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Data Science & Machine Learning using Python – A Bootcamp

A Jump start towards the most rewarding and in-demand career of Data Science and Machine Learning!
Instructor:
Dr. Junaid Qazi, PhD
1,169 students enrolled
English [Auto-generated]
You will learn the skill set and power of 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...................!

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 Tradeoff

  • Logistic Regression

  • Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision

  • K Nearest Neighbour

  • Curse of Dimensionality, Model Performance

  • Decision Trees

  • Tree Depth, Splitting at Nodes, Entropy, Information Gain 

  • Random Forest

  • 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, BoW, 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, transections 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! 

Welcome, Course Introduction & overview, and Environment set-up

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Welcome & Course Overview
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Please read, it's important for you to know!
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Download_Course_Material

Please download the course material (link is provided below FAQ). The material includes all the resource and code files that you need for this course!

Frequently Asked Question (FAQ):

How to get successful in the filed of Data Science and Machine Learning, what is the probability of getting the job?

Few tips for you: Remember, you need to create your profile. I suggest, if you don’t have, create your GitHub account and upload all of your work there. As a data scientist, one of the key thing is reporting based on your findings. This course is an excellent jump-off to get into this rewarding career. After finished each project / section, create a report and present conclusions. Attach the report to each project in your Github account. You are most welcome to reference this course in your report, this might be helpful. Please don’t put the course material (the folder which came with this course) in your Github, create your own stuff (don’t worry even if they are similar), everything should go through your hands.  I have seen that the blog posts are also helpful in creating your profile and to make good connections. Regarding job, there are more jobs in the market than the available professionals in this field. This course will land you in the interview, success is on your efforts and hard-work. Practice is a key and there are tons of datasets available to practice your skills. Keep yourself motivated, a great career is waiting for you!

4
Set-up the Environment for the Course
5
Download environment file and watch next lecture to setup -- super easy way
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Must watch -- Super easy way to setup environment (Recommended)

Please follow the instructions to create an environment. Once, you are done creating environment, you don't need to install anything for this course. This is super easy!

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Important Note:
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Possible updates in the course.

Python Essentials

1
Python data types Part 1
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Python Data Types Part 2
3
Comparisons Operators, if, else, elif statement
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Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)
5
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)
6
Python Essentials Exercises Overview
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Python Essentials Exercises Solutions

Python for Data Analysis using NumPy

1
What is Numpy? A brief introduction and installation instructions.
2
NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.
3
NumPy Essentials - Indexing, slicing, broadcasting & boolean masking
4
NumPy Essentials - Arithmetic Operations & Universal Functions
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NumPy Essentials Exercises Overview
6
NumPy Essentials Exercises Solutions

Python for Data Analysis using Pandas

1
What is pandas? A brief introduction and installation instructions.
2
Pandas Introduction.
3
Pandas Essentials - Pandas Data Structures - Series
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Pandas Essentials - Pandas Data Structures - DataFrame
5
Pandas Essentials - Hierarchical Indexing
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Pandas Essentials - Handling Missing Data
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Pandas Essentials - Data Wrangling - Combining, merging, joining
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Pandas Essentials - Groupby
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Pandas Essentials - Useful Methods and Operations
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Pandas Essentials - Project 1 (Overview) Customer Purchases Data
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Pandas Essentials - Project 1 (Solutions) Customer Purchases Data
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Pandas Essentials - Project 2 (Overview) Chicago Payroll Data
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Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data
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Pandas Essentials - Project 2 (Solutions Part 2) Chicago Payroll Data

Python for Data Visualization using matplotlib

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Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach
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Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach
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Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach
4
Matplotlib Essentials - Exercises Overview
5
Matplotlib Essentials - Exercises Solutions
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Matplotlib Essentials (Optional) - Advance

This is a self-study and optional part.

Jupyter notebook is provided for you guys to learn few advanced plotting concepts using matplotlib.

Matplotlib provides tons of options that we don't use often, but it is good to know few of them.

Keep in in your mind, you can always explore the official documentation for more resources. 

Python for Data Visualization using Seaborn

1
Seaborn - Introduction & Installation
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Seaborn - Distribution Plots
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Seaborn - Categorical Plots (Part 1)
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Seaborn - Categorical Plots (Part 2)
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Seaborn - Axis Grids
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Seaborn - Matrix Plots
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Seaborn - Regression Plots
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Seaborn - Controlling Figure Aesthetics
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Seaborn - Exercises Overview
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Seaborn - Exercise Solutions

Python for Data Visualization using pandas

1
Pandas Built-in Data Visualization
2
Pandas Data Visualization Exercises Overview
3
Panda Data Visualization Exercises Solutions

Python for interactive & geographical plotting using Plotly and Cufflinks

1
Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1)
2
Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2)
3
Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview)
4
Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions)

Capstone Project - Python for Data Analysis & Visualization

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Project 1 - Oil vs Banks Stock Price during recession (Overview)
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Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1)
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Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2)
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Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3)
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Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview)

Python for Machine Learning (ML) - scikit-learn - Linear Regression Model

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Introduction to ML - What, Why and Types.....
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Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff
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A note on student’s concerns and questions on FutureWarnings.
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scikit-learn - Linear Regression Model - Hands-on (Part 1)
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scikit-learn - Linear Regression Model Hands-on (Part 2)
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scikit-learn - Linear Regression Model (Insurance Data Project Overview)
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scikit-learn - Linear Regression Model (Insurance Data Project Solutions)

Python for Machine Learning - scikit-learn - Logistic Regression Model

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Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity...etc.
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Output of classification report in scikit-learn — A small change
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scikit-learn - Logistic Regression Model - Hands-on (Part 1)
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scikit-learn - Logistic Regression Model - Hands-on (Part 2)
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scikit-learn - Logistic Regression Model - Hands-on (Part 3)
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scikit-learn - Logistic Regression Model - Hands-on (Project Overview)
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scikit-learn - Logistic Regression Model - Hands-on (Project Solutions)

Python for Machine Learning - scikit-learn - K Nearest Neighbors

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Theory: K Nearest Neighbors, Curse of dimensionality ....
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scikit-learn - K Nearest Neighbors - Hands-on
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scikt-learn - K Nearest Neighbors (Project Overview)
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scikit-learn - K Nearest Neighbors (Project Solutions)

Python for Machine Learning - scikit-learn - Decision Tree and Random Forests

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Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging....
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scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1)
3
scikit-learn - Decision Tree and Random Forests (Project Overview)
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scikit-learn - Decision Tree and Random Forests (Project Solutions)

Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)

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Support Vector Machines (SVMs) - (Theory Lecture)
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25 hours on-demand video
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Full lifetime access
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Certificate of Completion