Python for Data Science and Machine Learning Bootcamp
4.6 /5- Offered byUDEMY
Python for Data Science and Machine Learning Bootcamp at UDEMY Overview
Duration | 25 hours |
Total fee | ₹699 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Python for Data Science and Machine Learning Bootcamp at UDEMY Highlights
- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Course Instructor
- Jose Portilla
Python for Data Science and Machine Learning Bootcamp at UDEMY Course details
- This course is meant for people with at least some programming experience
- 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
- 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 PythonNumPy with PythonUsing pandas Data Frames to solve complex tasksUse pandas to handle Excel FilesWeb scraping with pythonConnect Python to SQLUse matplotlib and seaborn for data visualizationsUse plotly for interactive visualizationsMachine Learning with SciKit Learn, including:Linear RegressionK Nearest NeighborsK Means ClusteringDecision TreesRandom ForestsNatural Language ProcessingNeural Nets and Deep LearningSupport Vector Machinesand much, much more! Enroll in the course and become a data scientist today!
Python for Data Science and Machine Learning Bootcamp at UDEMY Curriculum
Course Introduction
Introduction to the Course
Course Help and Welcome
Course FAQs
Environment Set-Up
Python Environment Setup
Environment Set-up and Installation
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
Introduction to Machine Learning
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
Neural Network Theory
What is TensorFlow?
Installing Tensorflow 1.10
TensorFlow Basics
MNIST - Part One
MNIST - Part Two
Tensorflow Estimators
Deep Learning Project
Deep Learning Project - Solutions
Changes with TensorFlow
APPENDIX: OLD TENSORFLOW VIDEOS (Version 0.8)
TensorFlow Installation
MNIST with Multi-Layer Perceptron - Part 1
MNIST with Multi-Layer Perceptron - Part 2
MNIST with Multi-Layer Perceptron - Part 3
TensorFlow with ContribLearn
Tensorflow Project Exercise Overview
Tensorflow Project Exercise - Solutions
BONUS SECTION: THANK YOU!
Bonus Lecture
Other courses offered by UDEMY
Student Forum
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