Data Science and Machine Learning Bootcamp

Join the world's most comprehensive and practical data science course to learn data science, machine learning, deep learning, and artificial intelligence (AI)!

Interested in this course? Email us at amer@astrolabs.com

Course curriculum

1. Welcome!
2. 👤 AstroLabs Chat
3. Zoom Link
4. Discussion Boards (Support)
1. Introduction to using data in 2021
2. Why choose Python (differences between Python and R)
3. Installing Visual Studio Code
4. Python for Data Engineering
5. How Machine Learning & Deep Learning Work
6. Understanding Predictive Analytics and Prescriptive Analytics
7. Overview of Scraping Data and Creating Data
1. Definition of Algorithms Definition of Data Structures
2. Blocks, Scope & Conditionals
3. Variables and Data Types
4. Dictionaries
5. Algorithm Practice
6. Basics of Data Science to Solve Problems
1. Tools for Data Science, Applications for Data Analysis and Languages for Data Ccience
2. Selecting Optimal Data Graphics, Communicating with Color and C Analyses for Data Science
3. Clustering, Classifying and Anomaly Detection
4. Data Preparation Basics Filtering and Selecting
5. Grouping and Aggregation & Conditional Statements
1. The Data Collection Process
2. Practical Data Science Applications: Getting Data in Using Web Scraping
3. Mocking Inputs / Pagination - Search and Filters
4. Saving, Reading / Writing to a File
5. Data Sourcing via Web Scraping
6. Data Parsing
7. Cleaning and Stemming Textual Data
8. Project- Scraping Yellow Pages
1. Descriptive Analyses and Predictive Models
2. Anomaly Detection, Dimensionality Reduction, Feature Selection and Creation
3. Mathematics for Data Science: Algebra & Calculus
4. Acting on Data Science, Interpretability Actionable Insights
5. Collaborative Analytics with Plotly
6. Opening Files Text vs. Binary Mode
7. Practical Data Visualization
1. Data Engineering Introduction
2. Tools in Big Data
3. Scientific Python Overview
4. Overview of Jupyter Notebooks & Overview of DataBricks
5. Understand Markdown Cells, Edit Notebooks
6. Slicing Learn Boolean Indexing
7. Pandas Overview, Load CSV Files, Parse Time
1. Main Components of Machine Learning, Prediction (with Linear Regression)
2. Classification (with Support Vector Machine)
3. Fundamental Concepts of Machine Learning
4. Supervised Learning & Unsupervised Learning
5. Top-N Hit Rate: Many Ways Coverage, Diversity, and Novelty
6. Deep Learning Introduction & Deep Learning Prerequisites
1. Using DataBricks
2. Coverage, Diversity, and Novelty
3. Content-based Recommendations and the Cosine Similarity Metric
4. Tuning Collaborative Filtering Algorithms
5. Playing with TensorFlow
6. Tuning Restricted Boltzmann Machines
7. Introduction and Installation of Apache Spark
8. AWS SageMaker and Factorization Machines
1. Statistics and Probability Theory
2. Linear Algebra
3. Calculus and Geometry
4. Discrete Mathematics
1. Brief

About this course

63 lessons

Data Science and Machine Learning Bootcamp

Course curriculum

Introduction

Module 1 - Introduction to Data Science

Module 2 - Python Syntax

Module 3 - Data Structures

Module 4 - Data Collection & Web Scraping

Module 5 - Analysis & Visualization

Module 6 - Data Engineering

Module 7 - Machine Learning

Module 8 - Advanced Machine Learning

Module 9 - Advanced Mathematics for Data Science

Final Project

About this course