Python for Data Science and Machine Learning Bootcamp
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!
4.61 (95796 reviews)
428133
Students25 hours
ContentMay 2020
Last UpdateWhat 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
Description
Are you ready to start your path to becoming a Data Scientist!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!
We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:
- Programming with Python
- NumPy with Python
- Using pandas Data Frames to solve complex tasks
- Use pandas to handle Excel Files
- Web scraping with python
- Connect Python to SQL
- Use matplotlib and seaborn for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with SciKit Learn, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Natural Language Processing
- Neural Nets and Deep Learning
- Support Vector Machines
- and much, much more!
Enroll in the course and become a data scientist today!
Screenshots
Content
Course Introduction
Introduction to the Course
Course Help and Welcome
Course FAQs
Environment Set-Up
Python Environment Setup
Jupyter Overview
Updates to Notebook Zip
Jupyter Notebooks
Optional: Virtual Environments
Python Crash Course
Welcome to the Python Crash Course Section!
Introduction to Python Crash Course
Python Crash Course - Part 1
Python Crash Course - Part 2
Python Crash Course - Part 3
Python Crash Course - Part 4
Python Crash Course Exercises - Overview
Python Crash Course Exercises - Solutions
Python for Data Analysis - NumPy
Welcome to the NumPy Section!
Introduction to Numpy
Numpy Arrays
Quick Note on Array Indexing
Numpy Array Indexing
Numpy Operations
Numpy Exercises Overview
Numpy Exercises Solutions
Python for Data Analysis - Pandas
Welcome to the Pandas Section!
Introduction to Pandas
Series
DataFrames - Part 1
DataFrames - Part 2
DataFrames - Part 3
Missing Data
Groupby
Merging Joining and Concatenating
Operations
Data Input and Output
Python for Data Analysis - Pandas Exercises
Note on SF Salary Exercise
SF Salaries Exercise Overview
SF Salaries Solutions
Ecommerce Purchases Exercise Overview
Ecommerce Purchases Exercise Solutions
Python for Data Visualization - Matplotlib
Welcome to the Data Visualization Section!
Introduction to Matplotlib
Matplotlib Part 1
Matplotlib Part 2
Matplotlib Part 3
Matplotlib Exercises Overview
Matplotlib Exercises - Solutions
Python for Data Visualization - Seaborn
Introduction to Seaborn
Distribution Plots
Categorical Plots
Matrix Plots
Grids
Regression Plots
Style and Color
Seaborn Exercise Overview
Seaborn Exercise Solutions
Python for Data Visualization - Pandas Built-in Data Visualization
Pandas Built-in Data Visualization
Pandas Data Visualization Exercise
Pandas Data Visualization Exercise- Solutions
Python for Data Visualization - Plotly and Cufflinks
Introduction to Plotly and Cufflinks
Plotly and Cufflinks
Python for Data Visualization - Geographical Plotting
Introduction to Geographical Plotting
Choropleth Maps - Part 1 - USA
Choropleth Maps - Part 2 - World
Choropleth Exercises
Choropleth Exercises - Solutions
Data Capstone Project
Welcome to the Data Capstone Projects!
911 Calls Project Overview
911 Calls Solutions - Part 1
911 Calls Solutions - Part 2
Bank Data
Finance Data Project Overview
Finance Project - Solutions Part 1
Finance Project - Solutions Part 2
Finance Project - Solutions Part 3
Introduction to Machine Learning
Welcome to the Machine Learning Section!
Link for ISLR
Supervised Learning Overview
Evaluating Performance - Classification Error Metrics
Evaluating Performance - Regression Error Metrics
Machine Learning with Python
Linear Regression
Linear Regression Theory
model_selection Updates for SciKit Learn 0.18
Linear Regression with Python - Part 1
Linear Regression with Python - Part 2
Linear Regression Project Overview
Linear Regression Project Solution
Cross Validation and Bias-Variance Trade-Off
Bias Variance Trade-Off
Logistic Regression
Logistic Regression Theory
Logistic Regression with Python - Part 1
Logistic Regression with Python - Part 2
Logistic Regression with Python - Part 3
Logistic Regression Project Overview
Logistic Regression Project Solutions
K Nearest Neighbors
KNN Theory
KNN with Python
KNN Project Overview
KNN Project Solutions
Decision Trees and Random Forests
Introduction to Tree Methods
Decision Trees and Random Forest with Python
Decision Trees and Random Forest Project Overview
Decision Trees and Random Forest Solutions Part 1
Decision Trees and Random Forest Solutions Part 2
Support Vector Machines
SVM Theory
Support Vector Machines with Python
SVM Project Overview
SVM Project Solutions
K Means Clustering
K Means Algorithm Theory
K Means with Python
K Means Project Overview
K Means Project Solutions
Principal Component Analysis
Principal Component Analysis
PCA with Python
Recommender Systems
Recommender Systems
Recommender Systems with Python - Part 1
Recommender Systems with Python - Part 2
Natural Language Processing
Natural Language Processing Theory
NLP with Python - Part 1
NLP with Python - Part 2
NLP with Python - Part 3
NLP Project Overview
NLP Project Solutions
Neural Nets and Deep Learning
Download TensorFlow Notebooks Here
Welcome to the Deep Learning Section!
Introduction to Artificial Neural Networks (ANN)
Installing Tensorflow
Perceptron Model
Neural Networks
Activation Functions
Multi-Class Classification Considerations
Cost Functions and Gradient Descent
Backpropagation
TensorFlow vs Keras
TF Syntax Basics - Part One - Preparing the Data
TF Syntax Basics - Part Two - Creating and Training the Model
TF Syntax Basics - Part Three - Model Evaluation
TF Regression Code Along - Exploratory Data Analysis
TF Regression Code Along - Exploratory Data Analysis - Continued
TF Regression Code Along - Data Preprocessing and Creating a Model
TF Regression Code Along - Model Evaluation and Predictions
TF Classification Code Along - EDA and Preprocessing
TF Classification - Dealing with Overfitting and Evaluation
TensorFlow 2.0 Project Options Overview
TensorFlow 2.0 Project Notebook Overview
Keras Project Solutions - Dealing with Missing Data
Keras Project Solutions - Dealing with Missing Data - Part Two
Keras Project Solutions - Categorical Data
Keras Project Solutions - Data PreProcessing
Keras Project Solutions - Data PreProcessing
Keras Project Solutions - Creating and Training a Model
Keras Project Solutions - Model Evaluation
Tensorboard
Big Data and Spark with Python
Welcome to the Big Data Section!
Big Data Overview
Spark Overview
Local Spark Set-Up
AWS Account Set-Up
Quick Note on AWS Security
EC2 Instance Set-Up
SSH with Mac or Linux
PySpark Setup
Lambda Expressions Review
Introduction to Spark and Python
RDD Transformations and Actions
BONUS SECTION: THANK YOU!
Bonus Lecture
Reviews
An informative, comprehensive and concise explanation of Python, albeit a bit rush for complete beginners.
The course is fairly good. It explains the basic theory of data science and gives you basic understanding of this world, and the technologies in it. I feel that Jose is one of the only people that is teaching these subjects in a fairly fast way that is understandable. I enjoyed the course and I feel that it educated me. The only thing that I would improve is to add a few lectures about some common topics that the course didn't address like how to pick a machine learning algorithm, how to choose the variables for the models and how to explore the data. I mean, all of this was mentioned in few words throughout the course but I feel that these topic should have a bit more emphasis and some explanations around, at least some rules of thumb or general thinking direction and not just 'I always like to do this check when I start exploring the data' Overall a good course and I feel that it was worth the money.
THIS COURSE IS AMAZING. I AM TAKING IT TO SUPPLEMENT MY COLLEGE INTRO TO DATA SCIENCE CLASS AND IT GOES OVER EVERYTHING IN MUCH BETTER DETAIL AND EXPLANATION. I FEEL READY TO GET A INTERNSHIP IN DATA SCIENCE NOW. THANK YOU!!!!!!!
I was almost a beginner in Python, however the gradual approach adopted for the topics presentation permitted me to properly learn some fundamental tools of Python. The approach to machine learning section is gradual as well, providing the necessary theoretichal tool to study and go deep into the math beyond the algorithm and the lybraries explained. However, the course mantains an application-focused and practical approch, consenting to immediately understand how to apply the presented tools. The final project at each session allowing to immediatly test yourself, also proiding the opportunity to build a small personal portfolio. It definitly worth the price I paid, for both the quality of the contents and the supporting service. Definitely reccommended to beginner and intermediate Python users who want to learn Machine Learning.
I just want to say thanks to Jose for designing this course and for everything that I have learnt so far through his other course on Python.
This was an exceptional course, perfect for anyone with basic python skills who wants to build a Data Science foundation. Very well-organized and accessible course covering core python data structures, visualization libraries, and machine learning tools and concepts.
Excellent course for beginners in ML. The course is well structured, explanations are clear, exercises are not so challenging but that's enough to apply the concepts exposed during the lectures. I'd also recommend reading the extra documentation : the ISLR book as suggested by Jose. That gives complementary information regarding the statiscal methods. Thanks!
The video instruction is very useful to learn about the Python environment. The instructor speaks a little fast, but that is a minor issue; I can watch the video again.
Didn't expect this course to be laid out so nicely. Everything so far has been planned out and instructions are beyond expectation! A+ job!
Nice introduction, clear explanations, great examples, go over a significant material at a comfortable pace and goads you into making specific forays into frameworks documentation etc. Quite well organized material
This course should be renamed 'python tools for data science'. Haven't learnted much data science but been told real good like how to pretty up a basic line plot. Kind of seems like the dude just looked at necessary python libaries for data science, youtube'd them shits and bundled them up. I'll use this course for a reference when I need to pretty up a line. Good on him for making a dime. Over 300k users, dude should start some Udemy infomercials on how to teach shit you don't know much about and make 3 million dollars doing it.
The basics are taken care of pretty quickly. That's a good thing about it. Being a experienced developer, I can just shoot through these very quickly and concentrate on the actual meaty parts.
Awesome course, recommended to everyone and well structured. Jose Portilla thanks and congratulations for such a great job.
Always happy to learn from Jose, I am actually learning more than what I learned in my master's. I will use all this new knowledge to pursue a new job position.
felt a bit confident while practicing it side ways on colab or jupiter notebook,nd well the respected sir is explaining quite effectively and smoothly
Coupons
| Status | Date | Discount | ||
|---|---|---|---|---|
| Expired | 6/28/2019 | 97% OFF | ||
903744
Udemy ID7/13/2016
Course created date5/14/2019
Course Indexed date
