Natural Language Processing and Machine Learning for Developers

• Cleaning Data with Regular Expressions
• Handling Contracted Words
• Python Code Samples of BoW
• One-Hot Encoding Examples
• Sklearn and Word Embedding Examples
• What is BeautifulSoup?
• Web Scraping with Pure Regular Expressions
• What is Scrapy?
• What is SpaCy?
• SpaCy and Stop Words
• SpaCy and Tokenization
• SpaCy and Lemmatization
• SpaCy and NER
• SpaCy Pipelines
• SpaCy and Word Vectors
• The scispaCy Library (Optional)
• Summary

4 Laboratorio en vivo in this lesson — see the labs panel →

• What is NLTK?
• NLTK and BoW
• NLTK and Stemmers
• NLTK and Lemmatization
• NLTK and Stop Words
• What is Wordnet?
• NLTK, lxml, and XPath
• NLTK and n-grams
• NLTK and POS (1)
• NLTK and POS (2)
• NLTK and Tokenizers
• NLTK and Context-Free Grammars (Optional)
• What is Gensim?
• An Example of Topic Modeling
• A Brief Comparison of Popular Python-Based NLP Libraries
• Miscellaneous Libraries
• Summary

2 Laboratorio en vivo in this lesson — see the labs panel →

• What is Machine Learning?
• Types of Machine Learning Algorithms
• Preparing a Dataset and Training a Model
• Feature Engineering, Selection, and Extraction
• Working with Datasets
• Overfitting versus Underfitting
• Data Normalization Techniques
• Metrics in Machine Learning
• What is Linear Regression?
• Other Types of Regression
• Scatter Plots with NumPy and Matplotlib (1)
• Scatter Plots with NumPy and Matplotlib (2)
• A Quadratic Scatterplot with NumPy and Matplotlib
• The Mean Squared Error (MSE) Formula
• Calculating the MSE Manually
• Approximating Linear Data with np.linspace()
• What are Ensemble Methods?
• Four Types of Ensemble Methods
• Common Boosting Algorithms
• Hyperparameter Optimization
• AutoML, AutoML-Zero, and AutoNLP
• Miscellaneous Topics
• Summary

3 Laboratorio en vivo in this lesson — see the labs panel →

• What is Classification?
• What are Linear Classifiers?
• What is kNN?
• What are Decision Trees?
• Decision Tree Code Samples
• Decision Trees, Gini Impurity, and Entropy
• What are Random Forests?
• What are Support Vector Machines?
• What is a Bayesian Classifier?
• Training Classifiers
• Evaluating Classifiers
• Trade-offs for ML Algorithms
• What are Activation Functions?
• Common Activation Functions
• The ReLU and ELU Activation Functions
• Sigmoid, Softmax, and Hardmax Similarities
• Sigmoid, Softmax, and HardMax Differences
• Hyperparameters for Neural Networks
• What is Logistic Regression?
• Keras, Logistic Regression, and Iris Dataset
• Sklearn and Linear Regression
• SciPy and Linear Regression
• Keras and Linear Regression
• Summary

1 Laboratorio en vivo in this lesson — see the labs panel →

• What is Text Summarization?
• Text Summarization with gensim and SpaCy
• What are Recommender Systems?
• Content-Based Recommendation Systems
• Collaborative Filtering Algorithm
• Recommender Systems and Reinforcement Learning (Optional)
• What is Sentiment Analysis?
• Sentiment Analysis with Naïve Bayes
• Sentiment Analysis in NLTK and VADER
• Sentiment Analysis with Textblob
• Sentiment Analysis with Flair
• Detecting Spam
• Logistic Regression and Sentiment Analysis
• Working with COVID-19
• What are Chatbots?
• Summary

3 Laboratorio en vivo in this lesson — see the labs panel →

• Term-Document Matrix
• Text Classification Algorithms in Machine Learning
• A Keras-Based Tokenizer
• TF2 and Tokenization
• TF2 and Encoding
• A Keras-Based Word Embedding
• An Example of BoW with TF2
• The 20newsgroup Dataset
• Text Classification with the kNN Algorithm
• Text Classification with a Decision Tree Algorithm
• Text Classification with a Random Forest Algorithm
• Text Classification with the SVC Algorithm
• Text Classification with the Naïve Bayes Algorithm
• Text Classification with the kMeans Algorithm
• TF2/Keras and Word Tokenization
• TF2/Keras and Word Encodings
• Text Summarization with TF2/Keras and Reuters Dataset
• Summary

4 Laboratorio en vivo in this lesson — see the labs panel →

• What is Attention?
• An Overview of the Transformer Architecture
• What is T5?
• What is BERT?
• The Inner Workings of BERT
• Subword Tokenization
• Sentence Similarity in BERT
• Generating BERT Tokens (1)
• Generating BERT Tokens (2)
• The BERT Family
• Introduction to GPT
• Working with GPT-2
• What is GPT-3?
• The Switch Transformer: One Trillion Parameters
• Looking Ahead
• Summary

5 Laboratorio en vivo in this lesson — see the labs panel →

• What are Datasets?
• Preparing Datasets
• Missing Data, Anomalies, and Outliers
• What is Imbalanced Classification?
• What is SMOTE?
• Analyzing Classifiers
• What is a Probability?
• Random Variables
• Fundamental Concepts in Statistics
• The Moments of a Function (Optional)
• Data and Statistics
• The Bias-Variance Trade-off
• Gini Impurity, Entropy, and Perplexity
• Cross-Entropy and KL Divergence
• Covariance and Correlation Matrices
• Principal Component Analysis (PCA)
• Dimensionality Reduction
• Dimensionality Reduction Techniques
• Linear Versus Nonlinear Reduction Techniques
• Types of Distance Metrics
• Other Well-Known Distance Metrics
• What is Sklearn?
• What is Bayesian Inference?
• What are Vector Spaces?
• Summary

• Tools for Python
• Python Installation
• Setting the PATH Environment Variable (Windows Only)
• Launching Python on Your Machine
• Python Identifiers
• Lines, Indentation, and Multilines
• Quotation and Comments in Python
• Saving Your Code in a Module
• Some Standard Modules in Python
• The help() and dir() Functions
• Compile Time and Runtime Code Checking
• Simple Data Types in Python
• Working with Numbers
• Working with Fractions
• Unicode and UTF-8
• Working with Unicode
• Working with Strings
• Uninitialized Variables and the Value None in Python
• Slicing and Splicing Strings
• Search and Replace a String in Other Strings
• Remove Leading and Trailing Characters
• Printing Text without NewLine Characters
• Text Alignment
• Working with Dates
• Exception Handling in Python
• Handling User Input
• Python and Emojis (Optional)
• Command-Line Arguments
• Summary

• What are Regular Expressions?
• Metacharacters in Python
• Character Sets in Python
• Character Classes in Python
• Matching Character Classes with the re Module
• Using the re.match() Method
• Options for the re.match() Method
• Matching Character Classes with the re.search() Method
• Matching Character Classes with the findAll() Method
• Additional Matching Function for Regular Expressions
• Grouping with Character Classes in Regular Expressions
• Using Character Classes in Regular Expressions
• Modifying Text Strings with the re Module
• Splitting Text Strings with the re.split() Method
• Splitting Text Strings Using Digits and Delimiters
• Substituting Text Strings with the re.sub() Method
• Matching the Beginning and the End of Text Strings
• Compilation Flags
• Compound Regular Expressions
• Counting Character Types in a String
• Regular Expressions and Grouping
• Simple String Matches
• Additional Topics for Regular Expressions
• Summary
• Exercises

• What is Keras?
• Creating a Keras-Based Model
• Keras and Linear Regression
• Keras, MLPs, and MNIST
• Keras, CNNs, and cifar10
• Resizing Images in Keras
• Keras and Early Stopping (1)
• Keras and Early Stopping (2)
• Keras and Metrics
• Saving and Restoring Keras Models
• Summary

• What is TF 2?
• Other TF 2-Based Toolkits
• TF 2 Eager Execution
• TF 2 Tensors, Data Types, and Primitive Types
• Constants in TF 2
• Variables in TF 2
• The tf.rank() API
• The tf.shape() API
• Variables in TF 2 (Revisited)
• What is @tf.function in TF 2?
• Working with @tf.function in TF 2
• Arithmetic Operations in TF 2
• Caveats for Arithmetic Operations in TF 2
• TF 2 and Built-In Functions
• Calculating Trigonometric Values in TF 2
• Calculating Exponential Values in TF 2
• Working with Strings in TF 2
• Working with Tensors and Operations in TF 2
• Second-Order Tensors in TF 2 (1)
• Second-Order Tensors in TF 2 (2)
• Multiplying Two Second-Order Tensors in TF
• Convert Python Arrays to TF Tensors
• Differentiation and tf.GradientTape in TF 2
• Examples of tf.GradientTape
• What is Trax?
• Google Colaboratory
• Other Cloud Platforms
• TF2 and tf.data.Dataset
• The TF 2 tf.data.Dataset
• What are Lambda Expressions?
• Working with Generators in TF 2
• Summary

• What is Data Visualization?
• What is Matplotlib?
• Horizontal Lines in Matplotlib
• Slanted Lines in Matplotlib
• Parallel Slanted Lines in Matplotlib
• A Grid of Points in Matplotlib
• A Dotted Grid in Matplotlib
• Lines in a Grid in Matplotlib
• A Colored Grid in Matplotlib
• A Colored Square in an Unlabeled Grid in Matplotlib
• Randomized Data Points in Matplotlib
• A Histogram in Matplotlib
• A Set of Line Segments in Matplotlib
• Plotting Multiple Lines in Matplotlib
• Trigonometric Functions in Matplotlib
• Display IQ Scores in Matplotlib
• Plot a Best-Fitting Line in Matplotlib
• Introduction to Sklearn (scikit-learn)
• The Digits Dataset in Sklearn
• The Iris Dataset in Sklearn
• The Iris Dataset in Sklearn (Optional)
• The faces Dataset in Sklearn (Optional)
• Working with Seaborn
• Seaborn Built-in Datasets
• The Iris Dataset in Seaborn
• The Titanic Dataset in Seaborn
• Extracting Data from the Titanic Dataset in Seaborn (1)
• Extracting Data from the Titanic Dataset in Seaborn (2)
• Visualizing a Pandas Dataset in Seaborn
• Data Visualization in Pandas
• Summary

• AI vs Machine Learning vs Deep Learning – Explained Simply with Examples