Courses Details

What you'll learn

1. The course provides the entire toolbox you need to become a data scientist, Learn Data Science step-by-step, and become a certified Data Scientist


2. Start coding in Python from basic to advance and learn how to use it for statistical analysis


3. Fill up your resume with in-demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, Scikit-Learn and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models, Deep learning, MySQL, Tableau, etc


4. Impress interviewers by showing an understanding of the data science field


5. Learn how to pre-process data with Hands-on Learning.


6. Understand the logic behind Machine Learning, and how to implement it


7. Perform linear and logistic regressions in Python


8. Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn


9. Apply your skills to real-life business cases


10. Unfold the power of Deep Learning


11. Learn MySQL from basics to further implement it in real-life projects


12. Learn from Zero and Use Tableau for sheet creation, dashboard creation, storytelling, etc.


13. Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations, Create 5 major live industry projects with us - OLX like Car Price Prediction, Mask Detection System, Heart Disease Classifier, Calorie Burn Prediction, and Movie Recommendation System



Course content


Requirements

- No prior experience is needed (not even Math and Statistics). We start from the very basics.
- A computer (Linux/Windows/Mac) with internet connection.
- Two paths for those that know programming and those that don't.
- All tools used in this course are free for you to use.

Description

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.


The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:


- Data Exploration and Visualizations

- Neural Networks and Deep Learning

- Model Evaluation and Analysis

- Python 3

- Tensorflow 2.0

- Numpy

- Scikit-Learn

- Data Science and Machine Learning Projects and Workflows

- Data Visualization in Python with MatPlotLib and Seaborn

- Transfer Learning

- Image recognition and classification

- Train/Test and cross validation

- Supervised Learning: Classification, Regression and Time Series

- Decision Trees and Random Forests

- Ensemble Learning

- Hyperparameter Tuning

- Using Pandas Data Frames to solve complex tasks

- Use Pandas to handle CSV Files

- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

- Using Kaggle and entering Machine Learning competitions

- How to present your findings and impress your boss

- How to clean and prepare your data for analysis

- K Nearest Neighbours

- Support Vector Machines

- Regression analysis (Linear Regression/Polynomial Regression)

- How Hadoop, Apache Spark, Kafka, and Apache Flink are used

- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

- Using GPUs with Google Colab


By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.


Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems.


Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.


Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!


Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!



Who this course is for

  • Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python
  • You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable
  • Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
  • You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry
  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
  • You want to learn to use Deep learning and Neural Networks with your projects
  • You want to add value to your own business or company you work for, by using powerful Machine Learning tools.

Author

Arun Paswan


Student feedback

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Reviews

Fiaz Chaudhary
Student

Great course sir!

27 Jan 2023 01:35 PM