Students will learn how to solve the Complex Numbers in Python
In this course, mathematics of complex numbers is explained in detailed. Mathematical operations on Complex Numbers are explained with examples. All these problems are also solved in Python. Important python modules for this course such as NumPy and Matplotlib are also the part of this course. After the completion of this course students will be able to solve Complex Number problems manually and by python.
Visualize multiple forms of both 2D and 3D graphs, like line graphs, scatter plots, bar charts, and more
Create live graphs
Multiple Plots in a Graph
PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot
Customize graphs, modifying colors, lines, fonts, and more
More and more people are realizing the vast benefits and uses of analyzing big data. However, the majority of people lack the skills and the time needed to understand this data in its original form. That’s where data visualization comes in; creating easy to read, simple to understand graphs, charts and other visual representations of data. Python 3 and Matplotlib are the most easily accessible and efficient to use programs to do just this.
Learn Big Data Python
Visualize multiple forms of 2D and 3D graphs; line graphs, scatter plots, bar charts, etc.
Load and organised data from various sources for visualization
Create and customize live graphs
Add finesse and style to make your graphs visually appealing
Python Data Visualization made Easy
With over 58 lectures and 6 hours of content, this course covers almost every major chart that Matplotlib is capable of providing. Intended for students who already have a basic understanding of Python, you’ll take a step-by-step approach to create line graphs, scatter plots, stack plots, pie charts, bar charts, 3D lines, 3D wire frames, 3D bar charts, 3D scatter plots, geographic maps, live updating graphs, and virtually anything else you can think of!
Starting with basic functions like labels, titles, window buttons and legends, you’ll then move onto each of the most popular types of graph, covering how to import data from both a CSV and NumPy. You’ll then move on to more advanced features like customized spines, styles, annotations, averages and indicators, geographical plotting with Basemap and advanced wire frames.
This course has been specially designed for students who want to learn a variety of ways to visually display python data. On completion of this course, you will not only have gained a deep understanding of the options available for visualizing data, but you’ll have the know-how to create well presented, visually appealing graphs too.
Tools Used
Python 3: Python is a general purpose programming language which a focus on readability and concise code, making it a great language for new coders to learn. Learning Python gives a solid foundation for learning more advanced coding languages, and allows for a wide variety of applications.
Matplotlib: Matplotlib is a plotting library that works with the Python programming language and its numerical mathematics extension ‘NumPy’. It allows the user to embed plots into applications using various general purpose tool kits (essentially, it’s what turns the data into the graph).
IDLE: IDLE is an Integrated Development Environment for Python; i.e where you turn the data into the graph. Although you can use any other IDE to do so, we recommend the use of IDLE for this particular course.
1. Matplotlib Introduction
2. PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot
3. Multiple Plots in a Graph
Developing & deploying AI & Machine Learning applications using python AI & ML frameworks
how to use most popular AI & ML frameworks: NumPy ,SciPy, Scikit-Learn, Matplotlib
How to use Jupyter/iPython notebook for interactive development
How to create multi-user notebook enviroment using JupyterHub
When you want to learn a new technology for professional use, there are two mutually exclusive options, either you learn it yourself or you go for instructor based training.
Self learning is least expensive but lot of time results in wasting time in finding right contents, setting up the environment , troubleshooting issues and may make you give up in the middle.
Instructor based training can be expensive at times and need your time commitment.
This course combines the best of both these options. The course is based on one of the most famous books in the field “Python Machine Learning (2nd Ed.)” by Sebastian Raschka and Vahid Mirjalili and provides you video tutorials on how to understand the AI/ML concepts from the books by providing out of box virtual machine with demo examples for each chapter in the book and complete preinstalled setup to execute the code.
You learn the concepts by self learning and get hands on executing the sample code in the virtual machine.
The demo covers following concepts:
In addition to the preinstalled setup and demos, the VM also comes with:
The VM is available on :
Getting Started with Machine Learning
This course is especially for beginners who want to get started their journey in the field of machine learning. This course provides the hands-on experience with the python and scikit learn. So if you are new to the machine learning Get started with this course will be a good choice.
Basic operations in Numpy, Scipy, Pandas, and Matplotlib
Vector, Matrix, and Tensor manipulation
Visualizing data
Reading, writing, and manipulating DataFrames
Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2).
The reason I made this course is because there is a huge gap for many students between machine learning “theory” and writing actual code.
As I’ve always said: “If you can’t implement it, then you don’t understand it”.
Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.
This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.
The goal is that, after you take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.
Suggested Prerequisites:
Machine learning
Prediction Models
Scikit learn
Python
Numpy
Pandas
Matplotlib
This is a practical machine learning course for people who wan to kickstart their career in Machine learning. This course will give you an understanding of what is machine learning and the concepts related to it. The course is structured in the following way:
At the end of this course you will learn how to create a simple pipeline for a prediction model and make it feasible for real time deployment.
