Python for Data Science and Machine Learning Bootcamp

Home
Courses
Python for Data Science and Machine Learning Bootcamp

Software Development / Data Science

Python for Data Science and Machine Learning Bootcamp

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

0.00 0 Review(s)

0 Students Enrolled

Intermediate

English

Zoila Lemke

Python for Data Science and Machine Learning Bootcamp

This Course Includes:

18h 07m
166 Lectures
294 Downloadable Assets
Full Lifetime Access
Access On Mobile And Tv
Certificate On Completion

At Just Price Of $824.00

Enroll Now

Favorite

Overview

What will students learn in your course?

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

What are the requirements or prerequisites for taking your course?

Some programming experience
Admin permissions to download files

Who is the course for?

This course is meant for people with at least some programming experience

Description

Course Tags

Development Data Science Machine Learning

Course Content

27 Sections
166 Lectures
18h 07m Total Length

Section 1

Course Introduction

Time 20m

Lectures 3

See All

Introduction to the Course

Preview 0:06:54

Course Help and Welcome

Preview 0:06:54

Course FAQs

0:06:54

Section 2

Environment Set-Up

Time 06m

Lectures 1

See All

Section 3

Jupyter Overview

Time 19m

Lectures 3

See All

Updates to Notebook Zip

0:06:12

Jupyter Notebooks

0:06:12

Optional: Virtual Environments

0:07:30

Section 4

Python Crash Course

Time 59m

Lectures 8

See All

Welcome to the Python Crash Course Section!

0:09:13

Introduction to Python Crash Course

0:07:30

Python Crash Course - Part 1

0:06:54

Python Crash Course - Part 2

0:06:12

Python Crash Course - Part 3

0:06:54

Python Crash Course - Part 4

0:09:13

Python Crash Course Exercises - Overview

0:06:12

Python Crash Course Exercises - Solutions

0:06:54

Section 5

Python for Data Analysis - NumPy

Time 43m

Lectures 8

See All

Welcome to the NumPy Section!

0:03:41

Introduction to Numpy

0:03:41

Numpy Arrays

0:03:41

Quick Note on Array Indexing

0:06:54

Numpy Array Indexing

0:06:12

Numpy Operations

0:03:41

Numpy Exercises Overview

0:09:13

Numpy Exercises Solutions

0:06:12

Section 6

Python for Data Analysis - Pandas

Time 1h 11m

Lectures 11

See All

Welcome to the Pandas Section!

0:06:54

Introduction to Pandas

0:06:12

Series

0:06:54

DataFrames - Part 1

0:06:12

DataFrames - Part 2

0:07:30

DataFrames - Part 3

0:03:41

Missing Data

0:07:30

Groupby

0:06:12

Merging Joining and Concatenating

0:06:12

Operations

0:07:30

Data Input and Output

0:06:54

Section 7

Python for Data Analysis - Pandas Exercises

Time 34m

Lectures 5

See All

Note on SF Salary Exercise

0:03:41

SF Salaries Exercise Overview

0:09:13

SF Salaries Solutions

0:06:12

Ecommerce Purchases Exercise Overview

0:09:13

Ecommerce Purchases Exercise Solutions

0:06:12

Section 8

Python for Data Visualization - Matplotlib

Time 50m

Lectures 7

See All

Section 9

Python for Data Visualization - Seaborn

Time 1h 02m

Lectures 9

See All

Introduction to Seaborn

0:09:13

Distribution Plots

Preview 0:09:13

Categorical Plots

0:03:41

Matrix Plots

0:03:41

Grids

0:06:54

Regression Plots

0:06:54

Style and Color

0:09:13

Seaborn Exercise Overview

0:07:30

Seaborn Exercise Solutions

0:06:12

Section 10

Python for Data Visualization - Pandas Built-in Data Visualization

Time 20m

Lectures 3

See All

Pandas Built-in Data Visualization

0:09:13

Pandas Data Visualization Exercise

0:07:30

Pandas Data Visualization Exercise- Solutions

0:03:41

Section 11

Python for Data Visualization - Plotly and Cufflinks

Time 17m

Lectures 3

See All

Section 12

Python for Data Visualization - Geographical Plotting

Time 41m

Lectures 5

See All

Section 13

Data Capstone Project

Time 1h 05m

Lectures 9

See All

Section 14

Introduction to Machine Learning

Time 40m

Lectures 6

See All

Welcome to Machine Learning. Here are a few resources to get you started!

0:03:41

Welcome to the Machine Learning Section!

0:06:54

Supervised Learning Overview

0:09:13

Evaluating Performance - Classification Error Metrics

0:09:13

Evaluating Performance - Regression Error Metrics

0:03:41

Machine Learning with Python

0:07:30

Section 15

Linear Regression

Time 40m

Lectures 6

See All

Linear Regression Theory

0:09:13

model_selection Updates for SciKit Learn 0.18

0:03:41

Linear Regression with Python - Part 1

0:07:30

Linear Regression with Python - Part 2

0:09:13

Linear Regression Project Overview

0:03:41

Linear Regression Project Solution

0:06:54

Section 16

Cross Validation and Bias-Variance Trade-Off

Time 06m

Lectures 1

See All

Section 17

Logistic Regression

Time 33m

Lectures 6

See All

Logistic Regression Theory

0:06:54

Logistic Regression with Python - Part 1

0:06:12

Logistic Regression with Python - Part 2

0:03:41

Logistic Regression with Python - Part 3

0:06:54

Logistic Regression Project Overview

0:06:12

Logistic Regression Project Solutions

0:03:41

Section 18

K Nearest Neighbors

Time 22m

Lectures 4

See All

KNN Theory

0:06:12

KNN with Python

0:06:12

KNN Project Overview

0:06:54

KNN Project Solutions

0:03:41

Section 19

Decision Trees and Random Forests

Time 37m

Lectures 5

See All

Introduction to Tree Methods

0:07:30

Decision Trees and Random Forest with Python

0:09:13

Decision Trees and Random Forest Project Overview

0:07:30

Decision Trees and Random Forest Solutions Part 1

0:07:30

Decision Trees and Random Forest Solutions Part 2

0:06:12

Section 20

Support Vector Machines

Time 24m

Lectures 4

See All

SVM Theory

0:03:41

Support Vector Machines with Python

0:06:54

SVM Project Overview

0:07:30

SVM Project Solutions

0:06:54

Section 21

K Means Clustering

Time 26m

Lectures 4

See All

K Means Algorithm Theory

0:09:13

K Means with Python

0:07:30

K Means Project Overview

0:06:12

K Means Project Solutions

0:03:41

Section 22

Principal Component Analysis

Time 09m

Lectures 2

See All

Principal Component Analysis

0:06:12

PCA with Python

0:03:41

Section 23

Recommender Systems

Time 20m

Lectures 3

See All

Recommender Systems

0:06:54

Recommender Systems with Python - Part 1

0:06:54

Recommender Systems with Python - Part 2

0:06:54

Section 24

Natural Language Processing

Time 40m

Lectures 6

See All

Natural Language Processing Theory

0:03:41

NLP with Python - Part 1

0:06:12

NLP with Python - Part 2

0:06:12

NLP with Python - Part 3

0:09:13

NLP Project Overview

0:07:30

NLP Project Solutions

0:07:30

Section 25

Neural Nets and Deep Learning

Time 3h 09m

Lectures 31

See All

Download TensorFlow Notebooks Here

0:06:54

Quick Check for Notes

0:09:13

Welcome to the Deep Learning Section!

0:03:41

Introduction to Artificial Neural Networks (ANN)

0:06:12

Installing Tensorflow

0:03:41

Perceptron Model

0:07:30

Neural Networks

0:06:12

Activation Functions

0:03:41

Multi-Class Classification Considerations

0:06:12

Cost Functions and Gradient Descent

0:09:13

Backpropagation

0:06:54

TensorFlow vs Keras

0:06:54

TF Syntax Basics - Part One - Preparing the Data

0:07:30

TF Syntax Basics - Part Two - Creating and Training the Model

0:06:12

TF Syntax Basics - Part Three - Model Evaluation

0:07:30

TF Regression Code Along - Exploratory Data Analysis

0:03:41

TF Regression Code Along - Exploratory Data Analysis - Continued

0:03:41

TF Regression Code Along - Data Preprocessing and Creating a Model

0:03:41

TF Regression Code Along - Model Evaluation and Predictions

0:03:41

TF Classification Code Along - EDA and Preprocessing

0:06:12

TF Classification - Dealing with Overfitting and Evaluation

0:06:12

TensorFlow 2.0 Project Options Overview

0:03:41

TensorFlow 2.0 Project Notebook Overview

0:03:41

Keras Project Solutions - Dealing with Missing Data

0:06:54

Keras Project Solutions - Dealing with Missing Data - Part Two

0:03:41

Keras Project Solutions - Categorical Data

0:09:13

Keras Project Solutions - Data PreProcessing

0:06:54

Keras Project Solutions - Data PreProcessing

0:07:30

Keras Project Solutions - Creating and Training a Model

0:07:30

Keras Project Solutions - Model Evaluation

0:09:13

Tensorboard

0:06:12

Section 26

Big Data and Spark with Python

Time 1h 15m

Lectures 12

See All

Welcome to the Big Data Section!

0:06:54

Big Data Overview

0:06:54

Spark Overview

0:06:12

Local Spark Set-Up

0:06:12

AWS Account Set-Up

0:03:41

Quick Note on AWS Security

0:06:12

EC2 Instance Set-Up

0:06:54

SSH with Mac or Linux

0:07:30

PySpark Setup

0:03:41

Lambda Expressions Review

0:09:13

Introduction to Spark and Python

0:06:12

RDD Transformations and Actions

0:06:12

Section 27

BONUS SECTION: THANK YOU!

Time 06m

Lectures 1

See All

About Tutor

Zoila Lemke

4.50 (10 Reviews)

Courses 1

View Profile

Biography

Hi Students! My name is Zoila Lemke and I have expertise instructing both adults and children. As a senior teacher, I collaborated on the project in 1997 with the Directorate of Education and Trinity College, Spain. I have taught Chinese, Japanese, Hebrew-Israel languages in my teaching experience. Prior to that, I worked at several institutions such as the Army Wives Welfare Association's School of English and Hindustan Times Learning Centre, where I taught learners of various capacities. I am a happy person who enjoys interacting with others.

Ratings & Reviews

0.00

0 Reviews

Channels

Python for Data Science and Machine Learning Bootcamp

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

This Course Includes:

Share This Course

Overview

What will students learn in your course?

What are the requirements or prerequisites for taking your course?

Who is the course for?

Description

Course Tags

Course Content

Course Introduction

Environment Set-Up

Jupyter Overview

Python Crash Course

Python for Data Analysis - NumPy

Python for Data Analysis - Pandas

Python for Data Analysis - Pandas Exercises

Python for Data Visualization - Matplotlib

Python for Data Visualization - Seaborn

Python for Data Visualization - Pandas Built-in Data Visualization

Python for Data Visualization - Plotly and Cufflinks

Python for Data Visualization - Geographical Plotting

Data Capstone Project

Introduction to Machine Learning

Linear Regression

Cross Validation and Bias-Variance Trade-Off

Logistic Regression

K Nearest Neighbors

Decision Trees and Random Forests

Support Vector Machines

K Means Clustering

Principal Component Analysis

Recommender Systems

Natural Language Processing

Neural Nets and Deep Learning

Big Data and Spark with Python

BONUS SECTION: THANK YOU!

About Tutor

Zoila Lemke

Biography

Ratings & Reviews