Are you trying to master Machine Learning in Python, but tired of wasting your time on courses that don't move you towards your goal? Do you recognize the enormous value of text-based data, but don't know how to apply the right Machine Learning and Natural Language Processing techniques to extract that value?

In this Data School course, you'll gain hands-on experience using Machine Learning and Natural Language Processing to solve text-based data science problems. By the end of the course, you'll be able to confidently apply these techniques to your own data science problems.

Most data science courses suffer from a host of problems: They're poorly taught, lack the necessary depth, and include unexplained or broken code. They don't teach you how to apply what you're learning, and when you do apply it, there's no way to know how well you're doing.

But in this course, we'll go deep into Machine Learning with text, focusing on application from day one. We'll spend most of our time writing Python code, and you'll understand how every single line relates to the problem we're solving. You'll practice what you're learning through carefully crafted lessons and assignments.

At the end of this course, you'll leave with valuable Machine Learning experience, high-quality code that you can reuse to solve future text-based problems, and a wealth of curated resources to help you deepen your understanding of each course topic.

In this self-paced course, you'll learn how to build effective Machine Learning models using text-based data to solve your own data science problems. The course includes:

• 14 hours of high-quality instructional videos

• Well-commented lesson notebooks in Jupyter format (also available as Python scripts)

• Substantial homework assignments (with provided solutions) to help you practice everything you're learning

• A list of readings and videos to help prepare you for each class

• Links to 100+ carefully selected resources to deepen your understanding of course topics

• Lifetime access to all course materials

• Money-back guarantee (within 30 days of purchase)

Each module includes 2 to 4 hours of instructional videos, 1 lesson notebook, 1 to 2 homework assignments, and 15 to 20 supplementary resources.

Module 1: Working with Text Data in scikit-learn

By the end of this module, you'll be able to confidently perform the basic workflow for Machine Learning with text: creating a dataset, extracting features from unstructured text, building and evaluating models, and inspecting models for further insight. You'll also gain an understanding of Unicode, enabling you to troubleshoot encoding-based errors.

• Extracting features from unstructured text using CountVectorizer

• Building a MultinomialNB model for text classification

• Examining a model for further insight

• Model evaluation:

  • accuracy_score

  • confusion_matrix

  • roc_auc_score

• Comparing MultinomialNB with LogisticRegression

• Building a new dataset from individual text files using pandas

• Unicode basics

• Handling Unicode errors

Module 2: Applying Natural Language Processing Techniques to Machine Learning

By the end of this module, you'll be able to apply a handful of Natural Language Processing techniques to Machine Learning problems in order to improve the effectiveness of your models. You'll also learn how to perform sentiment analysis and build a simple document summarization tool for your own corpus of text.

• What is Natural Language Processing (NLP)?

• NLP terminology and examples

• Tuning CountVectorizer for better model performance:

  • n-grams

  • stop words

  • corpus-specific stop words

  • minimum document frequency

• Term Frequency-Inverse Document Frequency (TF-IDF) using TfidfVectorizer

• Text summarization

• Sentiment analysis using TextBlob

Module 3: Parsing Text Data Using Regular Expressions

By the end of this module, you'll be able to extract text features from messy data sources using regular expressions. You'll learn the basic rules and syntax that can be applied across programming languages, and you'll master the most important Python functions and options for working with regular expressions.

• Basic rules and principles

• Searching with re.search

• Metacharacters

• Greedy and lazy quantifiers

• Match groups

• Character classes

• Alternatives

• Substitution with re.sub

• Anchors

• Option flags

• Efficiently searching for multiple matches with re.findall

• Improving performance with re.compile

• Writing readable regular expressions with re.VERBOSE

Module 4: Workflow for a Text-Based Data Science Problem

By the end of this module, you'll be able to create an end-to-end workflow for solving a text-based data science problem using scikit-learn and pandas. You'll gain experience with data exploration, feature engineering, proper model evaluation, model tuning, and generating predictions for new observations.

• Data exploration and visualization

• Feature engineering using pandas

• Custom tokenization using regular expressions

• Multi-class classification

• Model evaluation:

  • train_test_split

  • cross_val_score

  • DummyClassifier

• Searching for optimal tuning parameters using GridSearchCV

• Chaining steps into a Pipeline

• Making predictions for out-of-sample data

Module 5: Advanced Machine Learning Techniques

By the end of this module, you'll be able to apply advanced Machine Learning techniques to improve the accuracy of your models and the efficiency of your workflow. You'll learn how to build and tune a multi-step, multi-layer Machine Learning pipeline, as well as how to ensemble and stack your models.

• Using a Pipeline for proper cross-validation

• Tuning a Pipeline with GridSearchCV

• Efficiently searching for tuning parameters using RandomizedSearchCV

• Stacking sparse and dense feature matrices using SciPy

• Combining the results of multiple feature extraction processes using FeatureUnion

• Building multi-level pipelines and feature unions

• Building custom transformers using FunctionTransformer

• Improving classifier performance through ensembling

• Unsupervised document clustering using cosine similarity

• Basic strategies for model stacking