Machine Learning & AI
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
+ View more
Course overview
Course Description
The goal of machine learning is to program computers to use example data or past experience to solve a given problem..
This course provides an introduction to machine learning i.e. how to make computers learn from data. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
Certificate
On successful completion of the course participants will be awarded participation certificate from DigiPAKISTAN. Also prepare for International Exam.
Duration & Frequency
Total Duration of the course is 3 months
What will i learn?
- Master Machine Learning and use it on the job
- Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
- Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use
- Present Data Science projects to management and stakeholders
- Learn which Machine Learning model to choose for each type of problem
- Real life case studies and projects to understand how things are done in the real world
- Learn best practices when it comes to Data Science Workflow
- Learn how to program in Python using the latest Python 3
- A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
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.
- All tools used in this course are free for you to use.
Curriculum for this course
Introduction
-
Introduction
-
Introduction Part 2
-
Introduction Part 3
Data Science using Python Part 1
-
Python Fundamentals Part 1
-
Python Fundamentals Part 2
-
Python Fundamentals Part 3
-
What is program
-
Programming paradigms
-
Compiler Vs Interpreter
-
Setting up Environment
-
Overview of Jupiter notebook
-
Keywords in python
-
Print Statements
-
Variables
-
Operators
-
Expressions
-
Eliminating Ambiguity
-
String Concatenation
-
Structured Programming
-
If Statement
-
Comparison Operator
-
Check greater of two number
-
And Operator
-
Or Operator
Data Science using Python Part 2
-
Nested If else
-
Concatenation
-
Comments
Data Science using Python Part 3
-
Introduction to List
-
Slicing in a list
-
Slicing with negative indices
-
Deleting an element from list
Data Science using Python Part 4
-
Tuple
-
For loop
-
Search in a loop
-
Some functions
-
Dictionary
Programming Fundamentals
-
Introduction to functions
-
Functions with positional arguments
-
Keyword arguments
-
Default arguments
-
Unknown number of arguments with tuples
-
Unknown number of arguments with dictionaries
-
Local Vs global variables
-
Call back functions
-
While loops
-
Classes
-
Object Oriented Analysis and Design
-
Pillars of OOP
-
Encapsulation
-
Inheritance
-
Method Overriding
-
Polymorphism
-
Modules
-
I/O
-
Exception Handling
-
Random module
-
Math and sys module
-
Docstring
-
String formatting
-
Strings
-
DateTime module
Data Science
-
Module overview
-
Introduction to numpy
-
Vectorized operations
-
Installing numpy
-
Arrays Vs List
-
Which is faster, an experiment
-
Lazy evaluation
-
Magic commands of jupyter
-
List comprehension
-
Mulitplying list in python
-
Multiplying arrays in numpy
-
Ndarray
-
Examples of array creation
-
Demonstration of array creation methods
-
Summary of array creation methods
-
Shape, ndim, dtype attributes
-
Shape example
-
Data types in numpy
-
Vectorization, indexing and slicing
-
Element-wise array functions
-
np.where()
-
Transpose, mean, cumsum, cumprod
-
Boolean funnctions
-
Boolean funnctions
-
Sorting, unique and filing operations
-
Linear algebra operatoins
-
Random number generation
-
Reshape and flatten
-
Concatenate, hstack, vstack, np.r_ and np.c_
-
Ravel vs flatten
-
Drawing patterns using numpy
-
Introduction to pandas
-
Series
-
DataFrame
-
loc/iloc
-
Adding, removing, updating values
-
Apply function
-
Some useful functions
-
Reading and writing to files
-
Dropping rows from data frame
-
Dropping columns
-
Reading data from files
-
Retrieving data from database
-
Data preprocessing and cleaning
-
Dropna
-
Dropna with threshold
-
Fillna
-
Removing duplicates
-
Map
-
Discretization and binning
-
Describing and finding outliers
-
Merge two frames
-
Join two frames
-
Concatenate frames
-
Introduction to matplotlib
-
Customizing plots
-
Specifying colors
-
Specifying limits
-
Scatter plots
-
Bargraph
-
Histograms
-
Box plots
-
Pie chart
-
Heatmaps
-
Various types of tasks
Machine Learning
-
Machine Learning
-
Machine Learning
-
Components of machine learning
-
Tasks of machine learning
-
Various types of tasks
-
Introduction to sk-learn
-
Training and testing
-
Building a mdodel in scikit-learn
-
Performance measures
-
Precision Recall
-
Overfitting and underfitting
-
Machine LEarnign classifiers - 2
-
sk-learn examples
Semantic Web
-
Semantic Web
-
Ontology
-
Requirements for good ontology language
-
RDF
-
RDF(S)
-
OWL
-
OWL Example
-
Protege
-
Creating classes
-
Creating data type properties
-
Creating object properties
-
Creating individuals
-
Visualization
-
SPARQL
-
Introduction to JENA
-
Setting up JENA
-
Introduction to Java
-
Creating RDF Resources and Statements
-
Alternate way of creating resources
-
Listing RDF statements
-
Writing model
-
Reading RDF file
Test Section
-
SEO test lesson
Preview
+ View more
Other related courses
Updated Thu, 09-Jun-2022
Updated Thu, 09-Jun-2022
Updated Thu, 09-Jun-2022
Updated Thu, 09-Jun-2022
Updated Thu, 09-Jun-2022
About instructor
Muheeb Abid
Show full biography
