Complete Machine Learning & Data Science Bootcamp 2023 is a project-based course that focuses on modern Data Scientist skills primarily Data Science and Machine Learning. The course is made up of 2 tracks, one for those who want to learn Python and Machine Learning from scratch, and the other dives into advanced concepts like Neural Networks, Deep Learning, and Transfer Learning.
Students get access to all of the code, workbooks, and templates (Jupyter Notebooks) on Github and they use the latest version of Python, Tensorflow 2.0, and other libraries. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Complete Machine Learning & Data Science Bootcamp 2023 Course for INR 449.
Who all can opt for this course?
- Anyone who wants to learn Python, Data Science, or Machine Learning but has no prior experience
- You are a programmer who wants to increase your value by expanding your knowledge of Data Science and Machine Learning
- Anyone who wants to learn about these subjects from professionals who haven’t just taught but have worked in the industry
- You’re seeking a single course that will teach you everything you need to know about Machine Learning and Data Science while keeping you updated with the current trends in the field
- Instead of spending hours watching someone code on your screen without actually getting it, you should study the fundamentals and be able to comprehend the subjects
- For your projects, you want to understand how to employ deep learning and neural networks
- By utilizing potent machine learning techniques, you aim to improve the company you work for or run yourself
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 43 hours |
| Rating | 4.6/5 |
| Student Enrollment | 80,325 students |
| Instructor | Andrei Neagoie https://www.linkedin.com/in/andreineagoie |
| Topics Covered |
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| Course Level | Intermediate |
| Total Student Reviews | 13,218 |
Learning Outcomes
- Learn to be a Data Scientist to land a job
- Learn how to apply Machine Learning at work
- Using the most recent Tensorflow 2.0
- Utilize cutting-edge tools that major tech firms like Google, Apple, Amazon, and Meta utilize
- Present your data science projects to management and stakeholders
- Find out which Machine Learning model is best for a certain problem type
- To understand how things are done in the real world, use case studies and projects from real life
- Learn about data science workflow best practices
- Put machine learning algorithms into practice
- Utilize the most recent Python 3 version to learn Python programming
- Learn ways to make your machine-learning models better
- Learn how to clean, preprocess, and analyze enormous amounts of data
- Create a collection of your work to include on your CV
- Configuration of the data science and machine learning developer environment
- Utilize data visualization tools like Matplotlib and Seaborn to explore big datasets
- Utilize Pandas to explore huge datasets and manage data
- Learn about NumPy and how to implement it in machine learning
- A portfolio of Data Science and Machine Learning projects, complete with source code and notebooks for portfolio
- Use Scikit-learn, a well-liked library, in your projects
- Learn about data engineering and the applications of tools like Kafka, Spark, and Hadoop
- Discover how to put Transfer Learning to use
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (15 minutes) | Course Outline |
| Join Our Online Classroom! | ||
| Exercise: Meet Your Classmates and Instructor | ||
| Your First Day | ||
| 2. | Machine Learning 101 (40 minutes) | What Is Machine Learning? |
| AI/Machine Learning/Data Science | ||
| Exercise: Machine Learning Playground | ||
| How Did We Get Here? | ||
| Exercise: YouTube Recommendation Engine | ||
| Types of Machine Learning | ||
| Are You Getting It Yet? | ||
| What Is Machine Learning? Round 2 | ||
| Section Review | ||
| Monthly Coding Challenges, Free Resources and Guides | ||
| 3. | Machine Learning and Data Science Framework (01 hour 07 minutes) | Section Overview |
| Introducing Our Framework | ||
| 6 Step Machine Learning Framework | ||
| Types of Machine Learning Problems | ||
| Types of Data | ||
| Types of Evaluation | ||
| Features In Data | ||
| Modelling – Splitting Data | ||
| Modelling – Picking the Model | ||
| Modelling – Tuning | ||
| Modelling – Comparison | ||
| Overfitting and Underfitting Definitions | ||
| Experimentation | ||
| Tools We Will Use | ||
| Optional: Elements of AI | ||
| 4. | The 2 Paths (04 minutes) | The 2 Paths |
| Python + Machine Learning Monthly | ||
| Endorsements on LinkedIn | ||
| 5. | Data Science Environment Setup (01 hour 48 minutes) | Section Overview |
| Introducing Our Tools | ||
| What is Conda? | ||
| Conda Environments | ||
| Mac Environment Setup | ||
| Mac Environment Setup 2 | ||
| Windows Environment Setup | ||
| Windows Environment Setup 2 | ||
| Linux Environment Setup | ||
| Sharing your Conda Environment | ||
| Jupyter Notebook Walkthrough | ||
| Jupyter Notebook Walkthrough 2 | ||
| Jupyter Notebook Walkthrough 3 | ||
| 6. | Pandas: Data Analysis (01 hour 37 minutes) | Section Overview |
| Downloading Workbooks and Assignments | ||
| Pandas Introduction | ||
| Series, Data Frames and CSVs | ||
| Data from URLs | ||
| Describing Data with Pandas | ||
| Selecting and Viewing Data with Pandas | ||
| Selecting and Viewing Data with Pandas Part 2 | ||
| Manipulating Data | ||
| Manipulating Data 2 | ||
| Manipulating Data 3 | ||
| Assignment: Pandas Practice | ||
| How To Download The Course Assignments | ||
| 7. | NumPy (02 hours 11 minutes) | Section Overview |
| NumPy Introduction | ||
| Quick Note: Correction In Next Video | ||
| NumPy DataTypes and Attributes | ||
| Creating NumPy Arrays | ||
| NumPy Random Seed | ||
| Viewing Arrays and Matrices | ||
| Manipulating Arrays | ||
| Manipulating Arrays 2 | ||
| Standard Deviation and Variance | ||
| Reshape and Transpose | ||
| Dot Product vs Element Wise | ||
| Exercise: Nut Butter Store Sales | ||
| Comparison Operators | ||
| Sorting Arrays | ||
| Turn Images Into NumPy Arrays | ||
| Exercise: Imposter Syndrome | ||
| Assignment: NumPy Practice | ||
| Optional: Extra NumPy resources | ||
| 8. | Matplotlib: Plotting and Data Visualization (02 hours 18 minutes) | Section Overview |
| Matplotlib Introduction | ||
| Importing And Using Matplotlib | ||
| Anatomy Of A Matplotlib Figure | ||
| Scatter Plot And Bar Plot | ||
| Histograms And Subplots | ||
| Subplots Option 2 | ||
| Quick Tip: Data Visualizations | ||
| Plotting From Pandas DataFrames | ||
| Quick Note: Regular Expressions | ||
| Plotting From Pandas DataFrames 2 | ||
| Plotting from Pandas DataFrames 3 | ||
| Plotting from Pandas DataFrames 4 | ||
| Plotting from Pandas DataFrames 5 | ||
| Plotting from Pandas DataFrames 6 | ||
| Plotting from Pandas DataFrames 7 | ||
| Customizing Your Plots | ||
| Customizing Your Plots 2 | ||
| Saving And Sharing Your Plots | ||
| Assignment: Matplotlib Practice | ||
| 9. | Scikit-learn: Creating Machine Learning Models (07 hours 56 minutes) | Section Overview |
| Scikit-learn Introduction | ||
| Quick Note: Upcoming Video | ||
| Refresher: What Is Machine Learning? | ||
| Quick Note: Upcoming Videos | ||
| Scikit-learn Cheatsheet | ||
| Typical scikit-learn Workflow | ||
| Optional: Debugging Warnings In Jupyter | ||
| Getting Your Data Ready: Splitting Your Data | ||
| Quick Tip: Clean, Transform, Reduce | ||
| Getting Your Data Ready: Convert Data To Numbers | ||
| Note: Update to next video (OneHotEncoder can handle NaN/None values) | ||
| Getting Your Data Ready: Handling Missing Values With Pandas | ||
| Extension: Feature Scaling | ||
| Note: Correction in the upcoming video (splitting data) | ||
| Getting Your Data Ready: Handling Missing Values With Scikit-learn | ||
| NEW: Choosing The Right Model For Your Data | ||
| NEW: Choosing The Right Model For Your Data 2 (Regression) | ||
| Quick Note: Decision Trees | ||
| Quick Tip: How ML Algorithms Work | ||
| Choosing The Right Model For Your Data 3 (Classification) | ||
| Fitting A Model To The Data | ||
| Making Predictions With Our Model | ||
| predict() vs predict_proba() | ||
| NEW: Making Predictions With Our Model (Regression) | ||
| NEW: Evaluating A Machine Learning Model (Score) Part 1 | ||
| NEW: Evaluating A Machine Learning Model (Score) Part 2 | ||
| Evaluating A Machine Learning Model 2 (Cross Validation) | ||
| Evaluating A Classification Model 1 (Accuracy) | ||
| Evaluating A Classification Model 2 (ROC Curve) | ||
| Evaluating A Classification Model 3 (ROC Curve) | ||
| Reading Extension: ROC Curve + AUC | ||
| Evaluating A Classification Model 4 (Confusion Matrix) | ||
| NEW: Evaluating A Classification Model 5 (Confusion Matrix) | ||
| Evaluating A Classification Model 6 (Classification Report) | ||
| NEW: Evaluating A Regression Model 1 (R2 Score) | ||
| NEW: Evaluating A Regression Model 2 (MAE) | ||
| NEW: Evaluating A Regression Model 3 (MSE) | ||
| Machine Learning Model Evaluation | ||
| NEW: Evaluating A Model With Cross Validation and Scoring Parameter | ||
| NEW: Evaluating A Model With Scikit-learn Functions | ||
| Improving A Machine Learning Model | ||
| Tuning Hyperparameters | ||
| Tuning Hyperparameters 2 | ||
| Tuning Hyperparameters 3 | ||
| Note: Metric Comparison Improvement | ||
| Quick Tip: Correlation Analysis | ||
| Saving And Loading A Model | ||
| Saving And Loading A Model 2 | ||
| Putting It All Together | ||
| Putting It All Together 2 | ||
| Scikit-Learn Practice | ||
| 10. | Supervised Learning: Classification + Regression (16 seconds) | Milestone Projects! |
| 11. | Milestone Project 1: Supervised Learning (Classification) (03 hours 35 minutes) | Section Overview |
| Project Overview | ||
| Project Environment Setup | ||
| Optional: Windows Project Environment Setup | ||
| Step 1~4 Framework Setup | ||
| Getting Our Tools Ready | ||
| Exploring Our Data | ||
| Finding Patterns | ||
| Finding Patterns 2 | ||
| Finding Patterns 3 | ||
| Preparing Our Data For Machine Learning | ||
| Choosing The Right Models | ||
| Experimenting With Machine Learning Models | ||
| Tuning/Improving Our Model | ||
| Tuning Hyperparameters | ||
| Tuning Hyperparameters 2 | ||
| Tuning Hyperparameters 3 | ||
| Quick Note: Confusion Matrix Labels | ||
| Evaluating Our Model | ||
| Evaluating Our Model 2 | ||
| Evaluating Our Model 3 | ||
| Finding The Most Important Features | ||
| Reviewing The Project | ||
| 12. | Milestone Project 2: Supervised Learning (Time Series Data) (03 hours 12 minutes) | Section Overview |
| Project Overview | ||
| Downloading the data for the next two projects | ||
| Project Environment Setup | ||
| Step 1~4 Framework Setup | ||
| Exploring Our Data | ||
| Exploring Our Data 2 | ||
| Feature Engineering | ||
| Turning Data Into Numbers | ||
| Filling Missing Numerical Values | ||
| Filling Missing Categorical Values | ||
| Fitting A Machine Learning Model | ||
| Splitting Data | ||
| Challenge: What’s wrong with splitting data after filling it? | ||
| Custom Evaluation Function | ||
| Reducing Data | ||
| RandomizedSearchCV | ||
| Improving Hyperparameters | ||
| Preproccessing Our Data | ||
| Making Predictions | ||
| Feature Importance | ||
| 13. | Data Engineering (57 minutes) | Data Engineering Introduction |
| What Is Data? | ||
| What Is A Data Engineer? | ||
| What Is A Data Engineer 2? | ||
| What Is A Data Engineer 3? | ||
| What Is A Data Engineer 4? | ||
| Types Of Databases | ||
| Quick Note: Upcoming Video | ||
| Optional: OLTP Databases | ||
| Optional: Learn SQL | ||
| Hadoop, HDFS and MapReduce | ||
| Apache Spark and Apache Flink | ||
| Kafka and Stream Processing | ||
| 14. | Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2 (07 hours 07 minutes) | Section Overview |
| Deep Learning and Unstructured Data | ||
| Setting Up With Google | ||
| Setting Up Google Colab | ||
| Google Colab Workspace | ||
| Uploading Project Data | ||
| Setting Up Our Data | ||
| Setting Up Our Data 2 | ||
| Importing TensorFlow 2 | ||
| Optional: TensorFlow 2.0 Default Issue | ||
| Using A GPU | ||
| Optional: GPU and Google Colab | ||
| Optional: Reloading Colab Notebook | ||
| Loading Our Data Labels | ||
| Preparing The Images | ||
| Turning Data Labels Into Numbers | ||
| Creating Our Own Validation Set | ||
| Preprocess Images | ||
| Preprocess Images 2 | ||
| Turning Data Into Batches | ||
| Turning Data Into Batches 2 | ||
| Visualizing Our Data | ||
| Preparing Our Inputs and Outputs | ||
| Optional: How machines learn and what’s going on behind the scenes? | ||
| Building A Deep Learning Model | ||
| Building A Deep Learning Model 2 | ||
| Building A Deep Learning Model 3 | ||
| Building A Deep Learning Model 4 | ||
| Summarizing Our Model | ||
| Evaluating Our Model | ||
| Preventing Overfitting | ||
| Training Your Deep Neural Network | ||
| Evaluating Performance With TensorBoard | ||
| Make And Transform Predictions | ||
| Transform Predictions To Text | ||
| Visualizing Model Predictions | ||
| Visualizing And Evaluate Model Predictions 2 | ||
| Visualizing And Evaluate Model Predictions 3 | ||
| Saving And Loading A Trained Model | ||
| Training Model On Full Dataset | ||
| Making Predictions On Test Images | ||
| Submitting Model to Kaggle | ||
| Making Predictions On Our Images | ||
| Finishing Dog Vision: Where to next? | ||
| 15. | Storytelling + Communication: How To Present Your Work (26 minutes) | Section Overview |
| Communicating Your Work | ||
| Communicating With Managers | ||
| Communicating With Co-Workers | ||
| Weekend Project Principle | ||
| Communicating With Outside World | ||
| Storytelling | ||
| Communicating and sharing your work: Further reading | ||
| 16. | Career Advice + Extra Bits (01 hour 21 minutes) | Endorsements On LinkedIn |
| Quick Note: Upcoming Video | ||
| What If I Don’t Have Enough Experience? | ||
| Learning Guideline | ||
| Quick Note: Upcoming Videos | ||
| JTS: Learn to Learn | ||
| JTS: Start With Why | ||
| Quick Note: Upcoming Videos | ||
| CWD: Git + Github | ||
| CWD: Git + Github 2 | ||
| Contributing To Open Source | ||
| Contributing To Open Source 2 | ||
| Exercise: Contribute To Open Source | ||
| Coding Challenges | ||
| 17. | Learn Python (04 hours 12 minutes) | What Is A Programming Language |
| Python Interpreter | ||
| How To Run Python Code | ||
| Our First Python Program | ||
| Latest Version Of Python | ||
| Python 2 vs Python 3 | ||
| Exercise: How Does Python Work? | ||
| Learning Python | ||
| Python Data Types | ||
| How To Succeed | ||
| Numbers | ||
| Math Functions | ||
| DEVELOPER FUNDAMENTALS: I | ||
| Operator Precedence | ||
| Exercise: Operator Precedence | ||
| Optional: bin() and complex | ||
| Variables | ||
| Expressions vs Statements | ||
| Augmented Assignment Operator | ||
| Strings | ||
| String Concatenation | ||
| Type Conversion | ||
| Escape Sequences | ||
| Formatted Strings | ||
| String Indexes | ||
| Immutability | ||
| Built-In Functions + Methods | ||
| Booleans | ||
| Exercise: Type Conversion | ||
| DEVELOPER FUNDAMENTALS: II | ||
| Exercise: Password Checker | ||
| Lists | ||
| List Slicing | ||
| Matrix | ||
| List Methods | ||
| List Methods 2 | ||
| List Methods 3 | ||
| Common List Patterns | ||
| List Unpacking | ||
| None | ||
| Dictionaries | ||
| DEVELOPER FUNDAMENTALS: III | ||
| Dictionary Keys | ||
| Dictionary Methods | ||
| Dictionary Methods 2 | ||
| Tuples | ||
| Tuples 2 | ||
| Sets | ||
| Sets 2 | ||
| 18. | Learn Python Part 2 (04 hours 49 minutes) | Breaking The Flow |
| Conditional Logic | ||
| Indentation In Python | ||
| Truthy vs Falsey | ||
| Ternary Operator | ||
| Short Circuiting | ||
| Logical Operators | ||
| Exercise: Logical Operators | ||
| is vs == | ||
| For Loops | ||
| Iterables | ||
| Exercise: Tricky Counter | ||
| range() | ||
| enumerate() | ||
| While Loops | ||
| While Loops 2 | ||
| break, continue, pass | ||
| Our First GUI | ||
| DEVELOPER FUNDAMENTALS: IV | ||
| Exercise: Find Duplicates | ||
| Functions | ||
| Parameters and Arguments | ||
| Default Parameters and Keyword Arguments | ||
| return | ||
| Exercise: Tesla | ||
| Methods vs Functions | ||
| Docstrings | ||
| Clean Code | ||
| *args and **kwargs | ||
| Exercise: Functions | ||
| Scope | ||
| Scope Rules | ||
| global Keyword | ||
| nonlocal Keyword | ||
| Why Do We Need Scope? | ||
| Pure Functions | ||
| map() | ||
| filter() | ||
| zip() | ||
| reduce() | ||
| List Comprehensions | ||
| Set Comprehensions | ||
| Exercise: Comprehensions | ||
| Python Exam: Testing Your Understanding | ||
| Modules in Python | ||
| Quick Note: Upcoming Videos | ||
| Optional: PyCharm | ||
| Packages in Python | ||
| Different Ways To Import | ||
| Next Steps | ||
| Bonus Resource: Python Cheatsheet | ||
| 19. | Extra: Learn Advanced Statistics and Mathematics for FREE! (17 seconds) | Statistics and Mathematics |
| 20. | Where To Go From Here? (03 minutes) | Become An Alumni |
| Thank You | ||
| Thank You Part 2 | ||
| Course Review | ||
| The Final Challenge | ||
| 21. | BONUS SECTION (16 seconds) | Special Bonus Lecture |
Resources Required
- You don’t need any prior knowledge (not even Math and Statistics)
- A Linux, Windows, or Mac computer with an internet connection
Featured Review
Alie Antar (5/5) : Love the structure and flow of this course. The details are incredibly laid out, and content is explained from the ground up!
Pros
- Nelson G. Gonzalez (5/5): I didn’t expect this course would be so practical and so hands on, I come from taking another bootcamp with a lot of theory (and practice as well, excellent by the way).
- Cherukuri Thanuja (5/5): For Beginners, this is the best Data Science course on Udemy.
- SURAJ TADE (5/5): The way it is being taught and all the learning part, it’s all wonderful.
- Jonathan (5/5): Excellent intro to comp-sci, wonderfully covers the basics in pandas, matplotlib, numpy, Jupyter notebook.
Cons
- David Boland (1/5): Just one algorithm is presented – the random forest and no explanation as to what this algorithm is for is given.
- Dominik Gschrei (2/5): The runtime is artificially inflated to a ridiculous degree, seemingly to get to the advertisable 30+hrs of runtime.
- Rami Aldoush (1/5): Seriously, this Course is not good Machine Learning section is very poor my advise find another course
- Simon Crayond’bois (1/5): Just started the course, it is EXTREMELY slow (and we all know they do this to artificially increase the global duration).
About the Author
The instructor of this course is Andrei Neagoie who is the founder of zerotomastery.io. With a 4.6 instructor rating and 2,35,184 reviews on Udemy, he offers 26 courses and has taught 9,20,475 students so far.
- Some of the most popular programming and technical courses online are taught by Andrei.
- He is now the creator of ZTM Academy, one of the education platforms with the quickest rate of growth worldwide.
- ZTM Academy is renowned for its top-notch instructors and high student success rates.
- Some of the greatest tech businesses in the world, including Apple, Google, Tesla, Amazon, JP Morgan, IBM, UNIQLO, etc. have hired alumni from his program.
- For many years, he worked as a senior software developer in Silicon Valley and Toronto.
- Now, he is using what he has learned to teach programming skills and to guide you in discovering the incredible job prospects that being a developer in life enables.
- As a self-taught programmer, he is aware that there are a bewildering array of online tutorials, books, and courses that are unnecessarily verbose and ineffective in imparting the necessary skills.
Comparison Table
| Parameters | Complete Machine Learning & Data Science Bootcamp 2023 | Tensorflow 2.0: Deep Learning and Artificial Intelligence | TensorFlow Developer Certificate in 2023: Zero to Mastery |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 44 hours | 22.5 hours | 63.5 hours |
| Rating | 4.6 /5 | 4.6 /5 | 4.7 /5 |
| Student Enrollments | 80,313 | 40,059 | 41,054 |
| Instructors | Andrei Neagoie | Lazy Programmer Inc. | Andrei Neagoie |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
Complete Machine Learning & Data Science Bootcamp 2023: FAQs
Ques. Is a Bootcamp worth it for Data Science?
Ans. Yes, Data Science Bootcamps generally cost less and take less time to complete than a traditional Data Science degree program. Bootcamps are intensive in nature and they offer more practical and hands-on training.
Ques. Can you get a job after a Data Science Bootcamp?
Ans. Data Science Bootcamp offers the opportunity to connect with recruiters and Data Scientists. Besides helping students develop portfolios, they teach them all the skills and tools that can help them land jobs after graduation.
Ques. Is it worth learning Machine Learning in 2023?
Ans. There are many career options available to Machine Learning experts. They can become Machine Learning Engineers, Data Scientists, NLP Scientists, Business Intelligence Developers, or Human-Centered Machine Learning Designers with a background in Machine Learning.
Ques. Is Python required for Complete Machine Learning & Data Science Bootcamp?
Ans. Python is required for Complete Machine Learning & Data Science Bootcamp, but the course includes a track for those who want to learn Python and Machine Learning before getting started.
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