# DevOps Artisan – Machine Learning in TensorFlow/Keras​ Fundamentals

Cursul DevOps Artisan – Machine Learning in TensorFlow/Keras​ Fundamentals se adresează tuturor celor care doresc să afle mai multe despre Inteligența Artificială.

## Ce vei învăța?

În cadrul acestui curs, participanții vor configura mediul, vor scrie primele linii de cod in Python folosind biblioteci numerice și tehnici de vizualizare a datelor. Acest curs poate fi utilizat ca o sursă de informații de sine stătătoare sau ca un pas spre Learning Path-ul Machine Learning.

## Cerințe preliminare:

Pentru a putea participa la acest curs, studenții trebuie să aibă cunoștințe la nivel basic de Data Science in Python.

## Agenda cursului:

Materialele de curs sunt în limba Engleză. Predarea se face în limba Română.

## Agenda cursului:

Materialele de curs sunt în limba Engleză. Predarea se face în limba Română.

Module 1: Introduction

• What is ML?
• Where can I find it in real life?
• Why now?
• What are the three main categories of ML?
• Supervised learning
• Unsupervised learning
• Reinforcement learning (demo)
• ML pipeline

Module 2: Machine Learning with sci-kit

• ML pipeline review
• scikit Python Library
• Data representation
• Feature matrix
• Target array
• Iris dataset example
• Estimator API
• Linear Regression
• Simple Linear Regression
• Model Evaluation
• Polynomial Regression

Hands-on Lab: Doing a little bit of data preprocessing, analyzing the difference between categorical and numerical data, plotting some relevant statistical values and visually inspecting the correlation between features

• Selecting the best model
• Logistic Regression
• Who survives the Titanic?
• Naive Bayes
• Gaussian Naive Bayes
• Multinomial Naive Bayes
• Categorical Naive Bayes
• k Nearest Neighbours
• k-Means Clustering
• Dimensionality reduction
• Principal Components Analysis (PCA)
• Singular Value Decomposition (SVD)
• Decision Trees
• Random Forests

Hands-on Lab: Playing around with different values affecting the bias and the variance, calculating precision, recall, F1 and F2-scores, comparing different models on the training and testing accuracies.

Module 3: Neural Networks in Tensorflow/Keras

• Artificial Neural Networks (ANNs)
• Neurons
• Layers
• Activation Functions
• More vocabulary
• Popular Frameworks
• Keras
• Linear Regression
• Defining Models in Keras
• Training and predicting
• Fashion MNIST example

Hands-on Lab: Creating our first custom neural network model; choosing the number of layers and the number of neurons per layer; tweaking the learning rate. Training the neural network on real world data.

Module 4: Convolutional Neural Networks (peek)

• Motivation behind CNNs
• CNN Building blocks
• Convolution Layers
• Pooling Layers
• CNNs in Keras
• Data Augmentation
• Architectures

Module 5: NLP using Deep Learning

• Spam detector
• Sentiment analyzer
• Autocomplete

Module 6: Reinforcement Learning

• Frozen Lake demo
• Flappy Bird demo

Module 7: Recommender Systems

• Data preparation
• Cosine distance
• SVD for recommender systems
• Autoencoder demo

## DevOps Artisan – Machine Learning in TensorFlow/Keras​ Fundamentals 2
zile

840 EUR

Clasă virtuală

2. Associate

DevOps