Data Science and Machine Learning using Python – A Bootcamp

Data Science and Machine Learning using Python – A Bootcamp, available at $64.99, has an average rating of 4.6, with 113 lectures, based on 556 reviews, and has 2528 subscribers.

You will learn about Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making. Python for Data Science and Machine Learning NumPy for Numerical Data Pandas for Data Analysis Plotting with Matplotlib Statistical Plots with Seaborn Interactive dynamic visualizations of data using Plotly SciKit-Learn for Machine Learning K-Mean Clustering, Logistic Regression, Linear Regression Random Forest and Decision Trees Principal Component Analysis (PCA) Support Vector Machines Recommender Systems Natural Language Processing and Spam Filters and much more……………….! This course is ideal for individuals who are For you, if you: or want to learn Data Science with Python or want to learn Machine Learning with Python or are tired of complicated courses and "Learn by Doing" It is particularly useful for For you, if you: or want to learn Data Science with Python or want to learn Machine Learning with Python or are tired of complicated courses and "Learn by Doing".

Enroll now: Data Science and Machine Learning using Python – A Bootcamp

Summary

Title: Data Science and Machine Learning using Python – A Bootcamp

Price: $64.99

Average Rating: 4.6

Number of Lectures: 113

Number of Published Lectures: 111

Number of Curriculum Items: 113

Number of Published Curriculum Objects: 111

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making.
Python for Data Science and Machine Learning
NumPy for Numerical Data
Pandas for Data Analysis
Plotting with Matplotlib
Statistical Plots with Seaborn
Interactive dynamic visualizations of data using Plotly
SciKit-Learn for Machine Learning
K-Mean Clustering, Logistic Regression, Linear Regression
Random Forest and Decision Trees
Principal Component Analysis (PCA)
Support Vector Machines
Recommender Systems
Natural Language Processing and Spam Filters
and much more……………….!

Who Should Attend

For you, if you:
want to learn Data Science with Python
want to learn Machine Learning with Python
are tired of complicated courses and "Learn by Doing"

Target Audiences

For you, if you:
want to learn Data Science with Python
want to learn Machine Learning with Python
are tired of complicated courses and "Learn by Doing"

Greetings,

I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the impact of your work around your, is not is amazing?

This is one of the most comprehensive course on any e-learning platform(including Udemy marketplace)which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making.

Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is “Learn by Doing”!

For your satisfaction, I would like to mention few topics that we will be learning in this course:

Basis Python programming for Data Science
Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter
NumPy
Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions
Pandas
Pandas Data Structures – Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling – Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data Visualization
Matplotlib
Basic Plotting & Object Oriented Approach
Seaborn
Distribution & Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics
Plotly and Cufflinks
Interactive & Geographical plotting
SciKit-Learn (one of the world’s best machine learning Python library) including:
Liner Regression
Over fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning Models
Logistic Regression
Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision
K Nearest Neighbour (KNN)
Curse of Dimensionality, Model Performance
Decision Trees
Tree Depth, Splitting at Nodes, Entropy, Information Gain
Random Forests
Bootstrap, Bagging (Bootstrap Aggregation)
K Mean Clustering
Elbow Method
Principle Component Analysis (PCA)
Support Vector Machine
Recommender Systems
Natural Language Processing (NLP)
Tokenization, Text Normalization, Vectorization, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Pipeline feature……..and MUCH MORE……….!

Not only the hands-on practice using tens of real data project, theory lectures are also provided to make you understand the working principle behind the Machine Learning models.

So, what are you waiting for, this is your opportunity to learn the real Data Science with a fraction of the cost of any of your undergraduate course…..!

Brief overview of Data around us:

According to IBM, we create 2.5 Quintillion bytes of data daily and 90% of the existing data in the world today, has been created in the last two years alone. Social media, transactions records, cell phones, GPS, emails, research, medical records and much more…., the data comes from everywhere which has created a big talent gap and the industry, across the globe, is experiencing shortage of experts who can answer and resolve the challenges associated with the data. Professionals are needed in the field of Data Science who are capable of handling and presenting the insights of the data to facilitate decision making. This is the time to get into this field with the knowledge and in-depth skills of data analysis and presentation.

Have Fun and Good Luck!

Course Curriculum

Chapter 1: Welcome, Course Introduction & overview, and Environment set-up

Lecture 1: Welcome & Course Overview

Lecture 2: Please read, it's important for you to know!

Lecture 3: Download_Course_Material

Lecture 4: Set-up the Environment for the Course (lecture 1)

Lecture 5: Set-up the Environment for the Course (lecture 2)

Lecture 6: Download environment file and watch next lecture to setup — super easy way

Lecture 7: Two other options to setup environment

Lecture 8: Important Note:

Lecture 9: Possible updates in the course.

Chapter 2: Python Essentials

Lecture 1: Python data types Part 1

Lecture 2: Python Data Types Part 2

Lecture 3: Comparisons Operators, if, else, elif statement

Lecture 4: Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)

Lecture 5: Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)

Lecture 6: Python Essentials Exercises Overview

Lecture 7: Python Essentials Exercises Solutions

Chapter 3: Python for Data Analysis using NumPy

Lecture 1: What is Numpy? A brief introduction and installation instructions.

Lecture 2: NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes.

Lecture 3: NumPy Essentials – Indexing, slicing, broadcasting & boolean masking

Lecture 4: NumPy Essentials – Arithmetic Operations & Universal Functions

Lecture 5: NumPy Essentials Exercises Overview

Lecture 6: NumPy Essentials Exercises Solutions

Chapter 4: Python for Data Analysis using Pandas

Lecture 1: What is pandas? A brief introduction and installation instructions.

Lecture 2: Pandas Introduction.

Lecture 3: Pandas Essentials – Pandas Data Structures – Series

Lecture 4: Pandas Essentials – Pandas Data Structures – DataFrame

Lecture 5: Pandas Essentials – Hierarchical Indexing

Lecture 6: Pandas Essentials – Handling Missing Data

Lecture 7: Pandas Essentials – Data Wrangling – Combining, merging, joining

Lecture 8: Pandas Essentials – Groupby

Lecture 9: Pandas Essentials – Useful Methods and Operations

Lecture 10: Pandas Essentials – Project 1 (Overview) Customer Purchases Data

Lecture 11: Pandas Essentials – Project 1 (Solutions) Customer Purchases Data

Lecture 12: Pandas Essentials – Project 2 (Overview) Chicago Payroll Data

Lecture 13: Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data

Lecture 14: Pandas Essentials – Project 2 (Solutions Part 2) Chicago Payroll Data

Chapter 5: Python for Data Visualization using matplotlib

Lecture 1: Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach

Lecture 2: Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach

Lecture 3: Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach

Lecture 4: Matplotlib Essentials – Exercises Overview

Lecture 5: Matplotlib Essentials – Exercises Solutions

Lecture 6: Matplotlib Essentials (Optional) – Advance

Chapter 6: Python for Data Visualization using Seaborn

Lecture 1: Seaborn – Introduction & Installation

Lecture 2: Seaborn – Distribution Plots

Lecture 3: Seaborn – Categorical Plots (Part 1)

Lecture 4: Seaborn – Categorical Plots (Part 2)

Lecture 5: Seaborn – Axis Grids

Lecture 6: Seaborn – Matrix Plots

Lecture 7: Seaborn – Regression Plots

Lecture 8: Seaborn – Controlling Figure Aesthetics

Lecture 9: Seaborn – Exercises Overview

Lecture 10: Seaborn – Exercise Solutions

Chapter 7: Python for Data Visualization using pandas

Lecture 1: Pandas Built-in Data Visualization

Lecture 2: Pandas Data Visualization Exercises Overview

Lecture 3: Panda Data Visualization Exercises Solutions

Chapter 8: Python for interactive & geographical plotting using Plotly and Cufflinks

Lecture 1: Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1)

Lecture 2: Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2)

Lecture 3: Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview)

Lecture 4: Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions)

Chapter 9: Capstone Project – Python for Data Analysis & Visualization

Lecture 1: Project 1 – Oil vs Banks Stock Price during recession (Overview)

Lecture 2: Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1)

Lecture 3: Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2)

Lecture 4: Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3)

Lecture 5: Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview)

Chapter 10: Python for Machine Learning (ML) – scikit-learn – Linear Regression Model

Lecture 1: Introduction to ML – What, Why and Types…..

Lecture 2: Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff

Lecture 3: A note on student’s concerns and questions on FutureWarnings.

Lecture 4: scikit-learn – Linear Regression Model – Hands-on (Part 1)

Lecture 5: scikit-learn – Linear Regression Model Hands-on (Part 2)

Lecture 6: Good to know! How to save and load your trained Machine Learning Model!

Lecture 7: scikit-learn – Linear Regression Model (Insurance Data Project Overview)

Lecture 8: scikit-learn – Linear Regression Model (Insurance Data Project Solutions)

Chapter 11: Python for Machine Learning – scikit-learn – Logistic Regression Model

Lecture 1: Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc.

Lecture 2: Output of classification report in scikit-learn — A small change

Lecture 3: scikit-learn – Logistic Regression Model – Hands-on (Part 1)

Lecture 4: scikit-learn – Logistic Regression Model – Hands-on (Part 2)

Lecture 5: scikit-learn – Logistic Regression Model – Hands-on (Part 3)

Lecture 6: scikit-learn – Logistic Regression Model – Hands-on (Project Overview)

Lecture 7: scikit-learn – Logistic Regression Model – Hands-on (Project Solutions)

Chapter 12: Python for Machine Learning – scikit-learn – K Nearest Neighbors

Lecture 1: Theory: K Nearest Neighbors, Curse of dimensionality ….

Lecture 2: scikit-learn – K Nearest Neighbors – Hands-on

Lecture 3: scikt-learn – K Nearest Neighbors (Project Overview)

Lecture 4: scikit-learn – K Nearest Neighbors (Project Solutions)

Chapter 13: Python for Machine Learning – scikit-learn – Decision Tree and Random Forests

Lecture 1: Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging….

Lecture 2: scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1)

Lecture 3: scikit-learn – Decision Tree and Random Forests (Project Overview)

Lecture 4: scikit-learn – Decision Tree and Random Forests (Project Solutions)

Instructors

Dr. Junaid Qazi, PhD
Data Scientist

Rating Distribution

1 stars: 11 votes
2 stars: 3 votes
3 stars: 55 votes
4 stars: 193 votes
5 stars: 294 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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