Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Language
English
Rating
2 views
Course Overview
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!
Are you ready to start your path to becoming a Data Scientist!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!
We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:
- Programming with Python
- NumPy with Python
- Using pandas Data Frames to solve complex tasks
- Use pandas to handle Excel Files
- Web scraping with python
- Connect Python to SQL
- Use matplotlib and seaborn for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with SciKit Learn, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Natural Language Processing
- Neural Nets and Deep Learning
- Support Vector Machines
- and much, much more!
Enroll in the course and become a data scientist today!
Course Content
- 27 section(s)
- 165 lecture(s)
- Section 1 Course Introduction
- Section 2 Environment Set-Up
- Section 3 Jupyter Overview
- Section 4 Python Crash Course
- Section 5 Python for Data Analysis - NumPy
- Section 6 Python for Data Analysis - Pandas
- Section 7 Python for Data Analysis - Pandas Exercises
- Section 8 Python for Data Visualization - Matplotlib
- Section 9 Python for Data Visualization - Seaborn
- Section 10 Python for Data Visualization - Pandas Built-in Data Visualization
- Section 11 Python for Data Visualization - Plotly and Cufflinks
- Section 12 Python for Data Visualization - Geographical Plotting
- Section 13 Data Capstone Project
- Section 14 Introduction to Machine Learning
- Section 15 Linear Regression
- Section 16 Cross Validation and Bias-Variance Trade-Off
- Section 17 Logistic Regression
- Section 18 K Nearest Neighbors
- Section 19 Decision Trees and Random Forests
- Section 20 Support Vector Machines
- Section 21 K Means Clustering
- Section 22 Principal Component Analysis
- Section 23 Recommender Systems
- Section 24 Natural Language Processing
- Section 25 Neural Nets and Deep Learning
- Section 26 Big Data and Spark with Python
- Section 27 BONUS SECTION: THANK YOU!
What You’ll Learn
- Use Python for Data Science and Machine Learning, Use Spark for Big Data Analysis, Implement Machine Learning Algorithms, Learn to use NumPy for Numerical Data, Learn to use Pandas for Data Analysis, Learn to use Matplotlib for Python Plotting, Learn to use Seaborn for statistical plots, Use Plotly for interactive dynamic visualizations, Use SciKit-Learn for Machine Learning Tasks, K-Means Clustering, Logistic Regression, Linear Regression, Random Forest and Decision Trees, Natural Language Processing and Spam Filters, Neural Networks, Support Vector Machines
Skills covered in this course
Reviews
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NNIYITANGA Eric
This is the best machine Learning course I can recommend for the one who has background on statistics or any mathematical subjects. Thank you for preparing this course sir. much appreciation to your hardwork and courage to share your knowledfe
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FFrancesco Grande
I found this course very useful for reviewing and consolidating the technical foundations of data science. Great job, I highly recommend it.
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MManuel Atienzo
Jose Portilla is an excellent instructor!! He really knows how to explain something that its complicated.
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DDaniela Spireva-Ivanovska
I found it a bit frustrating that during the course I had to look for alternatives for the given examples, since the libraries and the course are older. I would recommend updating the course. Also it would have been nice to have examples of unstructured data and see how python can be useful there.
