
  • WandB for logging experiments

12. Pixel Level Segmentation (Semantic Segmentation) with
pyTorch

  • Notebooks
  • Introduction
  • Coco Dataset + Augmentations for Segmentation with
  • Torchvision
  • Unet Architecture overview
  • PyTorch Model Architecture
  • PyTorch Hooks
  • PyTorch Hooks: Step through with breakpoints
  • PyTorch Weighted CrossEntropy Loss
  • Weights and Biases: Logging images.
  • Semantic Segmentation training with PyTorch Lightning

13. Deep Learning: Transformers and BERT

  • Resources
  • Introduction to Transformers
  • The illustrated Transformer (blogpost by Jay Alammar)
  • Encoder Transformer Models: The Maths
  • BERT – The theory
  • Kaggle Multi-lingual Toxic Comment Classification Challenge
  • Tokenizers and data prep for BERT models
  • Distilbert (Smaller BERT) model
  • Pytorch Lightning + DistilBERT for classification

14. Bayesian Learning and probabilistic programming

  • Introduction and Terminology
  • Bayesian Learning: Distributions
  • Bayes rule for population mean estimation
  • Bayesian learning: Population estimation pymc3 way
  • Coin Toss Example with Pymc3
  • Data Setup for Bayesian Linear Regression
  • Bayesian Linear Regression with pymc3
  • Bayesian Rolling Regression – Problem setup
  • Bayesian Rolling regression – pymc3 way
  • Bayesian Rolling Regression – forecasting
  • Variational Bayes Intro
  • Variational Bayes: Linear Classification
  • Variational Bayesian Inference: Result Analysis
  • Minibatch Variational Bayes
  • Deep Bayesian Networks
  • Deep Bayesian Networks – analysis

15. Model Deployment

  • Intro
  • Saving Models
  • FastAPl intro
  • FastAPl serving model
  • Streamlit Intro
  • Streamlit functions
  • CLIP model

16. AWS Sagemaker (for Model Deployment)

  • Resources
  • Introduction and WARNING (Must watch!)
  • Setting up AWS
  • awscli + IAM setup
  • AWS s3 introduction + bash scriptting
  • AWS IAM roles
  • AWS Sagemaker – Processing jobs Part 1
  • Sagemaker Processing – Part 2
  • Sagemaker Training – Part 1
  • Sagemaker Training – Pan 2
  • AWS Cloudwatch
  • AWS Sagemaker inference (model deployment)
  • AWS Sagemaker Inference – Part 2
  • AWS Sagemaker Inference – Part 3
  • AWS Billing
  • Part 1

17. Final Thoughts

  • Some advice on your journey
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