MACHINE LEARNING

   USING PYTHON    

FOR PROFESSIONAL

Machine Learning using Python 

Course Overview

Description:

This Machine Learning with Python course dives into the basics of machine    learning  using  an approachable,and  well-known, programming language. l learn about Supervised vs. Unsupervised  learning, look into how Statistical  Modeling relates to Machine Learning, and do a comparison of each. 

Prerequesites:

Expectations and Goals:

This course helps participants understand what data scientists do, the problems they solved at the tools and techniques they use. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field. 

Page 1/9

This course is suitable for students, developers, data analysts, and statisticians with basic knowledge of Computer Science and python programming. 

Arrays and Swapping Axes Universal Functions: Fast Element-Wise Array Functions Array-Oriented Programming with Arrays Expressing Conditional Logic as Array Operations Mathematical and Statistical Methods Methods for Boolean Arrays Sorting Unique and Other Set Logic File Input and Output with Arrays Linear Algebra Pseudorandom Number Generation Example: Random Walks Simulating Many Random Walks at Once 

Getting Started with pandas     
Introduction to pandas Data Structures 
Series 
DataFrame 
Index Objects 
Essential Functionality 
Reindexing 
Dropping Entries from an Axix 
Indexing, Selection, and Filtering 
Integer Indexes 
Arithmetic and Data Alignment 
Function Application and Mapping 
Sorting and Ranking 
Axis Indexes with Duplicate Labels 

Module 3:

Page 3/9

The Machine Learning Landscape
What Is Machine Learning?     
Types of Machine Learning Systems 
Why Use Machine Learning? 
Supervised/Unsupervised Learning 
Batch and Online Learning 
Instance-Based Versus Model-Based Learning 
Main Challenges of Machine Learning 
Insufficient Quantity of Training Data 
Non representative Training Data 
Poor-Quality Data 
Irrelevant Features 
Overfitting the Training Data 
Underfitting the Training Data 
Testing and Validating 

Page 2/9

Topics Covered:

Module 1:

NumPy Basics: Arrays and Vectorized Computation  
The NumPy ndarray: A Multidimensional Array Object 
Creating ndarrays 
Data Types for ndarrays 

Arithmetic with NumPy Arrays Basic Indexing and Slicing Boolean Indexing Fancy Indexing Transposing

Module 2:

Summarizing and Computing Descriptive Statistics Correlation and Covariance Unique Values, Value Counts, and Membership 

End to End Machine Learning Project  
Working with Real Data 
Look at the Big Picture 
Frame the Problem 
Select a Performance Measure 
Check the Assumptions 
Get the Data 
Create the Workspace 
Download the Data 
Take a Quick Look at the Data Structure 
Create a Test Set 
Discover and Visualize the Data to Gain Insights 
Visualizing Geographical Data 
Looking for Correlations 
Experimenting with Attribute Combinations 
Prepare the Data for Machine Learning Algorithms 
Data Cleaning 
Handling Text and Categorical Attributes 
Custom Transformers 
Feature Scaling 

Module 4:

Page 4/9

Page 3/4

Transformation Pipelines Select and Train a Model Training and Evaluating on the Training Set Better Evaluation Using Cross-Validation Fine-Tune Your Model Grid Search Randomized Search Ensemble Methods Analyze the Best Models and Their Errors Evaluate Your System on the Test Set Launch, Monitor, and Maintain Your System 

Page 5/9

Classification       
MNIST 
Training a Binary Classifier 
Performance Measures 
Measuring Accuracy Using Cross-Validation 
Confusion Matrix
Precision and Recall 
Precision/Recall Tradeoff 
The ROC Curve 
Multiclass Classification 
Error Analysis 
Multilabel Classification 
Multioutput Classification 

Module 5:

Training Models 
Linear Regression 

 

Module 6:

Support Vector Machines  
Linear SVM Classification 
Soft Margin Classification 
Nonlinear SVM Classification 
Polynomial Kernel 
Adding Similarity Features 


 

Page 6/9

The Normal Equation 
Computational Complexity 
Gradient Descent 
Batch Gradient Descent 
Stochastic Gradient Descent 
Mini-batch Gradient Descent 
Polynomial Regression 
Learning Curves 
Regularized Linear Models 
Ridge Regression 
Lasso Regression 
Elastic Net 
Early Stopping 
Logistic Regression 
Estimating Probabilities 
Training and Cost Function 
Decision Boundaries 
Softmax Regression 

Module 7:

Page 7/9

Module 9:

Decision Tree 
Training and visualizing a decision tree

Making predictions 
Estimating class probabilities 

The CART (Classification and Regression Tree) training algorithm 
Computational Complexity 
Gini Impurity or Entropy? 
Regularization hyper parameters 
Regression 
Instability  

Module 8:

Gaussian RBF Kernel Computational Complexity SVM Regression Under the Hood Decision Function and Predictions Training Objective Quadratic Programming The Dual Problem, Generalized Lagrangian for the Hard margin problem and KKT multiplier Kernelized SVM and Mercer’s Theorem online SVMs and Hinge loss and how is it used in SGD classification 

Ensemble Learning and Random Forest 
Voting Classifiers 
Bagging and Pasting 

Random Patches and Random subspaces 

Random Forests 
 

Dimensionality Reduction 
The Curse of Dimensionality 
Main Approaches for Dimensionality Reduction 
Projection 
Manifold Learning 
PCA 
Preserving the Variance 
Principal Components 
Projecting Down to d Dimensions 
Using Scikit-Learn 
Explained Variance Ratio 
Choosing the Right Number of Dimensions 
PCA for Compression 
Randomized PCA 
Incremental PCA 

Kernel PCA Selecting a Kernel and Tuning Hyperparameters LLE

Other Dimensionality Reduction Techniques 
 

Module 10:

Page 8/9

Boosting 
Stacking 

Page 9/9

Dimensionality Reduction 
The Curse of Dimensionality 
Main Approaches for Dimensionality Reduction 
Projection 
Manifold Learning 
PCA 
Preserving the Variance 
Principal Components 
Projecting Down to d Dimensions 
Using Scikit-Learn 
Explained Variance Ratio 
Choosing the Right Number of Dimensions 
PCA for Compression 
Randomized PCA 
Incremental PCA 

 

Module 11:

Project work and documentation 

Module 12:

Thank You

Logo-Red_Hat-Advanced_Bus_Partner-Traini
  • LinkedIn Social Icon
  • Facebook Social Icon
  • Instagram Social Icon

Copyrights © 2019 | Designed by OSELabs.