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Data Science and Machine Learning using Python - A Bootcamp (Udemy.com)

Numpy Pandas Matplotlib Seaborn Ploty Machine Learning Scikit-Learn Data Science Recommender system NLP Theory Hands-on

Created by: Dr. Junaid Qazi, PhD

Last updated February 2020

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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...................!

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating4.4 out of 5higher than 28% of them
  • Reviews561#220 of 462 by number of reviews
  • Length24.9 hourslonger than 86% of them
  • Last updatedFebruary 2020earlier than 91% of them

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Quality Score

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Overall Score : 88 / 100

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Course Description

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! 

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Instructor Details

Dr. Junaid Qazi, PhD

Dr. Qazi has a BS with major in Maths, Statistics  & Physics, MS in Computer Science and PhD degree.  As a mentor and a researcher scientist, with over 18 years of professional experience, Dr. Qazi has developed a skill set in data cleaning/mining, data analysis & data modelling, project management, teaching & training and career advising while working with academic and industrial giants. Dr. Qazi has also served in academia for several years at the rank of lecturer and assistant professor. During his career, he won several funding awards for his research ideas and published high quality articles in well reputed international journals in collaboration with leading scientists from University of British Columbia, Canada; University of Calgary, Canada; Uppsala University, Sweden; Institut Laue-Langevin (ILL), France; European Synchrotron Radiation Facility (ESRF), France; Diamond Light Source, UK; ISIS Neutron and Muon Source, UK; Unilever, UK; NIST Centre for Neutron Research, USA. He has developed algorithms and written computer codes for his published work. As an invited speaker, Dr. Qazi has delivered several lectures around the globe to the scientific and business community.

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Reviews

4.4

561 ratings on Udemy

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By Khaled Alam on 1/17/2020

It was a very good course on machine learning with python. The pace of the lecture was perfect for me, not too fast or not too slow. The course explains many topics in easy and interesting way. Now, I feel confident to apply this knowledge in new datasets. However, I have a little suggestion. In the projects, you used the cleaned up and processed datasets. Its better to include the data cleaning and processing steps applied on UCI raw datasets. This will be a good practice on data cleaning skills.

By Girijesh Prasad on 9/23/2019

The more I watch this again and again, more I fall in love with the in depth and easy to understand explanation of each and every topic covered. This course is very intuitive and from basic to up to a level from anyone can tackle real world scenario. From exploratory data analysis , pandas and until algorithm everything is clearly explained. Waiting for more courses like this from the Author. Regards, Girijesh

By Manoj Raghavan Srinivasan on 8/13/2019

This course helped me to kick start my machine learning journey from scratch. Mr.Qazi illustrated all the concepts through coding in python. Also provided practice sheets for students to enhance the machine learning knowledge by doing. Thank you Mr.Qazi and Udemy.

By Micah Gray on 7/9/2019

This was a good introductory course for learning Python and beginning to play with data. I feel like I now know enough to be dangerous. I had a hard time following the lecture on Principal Component Analysis. The concepts were abstract and complicated, and I felt that the given examples did not help my comprehension. Although I can replicate the processes used in the lecture, I do not know when I would use PCA, how it could be useful, or how to interpret the results.

By John on 5/27/2019

I had a general laymans knowledge on Machine Learning(I had heard of it) but no practical experience. After doing this course I was able to upload data from csv files into pandas and manipulate and clean the the data such that it was ready for data analysis and machine learning algorithms. This was invaluable since coincidentally a team building exercise at work tasked me to use machine learning to predict the cricket world cup. The course really helped me a lot in that task and I was able to create a prediction for the team exercise. I have to say after doing this course it has piqued an interest in Machine Learning and I will definitely continue to review this course further(I haven't completely finished it) and use this course as a reference material in the future. I can say that I recommend this course.

By Sahar Gargary on 5/11/2019

this course is highly recommended to all professionals! Data Science is very simply explained in this course. Practices and hands on training are very extensive that builds great confidence level. Dr Qazi generously is sharing his extensive knowledge and expertise in this course. I personally had a chance to know him and attend his course as well as taking his course online. He is passionate about data science as well as teaching! highly appreciate his effort to teach and grateful to know him.

By Brian Buccellato on 4/18/2019

There is a lot of showing without much explanation so far. It's clear that the intended audience shouldn't have much programming experience, but I'm not sure what the average audience should know. Additionally, Python syntax is really poor so far, which is really unfair to those without previous Python experience.

By Say Joon Tan on 3/28/2019

1. Lots of grammatical error in the exercise and solution notebooks 2. Did not cover Tensorflow

By Soelyla Salie on 12/11/2018

I've completed the course and it feels intuitive and completing tasks faster with greater understanding. Simply because the instructor gives a brilliant breakdown of the theory, lots of exercises and finally a 'learn-by-doing" enjoyable project. This is repeated throughout the course. Well done and I hope to see more courses , and perhaps additional topics to this already extensive Data Science course. Dr Quazi is a terrific instructor

By Kaja Khudhubudeen K on 11/20/2018

It is a wonderful course which focused on the required portions of Data Science and Machine Learning. Very helpful for DS Beginner and Practitioner to have overall knowledge of what to do in ML. Since I am a BI analyst, I am very much loved the Data Visualization lessons using Pandas and Seaborn. Please go through the courses thrice and you will be amazed to realize and learn new things in the same lesson. Good Luck Everyone! Thank you Junaid for keeping it crisp and knowledgeable!

By Sangeetha G J

Inspite of being a beginner to programming, i found it very useful and understandable. Explained in very good manner and quick response to the queries too.

By Adbullah Ahmad

Excellent teacher and to the point course content. Please create more courses so that your courses will provide complete learning path to students.

1- Data Science & Machine Learning using Python - A Bootcamp - good

for all

2- Refresher course in Linear Algebra, Calculus, Probability &

Statistics special focus ML & DL

2- Data Science & Machine Learning using Python - Intermediate level

3- Data Science & Machine Learning using Python - Advance level

4- Good size Projects of Data Science, Machine Learning, Deep Learning

Showing 12 of 89 reviews