we learn lots of dataset how to predict values using different machine learning algorithm. problem-solving oriented subject that learns to apply scientific techniques to practical problems. The course orients on practical classes and self-study during preparation of datasets and programming of data analysis tasks. This course takes you through all the important modules that you need to know about, including machine learning and programming languages.
You’ll receive the completely annotated Jupyter Notebook used in the course.
You’ll be able to define and give examples of the top libraries in Python used to build real world predictive models.
You will be able to create models with the most powerful language for machine learning there is.
You’ll understand the supervised predictive modeling process and learn the core vernacular at a high level.
Requirements
There are no prerequisites however knowledge of Python will be helpful.
A familiarity with the concepts of machine learning would be helpful but aren’t necessary.
Description
Recent Review from Similar Course:
“This was one of the most useful classes I have taken in a long time. Very specific, real-world examples. It covered several instances of ‘what is happening’, ‘what it means’ and ‘how you fix it’. I was impressed.” Steve
Welcome to The Top 5 Machine Learning Libraries in Python. This is an introductory course on the process of building supervised machine learning models and then using libraries in a computer programming language called Python.
What’s the top career in the world? Doctor? Lawyer? Teacher? Nope. None of those.
The top career in the world is the data scientist. Great. What’s a data scientist?
The area of study which involves extracting knowledge from data is called Data Science and people practicing in this field are called as Data Scientists.
Business generate a huge amount of data. The data has tremendous value but there so much of it where do you begin to look for value that is actionable? That’s where the data scientist comes in. The job of the data scientist is to create predictive models that can find hidden patterns in data that will give the business a competitive advantage in their space.
Don’t I need a PhD? Nope. Some data scientists do have PhDs but it’s not a requirement. A similar career to that of the data scientist is the machine learning engineer.
A machine learning engineer is a person who builds predictive models, scores them and then puts them into production so that others in the company can consume or use their model. They are usually skilled programmers that have a solid background in data mining or other data related professions and they have learned predictive modeling.
In the course we are going to take a look at what machine learning engineers do. We are going to learn about the process of building supervised predictive models and build several using the most widely used programming language for machine learning. Python. There are literally hundreds of libraries we can import into Python that are machine learning related.
A library is simply a group of code that lives outside the core language. We “import it” into our work space when we need to use its functionality. We can mix and match these libraries like Lego blocks.
Thanks for your interest in the The Top 5 Machine Learning Libraries in Python and we will see you in the course.
Who this course is for:
If you’re looking to learn machine learning then this course is for you.
PCA using Scikit-Learn (Python Library for Machine Learning)
PCA using MATLAB (Using Statistics and Machine Learning Toolbox)
Requirements
Python Programming
MATLAB Programming
Basics of Data Analysis
Description
Principal Component Analysis (PCA) is an unsupervised learning algorithms and it is mainly used for dimensionality reduction, lossy data compression and feature extraction. It is the mostly used unsupervised learning algorithm in the field of Machine Learning.
In this video tutorial, after reviewing the theoretical foundations of Principal Component Analysis (PCA), this method is implemented step-by-step in Python and MATLAB. Also, PCA is performed on Iris Dataset and images of hand-written numerical digits, using Scikit-Learn (Python library for Machine Learning) and Statistics Toolbox of MATLAB. Also the projects files are available to download at the end of this post.
You will learn about Basics of Python Programming and its features
You will learn and explore on Cloud Client Libraries in GCP
You will get to know the use of Python in Data Science
You will learn on working with ML application using Python
Requirements
If you have an understanding of Basic Python Programming
And Working Knowledge on GCP Cloud services
Description
If you are looking for building the skills on Python programming along with Machine learning, Data science and use of Python in cloud platforms, then this is the course for you!
This course takes you through hands-on approach with python programming using IDLE (Python 3.11 64-bit)
Python is an interpreted, high-level and general-purpose programming language. Python is easy to learn and it is powerful programming language. Python has syntax that allows developers to write programs with fewer lines compared to other programming languages
In this course you will learn about Python and its features, data types and data structures in Python. Looping and conditional statements, functions and modules.
You will also learn the OOPs concept of Python, decorators, generators, exception handling and file handling in Python
In this course you will learn to use the Python Libraries in GCP.
And how to use Python in Machine Learning and Data Science.
Our focus is to teach topics that flow smoothly. The course teaches you everything you need to know about python programming with hands-on examples
This course gives a quick introduction to python programming with an emphasis on its activity lessons
What are you waiting for?
Every day is a missed opportunity.
Hurry up!!!!!!
Who this course is for:
Developers interested in Mastering Python Programming
How to create NumPy arrays, 1D and 2D and nd arrays
Some built-in functions of Numpy,
Slicing in Numpy
Broadcasting, and manipulating arrays
Trigonometric function
Random sampling
String operations
Concatenate function
Sort, Unique, Union, Intersection etc.
Requirements
There are no prerequisites for this course, but it might be helpful if you are familiar with, Python Fundamentals for Data Science, by Saima Aziz
Laptop or PC with Internet Connection
Motivation to learn
Description
Welcome to Learn NumPy for Machine Learning course. My name is Saima Aziz and I will be the instructor for this course.
In this course we will learn how to create Numpy arrays, learn some built-in functions, access values, broadcasting and manipulating arrays etc.
Python is a general purpose and high level programming language. You can use Python for developing desktop GUI applications, websites and web applications. We will learn Numpy from scratch, which is one of the most popular Python programming language library.
Numpy stands for ‘Numerical Python’. It is an open-source Python library used to perform various mathematical and scientific tasks. It contains multi-dimensional arrays and matrices, along with many high-level mathematical functions that operate on these arrays and matrices. Moreover, NumPy forms the foundation of Machine Learning.
NumPy helps to calculate large quantities and common descriptive statistics. It is very useful for handling linear algebra, fourier transforms, and random numbers. It’s high speed coupled with easy to use functions make it a favorite among Data Science and Machine Learning practitioners. Many of its functions are very useful for performing any mathematical or scientific calculation.
I encourage you to take the course from beginning to end to get the full learning experience. Some topics may be very easy for you and others will be challenging, but each topic should offer something of value.
Hope you will enjoy the course!
Who this course is for:
Beginners, who want to learn Numpy, Python library from scratch and curious to learn data science and machine learning.
Understanding Machine Learning for Data Science in python. Best skill to get in free time.
Requirements
For Machine Learning Concept no prerequisite. Anyone can do this course.
Understanding of Data Preprocessing is required for Coding.
After completing this course, you can connect to me on my blog for any question.
Python is required to do the coding part
Description
This course is designed to understand basic Concept of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. Simple Linear Regression Concepts are covered in detail. Coding part is not covered, however wherever possible I have attached the code in the resources.
Now question is why this course?
This Course will not only teach you the basics of Machine learning and Simple Linear Regression. It will also cover in depth mathematical explanation of Cost function and use of Gradient Descent for Simple Linear Regression. Understanding these is must for a solid foundation before entering into Machine Learning World. This foundation will help you to understand all other algorithms and mathematics behind it.
Who this course is for:
Anyone who is looking or dont know from where to start Machine Learning can opt for this course.
This will provide a good foundation in understanding concept of Machine Learning.
Building stronger foundations for learning futuristic technologies such as Machine Learning, Artificial Intelligence etc.
Understand and learn how to create your own efficient python basic programmes
Understand latest python 3
Requirements
Not at All ! An absolute beginner can also start with the course very easily.
A computer – Windows, Mac, and Linux are all supported.
A great zeal to learn this new skill and apply very soon in practical world.
Description
Are you looking for exactly where to begin while learning Python? This course is specifically designed for the students who are genuine seekers for learning python language. The structure is prepared as follows so that it’s most convenient for any beginner to start with:
1. Firstly, I have created detailed Classroom Discussion sessions with my own handwriting so that you understand exactly all the fundamentals and concepts of python.
2. Secondly, Practice sessions of all the discussions have be prepared so that you understand the language better while coding in real life along with me.
3. Lastly, I have shared the practice files and resources to test, whether you have exactly understood whatever I tried to teach in classroom as well as practice sessions.
Most Importantly, It’s Worth your time.
The Structure and Contents of the course are listed in details for your reference before you start this Exciting Journey of Python Programming:
1. Introduction to World of Python
2. Basic Set-up for absolute beginner
3. Classroom Discussion About Strings
4. Practice of Strings Data Type
5. Classroom Discussion about Variables and Data Type
6. Practice of Variables and Data Type
7. Classroom Discussion about Numeric Operators
8. Practice of Numeric Operators
9. Classroom Discussion about Expressions and Operator Precedents
10. Practice of Expressions and Operator Precedents
11. Classroom Discussion about Indexing in Strings
12. Practice of Indexing in a String
13. Classroom Discussion about Slicing out of a String
14. Practice of Slicing out of a String
15. Classroom Discussion about Slicing with Negative Index Positions
16. Practice of Slicing with Negative Number Index
17. Classroom Discussion about Step-in with Strings
18. Practice of Step-in with Strings
19. Classroom Discussion about String Operators
20. Practice of String Operators
21. Classroom Discussion about String Replacements
This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.
Who this course is for:
People that have little or no prior Python knowledge interested in Bioinformatics
This course is completely practical based and is per-requisite for our upcoming Machine Learning course. With around 25 lectures, this course is designed in such a way that you can take spark of Google Colab enabling Jupiter notebook , the best platform to practice Machine Learning and is enriched with all the basic concepts that is required to start with python programming. After completing this course, you should have basic python understanding.
This course is designed for first time python learners. We have created a syllabus which curates for the need of an absolute beginner to Python. We have provided crystal clear explanations and relevant examples wherever needed. This course may help you shape problem solving skill in real life situations.
This course is designed for newbies only. If you are experienced, then this is not for you. Also, if you want to get a job as python developer or want to join internship in python. This course would be helpful for those as well. Feel free to ask if you have any questions. Apart from lectures, I’ll be sharing quizzes later.
If you learn coding, it enhances your personality and also provides you with a new way of looking at things. It enhances your logical reasoning skills. Overall, this course is good for school kids as well.
Once you learn python, it opens the door to many new opportunities like data science, machine learning, data analytics, artificial intelligence, and what not. Grab this opportunity to learn this great language and be awesome.
Note: Nowadays our attention span has reduced drastically. Hence, I would suggest going through each video at least thrice to get optimal benefit out of this course. Keep your pen and paper handy for taking notes or taking down important points.
