- 1205 students
- 125 lessons
- 0 quizzes
- 10 week duration
Master the basics of Pythons and Machine Learning from scratch. If you are looking for good start in Machine Learning this is the course for you to start with. This course is design in such a way which need only minimum or no programming experience from participants.
This course will walk you towards every step of Python and Machine learning starting from the history, setup and exercises in Python and Machine learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course focuses more on practical aspect rather than theoretical aspect of programming.
In this course you will cover the following topics in much greater details.:
- Introduction to Python
- Python Data Types
- Control Flow
- Function, Modules and Packages
- I/O errors and Exceptions
- Standard Libraries
- Machine Learning
- Introduction to Machine Learning
- Introduction to Scikit
- Installing Scikit – learn
- Introduction and Installing Jupyter Notebook
- Techniques Of Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Types of Machine Learning Algorithms
- and many more.
Who is the target audience?
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
Introduction to Python
- Introduction to Python
- History of Python
- Who can take this Course ? (Prerequisites)
- Installing latest Python with editor
- Variables in Python
- Simple Demonstration on variables
- Operators in Python
- Operators and Operands
- Types of Operators
- Arithmetic Operators
- Hands on exercise on Arithmetic Operators
- Overview on Comparison and Relational Operators
- Hands on exercise on Comparison and Relational Operators
- Overview on Assignment Operator
Control Flow Statements in Python
- What are going to learn in this sections ?
- “if” Statement in Python
- Hands on exercise on “if” statement
- Hands on exercise on “if” statement — 2
- “while” statement in python
- “for” statement in Python
- Hands on exercise on “for” statement
- “break” keyword in Python
- What are Functions in Python ?
- Hands on exercise on functions in Python
- range() function in Python
- Hands on exercise on “range()” function
- What are arbitrary arguments ?
- “lambda” expressions in Pythons
Data Structure in Python
Classes in Python
Modules in Python
Input/Outputs in Python
Exception handling in Python
Setting up Jupyter Notebook and Scikit - Learn
Numpy package for scientific computing with Python
- What is Numpy ?
- Installation and use of Numpy
- Hands on exercise on “Mathematical calculation using Numpy”
- Basic functions in Numpy
- Hands on exercise on “arange() funtion in Numpy”
- Exercise on Array Creation by Numpy
- Using shortcut key to read documentations
- Mathematical Operations using Numpy
- Elementwise products using Numpy
- Matrix calculations using Numpy
- Universal functions in Numpy
- Indexing, Slicing and Iteration using Numpy
- Multidimension Array in Numpy
- How to change shape of an Array
- Copies and Views of Array in Numpy
- Hands on exercise on Numpy 1
- Hands on exercise on Numpy 2
- Hands on exercise on Numpy 3
Matplotlib 2D Library for Python and Machine Learning
- What is Matplotlib ?
- Installing Matplotlib
- Anatomy of Matplotlib figure
- Importing matplot and numpy in python code
- Creating blank figure using matplotlib
- Axis, Axes, and Artist in Matplotlib
- Simple PyPlot using plot() function
- Different types of Plots in Matplotlib
- Hands on exercise on “plot()” function – 1
- Hands on exercise on “plot()” function – 2
- Adding marker, color, markersize, linewidth to Matplotlib
- Properties in Pyplot
- Adding Subplot to figure
- hist() function to determine frequency
Pandas Library for Machine Learning
- What is a Pandas Library ?
- Importing Pandas to Jupyter notebook
- Simple Hands On experience with Pandas
- date_range() function in Pandas
- DataFrame to display columns names with Pandas
- How to make selection from given Data Sets
- Boolean indexing with Pandas
- Dealing with NaN
- Finding statistics from a given dataset
- Writing Plot from given DataFrame
- Hands On : Reading and Writing Files in Pandas 1
- Hands On : Reading and Writing Files in Pandas 2
- Hands On : Reading and Writing Files in Pandas 3
Introduction to Machine Learning
K - Neighbours Classification
Logistic Regression Classifier
0.00 average based on 0 ratings