ما هو Machine Learning Scientist with Python Career Track؟
مخطط المنهج الدراسي:
1. Supervised Learning with scikit-learn
a. Classification
b. Regression
c. Fine-tuning your model
d. Preprocessing and pipelines
2. Unsupervised Learning in Python
a. Clustering for dataset exploration
b. Visualization with hierarchical clustering and t-SNE
c. Decorrelating your data and dimension reduction
d. Discovering interpretable features
3. Linear Classifiers in Python
a. Applying logistic regression and SVM
b. Loss functions
c. Logistic regression
d. Support Vector Machines
4. Machine Learning with Tree-Based Models in Python
a. Classification and Regression Trees
b. The Bias-Variance Tradeoff
c. Bagging and Random Forests
d. Boosting
e. Model Tuning
5. Extreme Gradient Boosting with XGBoost
a. Classification with XGBoost
b. Regression with XGBoost
c. Fine-tuning your XGBoost model
d. Using XGBoost in pipelines
6. Cluster Analysis in Python
a. Introduction to Clustering
b. Hierarchical Clustering
c. K-Means Clustering
d. Clustering in Real World
7. Dimensionality Reduction in Python
a. Exploring high dimensional data
b. Feature selection I, selecting for feature information
c. Feature selection II, selecting for model accuracy
d. Feature extraction
8. Preprocessing for Machine Learning in Python
a. Introduction to Data Preprocessing
b. Standardizing Data
c. Feature Engineering
d. Selecting features for modeling
e. Putting it all together
9. Machine Learning for Time Series Data in Python
a. Time Series and Machine Learning Primer
b. Time Series as Inputs to a Model
c. Predicting Time Series Data
d. Validating and Inspecting Time Series Models
10. Feature Engineering for Machine Learning in Python
a. Creating Features
b. Dealing with Messy Data
c. Conforming to Statistical Assumptions
d. Dealing with Text Data
11. Model Validation in Python
a. Basic Modeling in scikit-learn
b. Validation Basics
c. Cross Validation
d. Selecting the best model with Hyperparameter tuning.
12. Introduction to Natural Language Processing in Python
a. Regular expressions & word tokenization
b. Simple topic identification
c. Named-entity recognition
d. Building a "fake news" classifier
13. Feature Engineering for NLP in Python
a. Basic features and readability scores
b. Text preprocessing, POS tagging and NER
c. N-Gram models
d. TF-IDF and similarity scores
14. Introduction to TensorFlow in Python
a. Introduction to TensorFlow
b. Linear models
c. Neural Networks
d. High Level APIs
15. Introduction to Deep Learning in Python
a. Basics of deep learning and neural networks
b. Optimizing a neural network with backward propagation
c. Building deep learning models with keras
d. Fine-tuning keras models
16. Introduction to Deep Learning with Keras
a. Introducing Keras
b. Going Deeper
c. Improving Your Model Performance
d. Advanced Model Architectures
17. Advanced Deep Learning with Keras
a. The Keras Functional API
b. Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers
c. Multiple Inputs: 3 Inputs (and Beyond!)
d. Multiple Outputs
18. Image Processing in Python
a. Introducing Image Processing and scikit-image
b. Filters, Contrast, Transformation and Morphology
c. Image restoration, Noise, Segmentation and Contours
d. Advanced Operations, Detecting Faces and Features
19. Image Processing with Keras in Python
a. Image Processing With Neural Networks
b. Using Convolutions
c. Going Deeper
d. Understanding and Improving Deep Convolutional Networks
20. Hyperparameter Tuning in Python
a. Hyperparameters and Parameters
b. Grid search
c. Random Search
d. Informed Search
21. Introduction to PySpark
22. Machine Learning with PySpark
a. Introduction
b. Classification
c. Regression
d. Ensembles & Pipelines
23. Winning a Kaggle Competition in Python
a. Kaggle competitions process
b. Dive into the Competition
c. Feature Engineering
d. Modeling
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