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Data Science & Machine Learning with Python

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Details

Through this Machine Learning course, you will learn how to process, clean, visualize and analyse data by using Python, one of the most popular machine learning tools.
  • After a broad overview of the discipline's most common techniques and applications, you'll gain more insight into the assessment and training of different machine learning models.
  • The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression and clustering.

Outline

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

Schedules

Mar 01, 2020 - Jun 25, 2020
ENDED
Tue, Thu, Sun 06:00 PM — 10:00 PM
Mar 14, 2020 - Jun 27, 2020
ENDED
Mon, Wed, Sat 06:00 PM — 10:00 PM
Total Hours: 100
No. of Participants: 15
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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.

25 Years in the market.

We delivered over 4,200 courses to 63,500 professionals in our centers.

We delivered 1,200 courses to 18,240 corporate employees on Site.

 
CLS Learning Solutions
5 Elmesaha ,Dokki, Giza, Egypt 12421
02-33370280
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