# Machine Learning A-Z: Hands-On Python & R In Data Science

- Offered byUDEMY

## Machine Learning A-Z: Hands-On Python & R In Data Science at UDEMY Overview

Duration | 10 hours |

Mode of learning | Online |

Difficulty level | Intermediate |

Credential | Certificate |

## Machine Learning A-Z: Hands-On Python & R In Data Science at UDEMY Highlights

- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Self paced Course

## Machine Learning A-Z: Hands-On Python & R In Data Science at UDEMY Course details

- 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.

- Master Machine Learning on Python & R
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make powerful analysis
- Make robust Machine Learning models
- Create strong added value to your business
- Use Machine Learning for personal purpose
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem

- Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of 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 is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:Part 1 - Data PreprocessingPart 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 - Clustering: K-Means, Hierarchical ClusteringPart 5 - Association Rule Learning: Apriori, EclatPart 6 - Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 - Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 - Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

## Machine Learning A-Z: Hands-On Python & R In Data Science at UDEMY Curriculum

**Welcome to the course!**

Applications of Machine Learning

BONUS: Learning Paths

Why Machine Learning is the Future

Important notes, tips & tricks for this course

This PDF resource will help you a lot

The whole code folder of the course

Updates on Udemy Reviews

Installing Python and Anaconda (Mac, Linux & Windows)

Update: Recommended Anaconda Version

Installing R and R Studio (Mac, Linux & Windows)

BONUS: Meet your instructors

Some Additional Resources

FAQBot!

**-------------------- Part 1: Data Preprocessing --------------------**

Welcome to Part 1 - Data Preprocessing

Get the dataset

Importing the Libraries

Importing the Dataset

For Python learners, summary of Object-oriented programming: classes & objects

Missing Data

Categorical Data

WARNING - Update

Splitting the Dataset into the Training set and Test set

Feature Scaling

And here is our Data Preprocessing Template!

**-------------------- Part 2: Regression --------------------**

Welcome to Part 2 - Regression

**Simple Linear Regression**

How to get the dataset

Dataset + Business Problem Description

Simple Linear Regression Intuition - Step 1

Simple Linear Regression Intuition - Step 2

Simple Linear Regression in Python - Step 1

Simple Linear Regression in Python - Step 2

Simple Linear Regression in Python - Step 3

Simple Linear Regression in Python - Step 4

Simple Linear Regression in R - Step 1

Simple Linear Regression in R - Step 2

Simple Linear Regression in R - Step 3

Simple Linear Regression in R - Step 4

**Multiple Linear Regression**

How to get the dataset

Dataset + Business Problem Description

Multiple Linear Regression Intuition - Step 1

Multiple Linear Regression Intuition - Step 2

Multiple Linear Regression Intuition - Step 3

Multiple Linear Regression Intuition - Step 4

Prerequisites: What is the P-Value?

Multiple Linear Regression Intuition - Step 5

Multiple Linear Regression in Python - Step 1

Multiple Linear Regression in Python - Step 2

Multiple Linear Regression in Python - Step 3

Multiple Linear Regression in Python - Backward Elimination - Preparation

Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !

Multiple Linear Regression in Python - Backward Elimination - Homework Solution

Multiple Linear Regression in Python - Automatic Backward Elimination

Multiple Linear Regression in R - Step 1

Multiple Linear Regression in R - Step 2

Multiple Linear Regression in R - Step 3

Multiple Linear Regression in R - Backward Elimination - HOMEWORK !

Multiple Linear Regression in R - Backward Elimination - Homework Solution

Multiple Linear Regression in R - Automatic Backward Elimination

**Polynomial Regression**

Polynomial Regression Intuition

How to get the dataset

Polynomial Regression in Python - Step 1

Polynomial Regression in Python - Step 2

Polynomial Regression in Python - Step 3

Polynomial Regression in Python - Step 4

Python Regression Template

Polynomial Regression in R - Step 1

Polynomial Regression in R - Step 2

Polynomial Regression in R - Step 3

Polynomial Regression in R - Step 4

R Regression Template

**Support Vector Regression (SVR)**

How to get the dataset

SVR Intuition

SVR in Python

SVR in R

**Decision Tree Regression**

Decision Tree Regression Intuition

How to get the dataset

Decision Tree Regression in Python

Decision Tree Regression in R

**Random Forest Regression**

Random Forest Regression Intuition

How to get the dataset

Random Forest Regression in Python

Random Forest Regression in R

**Evaluating Regression Models Performance**

R-Squared Intuition

Adjusted R-Squared Intuition

Evaluating Regression Models Performance - Homework's Final Part

Interpreting Linear Regression Coefficients

Conclusion of Part 2 - Regression

**-------------------- Part 3: Classification --------------------**

Welcome to Part 3 - Classification

**Logistic Regression**

Logistic Regression Intuition

How to get the dataset

Logistic Regression in Python - Step 1

Logistic Regression in Python - Step 2

Logistic Regression in Python - Step 3

Logistic Regression in Python - Step 4

Logistic Regression in Python - Step 5

Python Classification Template

Logistic Regression in R - Step 1

Logistic Regression in R - Step 2

Logistic Regression in R - Step 3

Logistic Regression in R - Step 4

Logistic Regression in R - Step 5

R Classification Template

**K-Nearest Neighbors (K-NN)**

K-Nearest Neighbor Intuition

How to get the dataset

K-NN in Python

K-NN in R

**Support Vector Machine (SVM)**

SVM Intuition

How to get the dataset

SVM in Python

SVM in R

**Kernel SVM**

Kernel SVM Intuition

Mapping to a higher dimension

The Kernel Trick

Types of Kernel Functions

How to get the dataset

Kernel SVM in Python

Kernel SVM in R

**Naive Bayes**

Bayes Theorem

Naive Bayes Intuition

Naive Bayes Intuition (Challenge Reveal)

Naive Bayes Intuition (Extras)

How to get the dataset

Naive Bayes in Python

Naive Bayes in R

**Decision Tree Classification**

Decision Tree Classification Intuition

How to get the dataset

Decision Tree Classification in Python

Decision Tree Classification in R

**Random Forest Classification**

Random Forest Classification Intuition

How to get the dataset

Random Forest Classification in Python

Random Forest Classification in R

**Evaluating Classification Models Performance**

False Positives & False Negatives

Confusion Matrix

Accuracy Paradox

CAP Curve

CAP Curve Analysis

Conclusion of Part 3 - Classification

**-------------------- Part 4: Clustering --------------------**

Welcome to Part 4 - Clustering

**K-Means Clustering**

K-Means Clustering Intuition

K-Means Random Initialization Trap

K-Means Selecting The Number Of Clusters

How to get the dataset

K-Means Clustering in Python

K-Means Clustering in R

**Hierarchical Clustering**

Hierarchical Clustering Intuition

Hierarchical Clustering How Dendrograms Work

Hierarchical Clustering Using Dendrograms

How to get the dataset

HC in Python - Step 1

HC in Python - Step 2

HC in Python - Step 3

HC in Python - Step 4

HC in Python - Step 5

HC in R - Step 1

HC in R - Step 2

HC in R - Step 3

HC in R - Step 4

HC in R - Step 5

Conclusion of Part 4 - Clustering

**-------------------- Part 5: Association Rule Learning --------------------**

Welcome to Part 5 - Association Rule Learning

**Apriori**

Apriori Intuition

How to get the dataset

Apriori in R - Step 1

Apriori in R - Step 2

Apriori in R - Step 3

Apriori in Python - Step 1

Apriori in Python - Step 2

Apriori in Python - Step 3

**Eclat**

Eclat Intuition

How to get the dataset

Eclat in R

**-------------------- Part 6: Reinforcement Learning --------------------**

Welcome to Part 6 - Reinforcement Learning

**Upper Confidence Bound (UCB)**

The Multi-Armed Bandit Problem

Upper Confidence Bound (UCB) Intuition

How to get the dataset

Upper Confidence Bound in Python - Step 1

Upper Confidence Bound in Python - Step 2

Upper Confidence Bound in Python - Step 3

Upper Confidence Bound in Python - Step 4

Upper Confidence Bound in R - Step 1

Upper Confidence Bound in R - Step 2

Upper Confidence Bound in R - Step 3

Upper Confidence Bound in R - Step 4

**Thompson Sampling**

Thompson Sampling Intuition

Algorithm Comparison: UCB vs Thompson Sampling

How to get the dataset

Thompson Sampling in Python - Step 1

Thompson Sampling in Python - Step 2

Thompson Sampling in R - Step 1

Thompson Sampling in R - Step 2

**-------------------- Part 7: Natural Language Processing --------------------**

Welcome to Part 7 - Natural Language Processing

Natural Language Processing Intuition

How to get the dataset

Natural Language Processing in Python - Step 1

Natural Language Processing in Python - Step 2

Natural Language Processing in Python - Step 3

Natural Language Processing in Python - Step 4

Natural Language Processing in Python - Step 5

Natural Language Processing in Python - Step 6

Natural Language Processing in Python - Step 7

Natural Language Processing in Python - Step 8

Natural Language Processing in Python - Step 9

Natural Language Processing in Python - Step 10

Homework Challenge

Natural Language Processing in R - Step 1

Natural Language Processing in R - Step 2

Natural Language Processing in R - Step 3

Natural Language Processing in R - Step 4

Natural Language Processing in R - Step 5

Natural Language Processing in R - Step 6

Natural Language Processing in R - Step 7

Natural Language Processing in R - Step 8

Natural Language Processing in R - Step 9

Natural Language Processing in R - Step 10

Homework Challenge

**-------------------- Part 8: Deep Learning --------------------**

Welcome to Part 8 - Deep Learning

What is Deep Learning?

**Artificial Neural Networks**

Plan of attack

The Neuron

The Activation Function

How do Neural Networks work?

How do Neural Networks learn?

Gradient Descent

Stochastic Gradient Descent

Backpropagation

How to get the dataset

Business Problem Description

Installing Keras

ANN in Python - Step 1

ANN in Python - Step 2

ANN in Python - Step 3

ANN in Python - Step 4

ANN in Python - Step 5

ANN in Python - Step 6

ANN in Python - Step 7

ANN in Python - Step 8

ANN in Python - Step 9

ANN in Python - Step 10

ANN in R - Step 1

ANN in R - Step 2

ANN in R - Step 3

ANN in R - Step 4 (Last step)

**Convolutional Neural Networks**

Plan of attack

What are convolutional neural networks?

Step 1 - Convolution Operation

Step 1(b) - ReLU Layer

Step 2 - Pooling

Step 3 - Flattening

Step 4 - Full Connection

Summary

Softmax & Cross-Entropy

How to get the dataset

Installing Keras

CNN in Python - Step 1

CNN in Python - Step 2

CNN in Python - Step 3

CNN in Python - Step 4

CNN in Python - Step 5

CNN in Python - Step 6

CNN in Python - Step 7

CNN in Python - Step 8

CNN in Python - Step 9

CNN in Python - Step 10

CNN in R

**-------------------- Part 9: Dimensionality Reduction --------------------**

Welcome to Part 9 - Dimensionality Reduction

**Principal Component Analysis (PCA)**

Principal Component Analysis (PCA) Intuition

How to get the dataset

PCA in Python - Step 1

PCA in Python - Step 2

PCA in Python - Step 3

PCA in R - Step 1

PCA in R - Step 2

PCA in R - Step 3

**Linear Discriminant Analysis (LDA)**

Linear Discriminant Analysis (LDA) Intuition

How to get the dataset

LDA in Python

LDA in R

**Kernel PCA**

How to get the dataset

Kernel PCA in Python

Kernel PCA in R

**-------------------- Part 10: Model Selection & Boosting --------------------**

Welcome to Part 10 - Model Selection & Boosting

**Model Selection**

How to get the dataset

k-Fold Cross Validation in Python

k-Fold Cross Validation in R

Grid Search in Python - Step 1

Grid Search in Python - Step 2

Grid Search in R

**XGBoost**

How to get the dataset

XGBoost in Python - Step 1

XGBoost in Python - Step 2

XGBoost in R

Download all the Codes and Datasets Here

THANK YOU bonus video

**Bonus Lectures**

***YOUR SPECIAL BONUS***

**Other Machine Learning Algorithms**

**SUPER COMBO - EARLY BIRD BONUSES**

How to Download your Early Bird Bonuses

Bonus #0 : Your Combo Course Here!

Bonus #1: Top 3 Machine Learning Branches

Bonus #2: Getting Things Done Cheatsheet

Kernel SVM Intuition tutorial will be added soon!

SVR Intuition

Welcome to the course! (temp)

Early Bird Welcome!!!

BONUS #1

BONUS #1: Top 3 Machine Learning Branches

BONUS #1: Top 3 Machine Learning Branches (PDF)

BONUS #2: Voronoi Diagrams

BONUS #3: Best books

Download BONUS Videos Here

Bonus # 2: Workshop - CNN for Optical Character Recognition

Bonus # 3: AMA (Ask me Anything Session) with DataScience Expert

**>>>>>> JASONS SECTION**

Kernel PCA Intuition

Grid Search Intuition

k-Fold Cross Validation Intuition

XGBoost Intuition

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**Faculty:**Too good. Yeah it's very good and up to date. I like the Time series explanation. I'm looking forward to more practical sessions

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**Faculty:**Faculty approach is not given we can post queried fellow learners respond It's a good course structure.they give option to continue either in r or pytjon

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**Learning Experience:**This was an online course which was highly focused towards practical aspects of both Machine Learning & Deep Learning. There were a few case studies involving widely used ML Algortihms like(KNN,Decision Trees, SVM, Linear/Logistic Regression, Random Forest, XGBoost). I learnt where and when to use the appropriate algorithms as per given dataset and business idea.

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**Course Support:**No, there was no support/job assistance in this course.

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**Faculty:**Instructors taught well It makes learning consistent.It saves schools money.It provides measurable targets.

**Course Support:**Yes this is career supported course.

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