Machine Learning with Python
Schedule
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Course Details
Machine Learning with Python
Duration: 5 Days
Course Description:
The Machine Learning with Python for Beginner training course will give you a detailed overview on developing machine learning using python covering the topics like regression, Naive Bayes, Clustering, tensor flow etc.
Course Objectives:
On completion of this training, you will know:
• Overview of Python Programming Language
• Regression
• K-Nearest Neighbors
• Naive Bayes
• Neural Networks
• Clustering
• Network Analysis
• Classification
• Deep Learning using Tensor Flow
Course Outlines:
Chapter 1 Introduction
1. Why Machine Learning?
2. Why Python?
3. scikit-learn
4. Essential Libraries and Tools
5. Python 2 Versus Python 3
6. Versions Used in this Book
7. A First Application: Classifying Iris Species
8. Summary and Outlook
Chapter 2 Supervised Learning
1. Classification and Regression
2. Generalization, Overfitting, and Underfitting
3. Supervised Machine Learning Algorithms
4. Uncertainty Estimates from Classifiers
5. Summary and Outlook
Chapter 3 Unsupervised Learning and Preprocessing
1. Types of Unsupervised Learning
2. Challenges in Unsupervised Learning
3. Preprocessing and Scaling
4. Dimensionality Reduction, Feature Extraction, and Manifold Learning
5. Clustering
6. Summary and Outlook
Chapter 4 Representing Data and Engineering Features
1. Categorical Variables
2. Binning, Discretization, Linear Models, and Trees
3. Interactions and Polynomials
4. Univariate Nonlinear Transformations
5. Automatic Feature Selection
6. Utilizing Expert Knowledge
7. Summary and Outlook
Chapter 5 Model Evaluation and Improvement
1. Cross-Validation
2. Grid Search
3. Evaluation Metrics and Scoring
4. Summary and Outlook
Chapter 6 Algorithm Chains and Pipelines
1. Parameter Selection with Preprocessing
2. Building Pipelines
3. Using Pipelines in Grid Searches
4. The General Pipeline Interface
5. Grid-Searching Preprocessing Steps and Model Parameters
6. Grid-Searching Which Model To Use
7. Summary and Outlook
Chapter 7 Working with Text Data
1. Types of Data Represented as Strings
2. Example Application: Sentiment Analysis of Movie Reviews
3. Representing Text Data as a Bag of Words
4. Stopwords
5. Rescaling the Data with tf–idf
6. Investigating Model Coefficients
7. Bag-of-Words with More Than One Word (n-Grams)
8. Advanced Tokenization, Stemming, and Lemmatization
9. Topic Modeling and Document Clustering
10.Summary and Outlook
Chapter 8 Wrapping Up
1. Approaching a Machine Learning Problem
2. From Prototype to Production
3. Testing Production Systems
4. Building Your Own Estimator
5. Where to Go from Here
6. Conclusion