Review of Python Courses (Part 27)

In Part 26, I summarized my Datacamp courses 77-79. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #80 was Model Validation in Python. This course covers:

• Introduction to model validation
• Regression models (from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier)
• Classification models
• Creating train, test, and validation datasets (creating a holdout set)
• Accuracy metrics: regression models (from sklearn.metrics import mean_absolute_error, mean_squared_error)
• Classification metrics (from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score)
• Problems with holdout sets
• Cross-validation (from sklearn.model_selection import KFold)
• cross_val_score (from sklearn.model_selection import cross_val_score; from sklearn.metrics import make_scorer)
• Leave-one-out-cross-validation
• Introduction to hyperparameter tuning
• RandomizedSearchCV
• Selecting your final model

My course #81 was Image Processing in Python. Topics covered in this course include:

• Make images come alive with scikit-image (from skimage import data, color)
• NumPy for images
• Getting started with thresholding (from skimage.filters import try_all_threshold)
• Jump into filtering (from skimage.filters import sobel, gaussian)
• Contrast enhancement (from skimage import exposure)
• Transformations (from skimage.transform import rotate, rescale, resize)
• Morphology (from skimage import morphology)
• Image restoration (from skimage.restoration import inpaint)
• Noise (from skimage.util import random_noise; from skimage.restoration import denoise_tv_chambolle)
• Superpixels and segmentation (from skimage.segmentation import slic; from skimage.color import label2rgb)
• Finding contours (from skimage import measure)
• Finding the edges with Canny (from skimage.feature import canny)
• Right around the corner (from skimage.feature import corner_harris)
• Face detection (from skimage.feature import Cascade)
• Real-world applications

My course #82 was Recurrent Neural Networks for Language Modeling in Python. This course covers:

• Introduction to the course
• Introduction to RNN inside Keras (from keras.models import Sequential; from keras.layers import Dense)
• Vanishing and exploding gradients
• GRU and LSTM cells (from keras.layers import GRU, Dense; from keras.layers.recurrent import LSTM)
• The embedding layer (from keras.layers import Embedding; from keras.initializers import Constant)
• Sentiment classification revisited
• Data pre-processing (from keras.utils.np_utils import to_categorical)
• Transfer learning for language models (from gensim.models import word2vec, fasttext)
• Multi-class classification models (from sklearn.datasets import fetch_20newsgroups)
• Assessing the model’s performance (from sklearn.metrics import confusion_matrix, f1_score, classification_report)
• Sequence to sequence models
• Text generation models (from keras.preprocessing.text import Tokenizer)
• Neural machine translation (from keras.preprocessing.sequence import pad_sequences)

I will review more courses next time.