تفاصيل الدورة
Introduction to Data Science- 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
المتطلبات
You should be comfortable with Python, including functions, control flow, lists, and loops.
الموقع
نبذة عن معهد CLS Learn
Since 1995, CLS Learning solutions is leading the technology learning market in Egypt, the Middle East, and Africa. With our wide network of international partners, trainers, instructors, and technology leaders; we are able to deliver top notch training programs to our students and technology professionals.
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