Python for Data Science and Machine Learning Bootcamp

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

  • Jose Portilla
  • 4.62
  • (123237 reviews)
  • 25 hrs
  • 165 lectures
  • Udemy
Python for Data Science and Machine Learning Bootcamp

What you will learn?

  • Use Python for Data Science and Machine Learning
  • Use Spark for Big Data Analysis
  • Implement Machine Learning Algorithms
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Learn to use Matplotlib for Python Plotting
  • Learn to use Seaborn for statistical plots
  • Use Plotly for interactive dynamic visualizations
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest and Decision Trees
  • Natural Language Processing and Spam Filters
  • Neural Networks
  • Support Vector Machines

Your trainer

Jose Portilla

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

165 lessons

Easy to follow lectures and videos covering subject details.

25 hours

This course includes hours of video material. Watch on-demand, anytime, anywhere.

Certificate of Completion

You will earn a Certificate of Completion at the end of this course.

Course curriculum

Course Introduction07:11

  • Introduction to the Course03:33
  • Course Help and Welcome00:36
  • Course FAQs03:02

  • Python Environment Setup11:14

  • Updates to Notebook Zip00:09
  • Jupyter Notebooks13:48
  • Optional: Virtual Environments09:51

  • Welcome to the Python Crash Course Section!00:17
  • Introduction to Python Crash Course01:26
  • Python Crash Course - Part 119:29
  • Python Crash Course - Part 215:14
  • Python Crash Course - Part 316:39
  • Python Crash Course - Part 415:37
  • Python Crash Course Exercises - Overview03:35
  • Python Crash Course Exercises - Solutions11:56

  • Welcome to the NumPy Section!00:10
  • Introduction to Numpy02:12
  • Numpy Arrays16:49
  • Quick Note on Array Indexing00:48
  • Numpy Array Indexing18:23
  • Numpy Operations07:04
  • Numpy Exercises Overview02:46
  • Numpy Exercises Solutions15:31

  • Welcome to the Pandas Section!00:14
  • Introduction to Pandas01:44
  • Series10:39
  • DataFrames - Part 115:31
  • DataFrames - Part 217:10
  • DataFrames - Part 309:12
  • Missing Data06:19
  • Groupby06:48
  • Merging Joining and Concatenating08:55
  • Operations12:04
  • Data Input and Output14:00

  • Note on SF Salary Exercise00:22
  • SF Salaries Exercise Overview01:55
  • SF Salaries Solutions15:25
  • Ecommerce Purchases Exercise Overview02:11
  • Ecommerce Purchases Exercise Solutions15:12

  • Welcome to the Data Visualization Section!00:22
  • Introduction to Matplotlib03:02
  • Matplotlib Part 116:57
  • Matplotlib Part 215:51
  • Matplotlib Part 311:51
  • Matplotlib Exercises Overview01:46
  • Matplotlib Exercises - Solutions10:19

  • Introduction to Seaborn02:58
  • Distribution Plots18:20
  • Categorical Plots17:17
  • Matrix Plots10:14
  • Grids08:30
  • Regression Plots07:13
  • Style and Color08:21
  • Seaborn Exercise Overview01:53
  • Seaborn Exercise Solutions07:08

  • Pandas Built-in Data Visualization13:27
  • Pandas Data Visualization Exercise01:22
  • Pandas Data Visualization Exercise- Solutions08:55

  • Introduction to Plotly and Cufflinks03:22
  • READ ME FIRST BEFORE PLOTLY PLEASE!00:53
  • Plotly and Cufflinks18:38

  • Introduction to Geographical Plotting00:58
  • Choropleth Maps - Part 1 - USA19:26
  • Choropleth Maps - Part 2 - World06:53
  • Choropleth Exercises03:11
  • Choropleth Exercises - Solutions10:01

  • Welcome to the Data Capstone Projects!00:17
  • 911 Calls Project Overview02:07
  • 911 Calls Solutions - Part 114:29
  • 911 Calls Solutions - Part 217:37
  • Bank Data00:11
  • Finance Data Project Overview03:06
  • Finance Project - Solutions Part 116:13
  • Finance Project - Solutions Part 218:11
  • Finance Project - Solutions Part 306:23

  • Welcome to Machine Learning. Here are a few resources to get you started!00:21
  • Welcome to the Machine Learning Section!00:31
  • Supervised Learning Overview08:21
  • Evaluating Performance - Classification Error Metrics16:37
  • Evaluating Performance - Regression Error Metrics05:36
  • Machine Learning with Python09:27

  • Linear Regression Theory04:33
  • model_selection Updates for SciKit Learn 0.1800:26
  • Linear Regression with Python - Part 118:16
  • Linear Regression with Python - Part 207:05
  • Linear Regression Project Overview02:31
  • Linear Regression Project Solution18:43

  • Bias Variance Trade-Off06:25

  • Logistic Regression Theory11:53
  • Logistic Regression with Python - Part 117:43
  • Logistic Regression with Python - Part 216:57
  • Logistic Regression with Python - Part 308:15
  • Logistic Regression Project Overview01:36
  • Logistic Regression Project Solutions11:05

  • KNN Theory05:38
  • KNN with Python19:39
  • KNN Project Overview01:11
  • KNN Project Solutions14:14

  • Introduction to Tree Methods06:52
  • Decision Trees and Random Forest with Python13:57
  • Decision Trees and Random Forest Project Overview03:10
  • Decision Trees and Random Forest Solutions Part 112:13
  • Decision Trees and Random Forest Solutions Part 208:46

  • SVM Theory04:36
  • Support Vector Machines with Python17:52
  • SVM Project Overview02:21
  • SVM Project Solutions10:09

  • K Means Algorithm Theory05:15
  • K Means with Python12:35
  • K Means Project Overview02:53
  • K Means Project Solutions16:38

  • Principal Component Analysis03:26
  • PCA with Python16:58

  • Recommender Systems04:13
  • Recommender Systems with Python - Part 113:36
  • Recommender Systems with Python - Part 213:21

  • Natural Language Processing Theory05:06
  • NLP with Python - Part 116:02
  • NLP with Python - Part 218:46
  • NLP with Python - Part 317:30
  • NLP Project Overview02:04
  • NLP Project Solutions19:26

  • Download TensorFlow Notebooks Here00:02
  • Quick Check for Notes1 question
  • Welcome to the Deep Learning Section!00:21
  • Introduction to Artificial Neural Networks (ANN)02:15
  • Installing Tensorflow00:06
  • Perceptron Model10:39
  • Neural Networks07:19
  • Activation Functions10:39
  • Multi-Class Classification Considerations10:34
  • Cost Functions and Gradient Descent18:13
  • Backpropagation14:47
  • TensorFlow vs Keras02:13
  • TF Syntax Basics - Part One - Preparing the Data10:49
  • TF Syntax Basics - Part Two - Creating and Training the Model13:59
  • TF Syntax Basics - Part Three - Model Evaluation12:56
  • TF Regression Code Along - Exploratory Data Analysis18:50
  • TF Regression Code Along - Exploratory Data Analysis - Continued13:15
  • TF Regression Code Along - Data Preprocessing and Creating a Model08:42
  • TF Regression Code Along - Model Evaluation and Predictions11:23
  • TF Classification Code Along - EDA and Preprocessing08:05
  • TF Classification - Dealing with Overfitting and Evaluation16:50
  • TensorFlow 2.0 Project Options Overview01:40
  • TensorFlow 2.0 Project Notebook Overview07:41
  • Keras Project Solutions - Dealing with Missing Data20:35
  • Keras Project Solutions - Dealing with Missing Data - Part Two14:46
  • Keras Project Solutions - Categorical Data12:02
  • Keras Project Solutions - Data PreProcessing17:23
  • Keras Project Solutions - Data PreProcessing03:45
  • Keras Project Solutions - Creating and Training a Model03:57
  • Keras Project Solutions - Model Evaluation09:42
  • Tensorboard18:22

  • Welcome to the Big Data Section!00:23
  • Big Data Overview05:31
  • Spark Overview08:59
  • Local Spark Set-Up00:59
  • AWS Account Set-Up04:13
  • Quick Note on AWS Security00:16
  • EC2 Instance Set-Up16:18
  • SSH with Mac or Linux04:49
  • PySpark Setup23:48
  • Lambda Expressions Review05:26
  • Introduction to Spark and Python08:16
  • RDD Transformations and Actions23:08

  • Bonus Lecture00:10
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