Machine Learning Using Python complete course from Absolute beginner to Advanced level

About Course

Learn the fundamental concepts of artificial intelligence and machine learning in this free online course.

Artificial intelligence with machine learning (ML) is one of the biggest revolutions in the software industry.

This course is a ‘soft’ starting point that will walk you through the fundamental theoretical concepts of machine learning. We will open the mysterious ML black box, explore its potential and become more familiar with terms used in the industry.

The course is designed for absolute beginners and has no prerequisites.

The concept of AI and ML can be intimidating for beginners and specifically for people without any prior background in the complex fields of mathematics and programming. This course will act as a starting point to guide you through the fundamental theoretical concepts of AI and ML.

It is important to mention that there are no specific requirements for starting this course and we have designed it for absolute beginners. So what are you waiting for?

Start Course Now.

Outline the connection between artificial intelligence, machine learning and deep learning
Describe the concept of supervised and unsupervised learning
Recognise supervised learning building blocks of features and labels and provide examples
Explain the process of training a model in supervised learning
Recognise the challenges of underfitting and overfitting
Identify classification and regression tasks
Outline clustering and dimension reduction
Describe reinforcement learning

Course Content

MACHINE LEARNING PATH/ROADMAP

Steps For Learning Machine learning.

00:00
Mathematics or Calculus for Machine Learning { bonus}

00:00

MODULE 1. INTRODUCTION TO MACHINE LEARNING. (LEVEL 1 )

MODULE 2: MACHINE LEARNING ( ML ) BACKGROUND.

MODULE 3: MACHINE LEARNING TERMINOLOGIES, MODELS AND FEATURES.

MODULE 4: CLASSIFICATIONS OF MACHINE LEARNING SYSTEMS

MODULE 5: END OF COURSE INTRODUCTION .

MODULE 6: MACHINE LEARNING USING PYTHON AND PANDAS. ( LEVEL 2 )
Learn fundamentals of Python and the pandas library for use in data science projects with this free online course. This free online training introduces the Python programming language and the popular Python pandas library for developing various data science and machine learning projects. We will start by setting up a JupyterLab environment, reviewing the basic Python fundamental syntax and then move on to loading large dataset files, performing initial data exploration, cleaning the data, transforming columns and rows and more.

MODULE 7: PANDAS LIBRARY IN MACHINE LEARNING.

MODULE 8: DATA VISUALZATION WITH PYTHON ( LEVEL 3)

MODULE 9: COVERED LESSONS OVERVIEW

MODULE 10: ADVANCED MACHINE LEARNING ( LEVEL 4 ).

Advanced machine learning Outcomes ( practical)

00:00
Introduction to advanced Diploma in machine learning

09:38
Supervised Machine Learning

11:49
Unsupervised Machine Learning

00:00
End to End Machine Learning

13:13
Feature Scaling

00:00
Data Cleaning

07:50
Feature Engineering

00:00
Introduction summary Quiz
Linear Regression

00:00
Gradient Descent

00:00
Regression and Correlation Methods

00:00
Estimating Parameters: Least Squares Methods

12:56
Linear Regression Implementation

00:00
Logistic Regression

00:00
Regression Quiz
Lesson Summary

00:00
KNN Overview

00:00
Parametric and Non-Parametric Models

00:00
EDA on Iris Dataset, Part I

00:00
KNN Quiz.
Understanding KNN

00:00
Implementing the KNN Algorithm from Scratch

00:00
Comparing our Implementation with Sklearn Library

00:00
KNN Hyperparameter Tuning, Using the Cross-validation

00:00
KNN Quiz 2
The Decision Boundary Visualization

00:00
Manhattan Versus Euclidean Distance

00:00
Scaling in KNN

00:00
Curse of Dimensionality

00:00
KNN Use Cases

00:00
KNN Pros and Cons

00:00
Lesson Summary

00:00
Decision Trees Section Overview

00:00
EDA on Adult Dataset

00:00
Entropy and Information Gain, Part I

00:00
Entropy and Information Gain, Part II

00:00
Decision Trees Quiz 1
The Decision Tree ID3 Algorithm, Part I

00:00
The Decision Tree ID3 Algorithm, Part II

00:00
The Decision Tree ID3 Algorithm, Part III

00:00
ID3, Putting Everything Together, Part I

00:00
ID3, Putting Everything Together, Part II

00:00
Evaluating our ID3, Implementation

00:00
Compare with Sklearn Decision Tree

00:00
Visualizing the Tree

10:22
Plot the Features importance

00:00
Decision Trees Hyper-Parameters

00:00
Pruning

00:00
Gain Ratio

00:00
Decision Tree Pros and Cons

00:00
Predicting Income

00:00
Decision Trees Quiz 2.
Lesson Summary

00:00

MODULE 11: DIPLOMA IN MACHINE LEARNING CONTINUATION

Ensemble Learning Section Overview

00:00
What is Ensemble Learning?

00:00
Bootstrap Sampling

00:00
What is Bagging?

00:00
Out-of-Bag Error

00:00
Ensemble Learning and Random Forests Quiz 1
Implementing Random Forests, Part I

00:00
Implementing Random Forests, Part II

00:00
Implementing Random Forests, Part III

00:00
Compare with Sklearn Implementation

00:00
Random Forest Hyper-Parameters

00:00
Ensemble Learning and Random Forests Quiz 2
Random Forest Pros and Cons

00:00
What is Boosting?

00:00
AdaBoost, Part I

00:00
AdaBoost, Part II

00:00
Ensemble Learning and Random Forests Quiz 3
Lesson Summary

00:00
Support Vector Machines

00:00
SVM Outline

00:00
SVM Intuition

00:00
Hard versus Soft Margins

00:00
C Hyper-Parameter

00:00
Kernel Trick

00:00
Kernel Types

00:00
Support Vector Machines Quiz 1
SVM with Linear Dataset

00:00
SVM with Non-Linear Dataset

00:00
SVM with Regression

00:00
Voice Gender Recognition Using SVM

00:00
Unsupervised Machine Learning

00:00
K-Means Algorithm

00:00
Representation of Clusters

00:00
Distance Functions

00:00
Data Standardization

00:00
Support Vector Machines Quiz 2
Lesson Summary

00:00
Principal Component Analysis

00:00
PCA Section Overview

00:00
What Is PCA?

00:00
PCA Drawbacks

00:00
PCA Algorithm Steps

00:00
PCA, Cov versus SVD

00:00
Principal Component Analysis Quiz 1
Overview of PCA Main Applications

00:00
PCA Image Compression Scratch, Part I

00:00
PCA Image Compression Scratch, Part II

00:00
PCA Data Preprocessing from Scratch

00:00
PCA BiPlot

00:00
PCA Feature Scaling and Screeplot

00:00
PCA Supervised and Unsupervised Learning

00:00
PCA for Data Visualization

00:00
Principal Component Analysis Quiz 2
Lesson Summary

00:00

Module 11: DIPLOMA IN BUILDING HIGH ACCURACY MODEL WITH CORE MACHINE LEARNING.

Learning Outcomes

00:00
What Is Machine Learning The Third summary.

00:00
Basics Of Machine Learning summary

00:00
Installing Anaconda – Python Environment summary

00:00
Downloading – Setting Up Atome & Plugins

00:00
Variables In Python

00:00
Functions, Conditionals, & Loops In Python

00:00
High accuracy model Quiz 1
Arrays & Tupples In Python

00:00
Important Modules In Python

00:00
Lesson Summary

00:00
Building A Classification Model

00:00
What Is Scikit-Learn – Why Use It?

00:00
Installing Scikit-Learn & Scipy With Anaconda

00:00
Building A Classification Model Quiz 1
Introduction To The Iris Dataset

00:00
Datasets – Features & Labels Explained

00:00
Building a classification Model Quiz 2
Loading The Iris Dataset – Examining & Preparing Data

00:00
Creating – Teaching Training A Kneighbors Classifier

00:00
Testing Prediction Accuracy With Test Data

00:00
Building Our Own Kneighbors Classifier

00:00
Building A Classification Model Quiz 3
Lesson Summary

00:00
Building A Convolutional Neutral Network

00:00
What Is Keras – Why Use It?

00:00
Installing Keras With Anaconda

00:00
Building a Classification Model Quiz 4
Preparing Dataset For a CNN

00:00
Building – Virtualizing a CNN Using Sequential – Part 1

00:00
Building – Virtualizing a CNN Using Sequential – Part 2

00:00
Building a classification Model Quiz 5
Training CNN – Evaluating Accuracy – Saving To Disk

00:00
Switching Python Environments – Converting To Core ML Model

00:00
Building classification models Quiz 6
Lesson Summary

00:00
Building A Handwriting Recognition App

00:00
Introduction To App – Handwriting

00:00
Building Interface – Wiring Up

00:00
Building Classification Model Quiz 7
Drawing On Screen

00:00
Important Core ML Model – Reading Metadata

00:00
Utilizing Core ML – Vision To Make Prediction

00:00
Handling – Displaying Prediction Results

00:00
Building Classification Model Quiz 8
Lesson Summary

00:00
Core Machine Learning Basics

00:00
Intro To App – Core ML Photo Analysis

00:00
What Is Machine Learning

00:00
What Is Core ML

00:00
Creating Xcode Project

00:00
Building ImageVC In Interface Building – Writing Up

00:00
Building classification model quiz 9
Creating Image Cell & SubClass – Writing Up

00:00
Creating FoodItems Helper File

00:00
Building Classification models Quiz 10
Creating Custom 3×3 Grid UICollectionViewFlowLayout

00:00
Choosing, Downloading, Important Core ML Model

00:00
Passing Images Through Core ML Model

00:00
Building Classification Models 11.
Handling Core ML Prediction Results

00:00
Challenge – Core ML Photo Analysis

00:00
Building core Learning Models Quiz 13
Lesson Summary

00:00

MODULE 12: MACHINE LEARNING PROJECTS.

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About Course

What Will You Learn?

Course Content

MACHINE LEARNING PATH/ROADMAP

Steps For Learning Machine learning.

Mathematics or Calculus for Machine Learning { bonus}

MODULE 1. INTRODUCTION TO MACHINE LEARNING. (LEVEL 1 )

Learning Outcomes

Importance of studying Machine Learning.

The Basic Introduction To machine learning.

MODULE 2: MACHINE LEARNING ( ML ) BACKGROUND.

The rise of artificial Intelligence.

Relationship between Artificial Intelligence ( AI )and Machine Learning (ML)

Classical Programming in Machine Learning

Machine Learning Techies.

Deep Learning in Machine Learning.

Applied vs Generalized Artificial Intelligence.

Why AI is Getting More and More Popular and Stronger.

End of Introduction Quiz.

MODULE 3: MACHINE LEARNING TERMINOLOGIES, MODELS AND FEATURES.

Overview of Machine Learning Terminologies,

The Black Box Metaphor in Machine Learning.

Features and Labels in Machine Learning.

Training a Model in Machine Learning.

Generalization In machine Learning.

Machine Learning Terms Quiz.

Certificate of supervision.

MODULE 4: CLASSIFICATIONS OF MACHINE LEARNING SYSTEMS

Classification 1. Supervised Machine Learning.

Supervised Machine Learning Quiz

Unsupervised Machine Learning.

Reinforcement Machine Learning

Reinforcement Machine Learning QUIZ.

MODULE 5: END OF COURSE INTRODUCTION .

Introduction to Machine Learning Summary.

End of Introduction to Machine Learning assessment Quiz.

Course Objectives.

Introduction to Python and Pandas.

python Libraries

Using JupyterLab.

Python and Pandas Quiz.

Introduction to Python for Machine Learning.

Python Variables and Data Types

Python Strings in ML.

Python Lists in Machine Learning.

Python IF and For Loop Statements in Machine Learning.

Python Functions In Machine Learning.

Python Dictionaries in Machine Learning.

Python Classes, Objects, Attributes, and Methods in Machine Learning.

Python Modules in Machine Learning.

Python Libraries for Data Science Projects in Machine Learning.

Machine Learning using python and pandas Quiz .

Exercise #1 Python

MODULE 7: PANDAS LIBRARY IN MACHINE LEARNING.

Introduction to Pandas Library

Series Data Structure (1D) in Pandas library

DataFrame Data Structure (2D) in Pandas Library

Data Selection in a DataFrame in Pandas Library

Pandas Library Quiz

Exercise #2 – Pandas Series and DataFrame

Introduction to Loading a DataFrame

Kaggle and the Titanic Dataset

Loading a Tabular Data File

Adjusting the Loading Parameters

Previewing the DataFrame

Using Summary Statistics

The Concept of Methods Chaining

Sorting and Ranking

Filtering.

Grouping.

Pandas Features Quiz.

Exercise #3 Data Loading and Analysis.

Introduction to Data Cleaning

Removing Columns or Rows

Removing Duplicate Rows

Renaming Column Labels

Dropping Missing Values

Filling-in Missing Values

Creating Dummy Variables

Exporting Data into Files