Machine Learning with Python هذه الدورة تدرس باللغة الإنجليزية القاهرة

تفاصيل الدورة

• What is data science and why is it so important?
• Applications of data science
• Various data science tools
• Data Science project methodology
• Tool of choice-Python: what & why?
• Case study
• Introduction to Python
• Installation of Python framework and packages: Anaconda & pip
• Writing/Running python programs using Spyder Command Prompt
• Working with Jupyter notebooks
• Creating Python variables
• Numeric , string and logical operations
• Data containers : Lists , Dictionaries, Tuples & sets
• Practice assignment
• Iterative Operations & Functions in Python
• Writing for loops in Python
• While loops and conditional blocks
• List/Dictionary comprehensions with loops
• Writing your own functions in Python
• Writing your own classes and functions
• Practice assignment
• Data summary & visualization in Python
• Need for data summary & visualization
•  Summarizing numeric data in pandas
• Summarizing categorical data
• Group wise summary of mixed data
• Basics of visualization with ggplot & Sea born
• Inferential visualization with Sea born
• Visual summary of different data combinations
• Practice assignment
• Data Handling in Python using NumPy & Pandas
•  Introduction to NumPy arrays, functions & properties
• Introduction to Pandas & data frames
• Importing and exporting external data in Python
• Feature engineering using Python
• Machine Learning in Python Machine Learning Basics
• Converting business problems to data problems
• Understanding supervised and unsupervised learning with examples
• Understanding biases associated with any machine learning algorithm
• Ways of reducing bias and increasing generalization capabilities
• Drivers of machine learning algorithms
• Cost functions
• Brief introduction to gradient descent
• Importance of model validation
• Methods of model validation
• Cross validation & average error
• Generalized Linear Models in Python
• Linear Regression
• Regularization of Generalized Linear Models
• Ridge and Lasso Regression
• Logistic Regression
• Methods of threshold determination and performance measures for classification score models
• Case Study
• Tree Models using Python
•  Introduction to decision trees
• Tuning tree size with cross validation
• Introduction to bagging algorithm
• Random Forests
• Grid search and randomized grid search Extra Trees (Extremely Randomized Trees)
• Partial dependence plots
• Case Study & Assignment
• Support Vector Machines (SVM) & kNN in Python
• Introduction to idea of observation based learning
• Distances and similarities
•  k Nearest Neighbors (kNN) for classification
• Brief mathematical background on SVM/li>
• Regression with kNN & SVM
• Case Study
• Unsupervised learning in Python
• Need for dimensionality reduction
• Principal Component Analysis (PCA)
• Difference between PCAs and Latent Factors
• Factor Analysis
• Hierarchical, K-means & DBSCAN Clustering
• Case study
• Artificial Neural Networks in Python
• Introduction to Neural Networks
• Single layer neural network
• Multiple layer Neural network
• Back propagation Algorithm
• Neural Networks Implementation in Python
• Case study