DS BootCamp

The course is a taught by the lead instructor at the App Brewery, London's leading in-person programming bootcamp.

500+ Students Enrolled
4.4 Rating (325) Ratings

What You'll Learn

  • Learn Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Learn Perform linear and logistic regressions in Python
  • Learn Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation.
  • Learn Understand the mathematics behind Machine Learning.
  • Learn Start coding in Python and learn how to use it for statistical analysis
  • Learn Carry out cluster and factor analysis.

Course Content

  • Python
  • Data wrangling and EDA (Exploratory Data Analysis) with Python, pandas, and Matplotlib
  • Git and GitHub workflow: branching and pull requests
  • Intro to Hierarchical Clustering
  • More on Hierarchical Clustering
  • Logistic regression vs Linear regression
  • Logistic Regression Training
  • DBSCAN
  • In-depth introduction to linear regression theory and application
  • Machine learning concepts: over-fitting, train/test splits and cross-validation
  • Regression & model evaluation in statsmodels and SciKit-learn
  • Web scraping with Beautiful Soup andSelenium
  • Object oriented programming principles
  • Introduction to time-series modeling
  • Statistics review
  • Hypothesis testing
  • Introduction to Bayes’ Theorem
  • Linear regression regularization (LASSO, Ridge, elastic net)
  • Classification and regression algorithms: K-nearest neighbors, logistic regression, support vector machines (SVM), Naive Bayes
  • Relational databases and writing code to query them (SQL)
  • Machine learning concepts: bias-variance tradeoff, classification errors, class imbalance
  • Other tools: creating and provisioning cloud servers
  • More supervised learning algorithms: Classification and regression trees, Random Forest
  • Relational databases and writing code to query them (SQL)
  • Interactive data visualization using Business Intelligence tools
  • Web development essentials including Javascript, HTML, and CSS
  • Deploying models in production and full stack in a nutshell: connecting a front end and a back- end with Python’s Flask package
  • Data & databases: RESTful APIs, NoSQL databases, MongoDB, pymongo
  • Generalized Linear Models and maximum likelihood estimation
  • Unsupervised learning algorithms for clustering, including k-means
  • Web development essentials including Javascript, HTML, and CSS
  • Natural Language Processing: textblob, NLTK, chunking, stemming, POS tagging, tf-idf
  • Data & databases: RESTful APIs, NoSQL databases, MongoDB, pymongo
  • Generalized Linear Models and maximum likelihood estimation
  • Unsupervised learning algorithms for clustering, including k-means
  • Web development essentials including Javascript, HTML, and CSS
  • Natural Language Processing: textblob, NLTK, chunking, stemming, POS tagging, tf-idf
  • Design and interpret the results of experiments, including A/B testing
  • Distributed databases, including Dask, Hadoop and HiveQL
  • Tools for distributed machine learning, including PySpark
  • Deep Learning: Convolutional Neural Networks for images and Recurrent
  • Neural Networks for NLP and time-series modeling
  • Frameworks for evaluating the ethics of data science projects
Web Development

  This Course includes

  Tutorials

  Articles, Videos

  Full Time Lectures

  Real Scenarios

  Project Work

  Certification of Completion

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