‘Learn Data Science and Machine Learning using Python’ is a comprehensive Bootcamp course. This course utilizes the power of Python to teach exploratory data analysis and machine learning techniques. It is one of the most comprehensive courses available on any online learning platform, including Udemy. Students will develop the ability to delve deeply into the data and make compelling arguments for decisions.
Bootcamps for data science can cost thousands of dollars, but this course is significantly less expensive than other comparable Bootcamps and offers HD lectures in addition to thorough code notebooks for each lesson. The course emphasizes “Learning by Doing” and includes practice tasks based on actual data for each topic they study. The course is usually available for INR 2,299 on Udemy but you can click now to get 80% off and get Learn Data Science and Machine Learning using Python: A Bootcamp Course for INR 449.
Who all can opt for this course?
- Candidates who want to learn Python for data science
- Candidates who desire to study Python-based machine learning
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 25 hours |
| Rating | 3.9/5 |
| Student Enrollment | 2,500 students |
| Instructor | Dr. Junaid Qazi, PhD https://www.linkedin.com/in/dr.junaidqazi,phd |
| Topics Covered | Python programming for Data Science, NumPy, Pandas, Matplotlib, SciKit-Learn, etc. |
| Course Level | Beginner |
| Total Student Reviews | 547 |
Learning Outcomes
- Use Python to analyze data, create state-of-the-art visualization and use machine learning algorithms to facilitate decision making
- Learn Python for machine learning and data science
- Use NumPy for numerical data
- Analyzing Data with Pandas
- Plotting with Matplotlib
- Statistical Plots with Seaborn
- Plotly-based interactive dynamic data visualizations
- Use SciKit-Learn for machine learning
- Linear Regression, Logistic Regression, and K-Mean Clustering
- Decision Trees and Random Forest
- Analysis by Principal Components (PCA)
- Support Vector Systems
- Recommendation engines
- Artificial Intelligence and spam filters
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Welcome, Course Introduction & overview, and Environment set-up (47 minutes) | Welcome & Course Overview |
| Please read, it’s important for you to know! | ||
| Download_Course_Material | ||
| Set-up the Environment for the Course (lecture 1) | ||
| Set-up the Environment for the Course (lecture 2) | ||
| Download environment file and watch next lecture to setup — super easy way | ||
| Two other options to setup the environment | ||
| Important Note: | ||
| Possible updates in the course. | ||
| 2. | Python Essentials (01 hour 47 minutes) | Python data types Part 1 |
| Python Data Types Part 2 | ||
| Comparisons Operators, if, else, elif statement | ||
| Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) | ||
| Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) | ||
| Python Essentials Exercises Overview | ||
| Python Essentials Exercises Solutions | ||
| 3. | Python for Data Analysis using NumPy (01 hour 31 minutes) | What is Numpy? A brief introduction and installation instructions. |
| NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes. | ||
| NumPy Essentials – Indexing, slicing, broadcasting & boolean masking | ||
| NumPy Essentials – Arithmetic Operations & Universal Functions | ||
| NumPy Essentials Exercises Overview | ||
| NumPy Essentials Exercises Solutions | ||
| 4. | Python for Data Analysis using Pandas (03 hours 36 minutes) | What is pandas? A brief introduction and installation instructions. |
| Pandas Introduction. | ||
| Pandas Essentials – Pandas Data Structures – Series | ||
| Pandas Essentials – Pandas Data Structures – DataFrame | ||
| Pandas Essentials – Hierarchical Indexing | ||
| Pandas Essentials – Handling Missing Data | ||
| Pandas Essentials – Data Wrangling – Combining, merging, joining | ||
| Pandas Essentials – Groupby | ||
| Pandas Essentials – Useful Methods and Operations | ||
| Pandas Essentials – Project 1 (Overview) Customer Purchases Data | ||
| Pandas Essentials – Project 1 (Solutions) Customer Purchases Data | ||
| Pandas Essentials – Project 2 (Overview) Chicago Payroll Data | ||
| Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data | ||
| Pandas Essentials – Project 2 (Solutions Part 2) Chicago Payroll Data | ||
| 5. | Python for Data Visualization using matplotlib (01 hour 24 minutes) | Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach |
| Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach | ||
| Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach | ||
| Matplotlib Essentials – Exercises Overview | ||
| Matplotlib Essentials – Exercises Solutions | ||
| Matplotlib Essentials (Optional) – Advance | ||
| 6. | Python for Data Visualization using Seaborn (02 hours 28 minutes) | Seaborn – Introduction & Installation |
| Seaborn – Distribution Plots | ||
| Seaborn – Categorical Plots (Part 1) | ||
| Seaborn – Categorical Plots (Part 2) | ||
| Seaborn – Axis Grids | ||
| Seaborn – Matrix Plots | ||
| Seaborn – Regression Plots | ||
| Seaborn – Controlling Figure Aesthetics | ||
| Seaborn – Exercises Overview | ||
| Seaborn – Exercise Solutions | ||
| 7. | Python for Data Visualization using pandas (49 minutes) | Pandas Built-in Data Visualization |
| Pandas Data Visualization Exercises Overview | ||
| Panda Data Visualization Exercises Solutions | ||
| 8. | Python for interactive & geographical plotting using Plotly and Cufflinks (01 hour 20 minutes) | Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) |
| Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) | ||
| Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) | ||
| Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) | ||
| 9. | Capstone Project – Python for Data Analysis & Visualization (01 hour 09 minutes) | Project 1 – Oil vs Banks Stock Price during recession (Overview) |
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) | ||
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) | ||
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) | ||
| Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) | ||
| 10. | Python for Machine Learning (ML) – scikit-learn – Linear Regression Model (01 hour 45 minutes) | Introduction to ML – What, Why and Types….. |
| Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff | ||
| A note on student’s concerns and questions on FutureWarnings. | ||
| scikit-learn – Linear Regression Model – Hands-on (Part 1) | ||
| scikit-learn – Linear Regression Model Hands-on (Part 2) | ||
| Good to know! How to save and load your trained Machine Learning Model! | ||
| scikit-learn – Linear Regression Model (Insurance Data Project Overview) | ||
| scikit-learn – Linear Regression Model (Insurance Data Project Solutions) | ||
| 11. | Python for Machine Learning – scikit-learn – Logistic Regression Model (01 hour 18 minutes) | Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. |
| Output of classification report in scikit-learn — A small change | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 1) | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 2) | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 3) | ||
| scikit-learn – Logistic Regression Model – Hands-on (Project Overview) | ||
| scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) | ||
| 12. | Python for Machine Learning – scikit-learn – K Nearest Neighbors (50 minutes) | Theory: K Nearest Neighbors, Curse of dimensionality …. |
| scikit-learn – K Nearest Neighbors – Hands-on | ||
| scikt-learn – K Nearest Neighbors (Project Overview) | ||
| scikit-learn – K Nearest Neighbors (Project Solutions) | ||
| 13. | Python for Machine Learning – scikit-learn – Decision Tree and Random Forests (56 minutes) | Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. |
| scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) | ||
| scikit-learn – Decision Tree and Random Forests (Project Overview) | ||
| scikit-learn – Decision Tree and Random Forests (Project Solutions) | ||
| 14. | Python for Machine Learning – scikit-learn -Support Vector Machines (SVMs) (01 hour 05 minutes) | Support Vector Machines (SVMs) – (Theory Lecture) |
| scikit-learn – Support Vector Machines – Hands-on (SVMs) | ||
| scikit-learn – Support Vector Machines (Project 1 Overview) | ||
| scikit-learn – Support Vector Machines (Project 1 Solutions) | ||
| scikit-learn – Support Vector Machines (Optional Project 2 – Overview) | ||
| 15. | Python for Machine Learning – scikit-learn – K Means Clustering (01 hour 03 minutes) | Theory: K Means Clustering, Elbow method ….. |
| scikit-learn – K Means Clustering – Hands-on | ||
| scikit-learn – K Means Clustering (Project Overview) | ||
| scikit-learn – K Means Clustering (Project Solutions) | ||
| 16. | Python for Machine Learning – scikit-learn – Principal Component Analysis (PCA) (49 minutes) | Theory: Principal Component Analysis (PCA) |
| scikit-learn – Principal Component Analysis (PCA) – Hands-on | ||
| scikit-learn – Principal Component Analysis (PCA) – (Project Overview) | ||
| scikit-learn – Principal Component Analysis (PCA) – (Project Solutions) | ||
| 17. | Recommender Systems with Python – (Additional Topic) (42 minutes) | Theory: Recommender Systems their Types and Importance |
| Python for Recommender Systems – Hands-on (Part 1) | ||
| Python for Recommender Systems – – Hands-on (Part 2) | ||
| 18. | Python for Natural Language Processing (NLP) – NLTK – (Additional Topic) (01 hour 25 minutes) | Natural Language Processing (NLP) – (Theory Lecture) |
| NLTK – NLP-Challenges, Data Sources, Data Processing ….. | ||
| NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing | ||
| NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. | ||
| NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … | ||
| NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… | ||
| 19. | Thank you and closing remarks (01 minutes) | Please read, it’s important! |
| Thank you for doing the course! |
Resources Required
A PC and the desire to succeed! Although not necessary, some programming expertise could be useful
Featured Review
Kaja Khudhubudeen K (5/5): 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!
Pros
- Mihir Kulkarni (5/5): The best part of this course is professor taught me Python basics and how to implement machine learning algorithms in Python.
- Muhammad Jamshed (5/5): This is an Excellent course for both beginners and people already learning Python for Data Science and Machine Learning.
- Muhammad Jamshed (5/5): Provides an excellent explanation on the course material in Lectures and during coding exercises.
- Mehdi Nikkhah (5/5): Using scikit-learn, the best library for coding Machine learning Problems, was the best get of the course.
Cons
- Divya T. (4/5): basics have been covered in the projects..but more steps could have been there for cleaning n missing data imputation which happens in real time data
- Say Joon T. (3/5): 1. Lots of grammatical errors in the exercise and solution notebooks 2. Did not cover Tensorflow
- Brian B. (3/5): 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.
About the Author
The instructor of this course is Dr. Junaid Qazi, Ph.D. who is a Data Scientist. With a 3.9 instructor rating and 547 reviews on Udemy, he offers 1 course and has taught 7,191 students so far.
- Dr. Qazi holds a Ph.D., an MS in computer science, and a BS with a major in math, statistics, and physics.
- With more than 18 years of professional experience as a mentor and researcher scientist, Dr. Qazi has honed a skill set in data cleaning/mining, data analysis & modeling, project management, teaching & training, and career counseling while working with academic and corporate behemoths.
- Dr. Qazi has also worked for a number of years as an academic, holding the positions of lecturer and assistant professor.
- He collaborated with eminent scientists from the University of British Columbia in Canada, the University of Calgary in Canada, Uppsala University in Sweden, the Institut Laue-Langevin (ILL) in France, the European Synchrotron Radiation Facility (ESRF) in France, the Diamond Light Source in the United Kingdom, the ISIS Neutron and Muon Source in the United Kingdom, Unilever in the United Kingdom, and the NIST Centre for Atomic and Molecular Measurements in the United States to for his published works, he has created algorithms and written computer code.
- Dr. Qazi has given various presentations to the scientific and business communities as an invited speaker around the world.
Comparison Table
| Parameters | Data Science and Machine Learning using Python – A Bootcamp | Machine Learning Classification Bootcamp in Python | Complete 2023 Data Science & Machine Learning Bootcamp |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 25 hours | 11.5 hours | 41.5 hours |
| Rating | 3.9 /5 | 4.4 /5 | 4.6 /5 |
| Student Enrollments | 2,500 | 8,576 | 42,138 |
| Instructors | Dr. Junaid Qazi, PhD | Dr. Ryan Ahmed, Ph.D., MBA | Philipp Muellauer |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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