**Machine
Learning & Deep Learning in Python & R**

Covers
Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting
and more using both Python & R

**About
the Course**

You're looking for a complete **Machine Learning and Deep Learning
course** that can help you launch a flourishing career in
the field of Data Science & Machine Learning, right?

**You've found the right
Machine Learning course!**

After
completing this course **you will be able to**:

·
Confidently build predictive Machine Learning and Deep Learning models to solve
business problems and create business strategy

·
Answer Machine Learning related interview questions

·
Participate and perform in online Data Analytics competitions such as Kaggle
competitions

Check
out the table of contents below to see what all Machine Learning and Deep
Learning models you are going to learn.

**How this course will
help you?**

A **Verifiable
Certificate of Completion** is presented to all students who
undertake this Machine learning basics course.

If
you are a business manager or an executive, or a student who wants to learn and
apply machine learning in Real world problems of business, this course will
give you a solid base for that by teaching you the most popular techniques of machine
learning.

**Why should you choose
this course?**

This
course covers all the steps that one should take while solving a business
problem through linear regression.

Most
courses only focus on teaching how to run the analysis but we believe that what
happens before and after running analysis is even more important i.e. before
running analysis it is very important that you have the right data and do some
pre-processing on it. And after running analysis, you should be able to judge
how good your model is and interpret the results to actually be able to help
your business.

**What makes us qualified
to teach you?**

The
course is taught by Abhishek and Pukhraj. As managers in Global Analytics
Consulting firm, we have helped businesses solve their business problem using
machine learning techniques and we have used our experience to include the
practical aspects of data analysis in this course

We
are also the creators of some of the most popular online courses - with over
600,000 enrollments and thousands of 5-star reviews like these ones:

*This is very good, i love the fact the all
explanation given can be understood by a layman - Joshua*

*Thank you Author for this wonderful course.
You are the best and this course is worth any price. - Daisy*

**Our Promise**

Teaching
our students is our job and we are committed to it. If you have any questions
about the course content, practice sheet or anything related to any topic, you
can always post a question in the course or send us a direct message.

**Download Practice files,
take Quizzes, and complete Assignments**

With
each lecture, there are class notes attached for you to follow along. You can
also take quizzes to check your understanding of concepts. Each section
contains a practice assignment for you to practically implement your learning.

**Table of Contents**

·
**Section 1 - Python basic**

This section gets you started with Python.

This section will help you set up the python and Jupyter
environment on your system and it'll teach you how to perform some basic
operations in Python. We will understand the importance of different libraries
such as Numpy, Pandas & Seaborn.

·
**Section 2 - R basic**

This section will help you set up the R and R studio on your
system and it'll teach you how to perform some basic operations in R.

·
**Section 3 - Basics of
Statistics**

This section is divided into five different lectures starting
from types of data then types of statistics then graphical representations to
describe the data and then a lecture on measures of center like mean median and
mode and lastly measures of dispersion like range and standard deviation

·
**Section 4 - Introduction
to Machine Learning**

In this section we will learn - What does Machine Learning mean.
What are the meanings or different terms associated with machine learning? You
will see some examples so that you understand what machine learning actually
is. It also contains steps involved in building a machine learning model, not
just linear models, any machine learning model.

·
**Section 5 - Data
Preprocessing**

In this section you will learn what actions you need to take
step by step to get the data and then prepare it for the analysis these steps
are very important. We start with understanding the importance of business
knowledge then we will see how to do data exploration. We learn how to do
uni-variate analysis and bivariate analysis then we cover topics like** outlier
treatment, missing value imputation, variable transformation and correlation.**

·
**Section 6 - Regression
Model**

This section starts with simple linear regression and then
covers multiple linear regression.

We have covered the basic theory behind each concept without
getting too mathematical about it so that you understand where the concept is
coming from and how it is important. But even if you don't understand it,
it will be okay as long as you learn how to run and interpret the result as
taught in the practical lectures.

We also look at how to quantify models accuracy, what is the
meaning of F statistic, how categorical variables in the independent variables
dataset are interpreted in the results, what are other variations to the
ordinary least squared method and how do we finally interpret the result to
find out the answer to a business problem.

·
**Section 7 -
Classification Models**

This section starts with Logistic regression and then covers
Linear Discriminant Analysis and K-Nearest Neighbors.

We have covered the basic theory behind each concept without
getting too mathematical about it so that you

understand where the concept is coming from and how it is
important. But even if you don't understand

it, it will be okay as long as you learn how to run and
interpret the result as taught in the practical lectures.

We also look at how to quantify models performance using
confusion matrix, how categorical variables in the independent variables
dataset are interpreted in the results, test-train split and how do we finally
interpret the result to find out the answer to a business problem.

·
**Section 8 - Decision
trees**

In this section, we will start with the basic theory of decision
tree then we will create and plot a simple Regression decision tree**.** Then
we will expand our knowledge of regression Decision tree to classification
trees, we will also learn how to create a classification tree in Python and R

·
**Section 9 - Ensemble
technique****
**In this
section, we will start our discussion about advanced ensemble techniques for
Decision trees. Ensembles techniques are used to improve the stability and
accuracy of machine learning algorithms. We will discuss Random Forest,
Bagging, Gradient Boosting, AdaBoost and XGBoost.

·
**Section 10 - Support
Vector Machines****
**SVM's
are unique models and stand out in terms of their concept

**.**In this section, we will discussion about support vector classifiers and support vector machines.

·
**Section 11 - ANN
Theoretical Concepts**

This part will give you a solid understanding of concepts
involved in Neural Networks.

In this section you will learn about the single cells or
Perceptrons and how Perceptrons are stacked to create a network architecture.
Once architecture is set, we understand the Gradient descent algorithm to find
the minima of a function and learn how this is used to optimize our network
model.

·
**Section 12 - Creating
ANN model in Python and R**

In this part you will learn how to create ANN models in Python
and R.

We will start this section by creating an ANN model using
Sequential API to solve a classification problem. We learn how to define
network architecture, configure the model and train the model. Then we evaluate
the performance of our trained model and use it to predict on new data. Lastly
we learn how to save and restore models.

We also understand the importance of libraries such as Keras and
TensorFlow in this part.

·
**Section 13 - CNN
Theoretical Concepts**

In this part you will learn about convolutional and pooling
layers which are the building blocks of CNN models.

In this section, we will start with the basic theory of
convolutional layer, stride, filters and feature maps. We also explain how
gray-scale images are different from colored images. Lastly we discuss pooling
layer which bring computational efficiency in our model.

·
**Section 14 - Creating
CNN model in Python and R****
**In this
part you will learn how to create CNN models in Python and R.

We will take the same problem of recognizing fashion objects and
apply CNN model to it. We will compare the performance of our CNN model
with our ANN model and notice that the accuracy increases by 9-10% when we use
CNN. However, this is not the end of it. We can further improve accuracy by
using certain techniques which we explore in the next part.

·
**Section 15 - End-to-End
Image Recognition project in Python and R****
**In this
section we build a complete image recognition project on colored images.

We take a Kaggle image recognition competition and build
CNN model to solve it. With a simple model we achieve nearly 70% accuracy
on test set. Then we learn concepts like Data Augmentation and Transfer
Learning which help us improve accuracy level from 70% to nearly 97% (as good
as the winners of that competition).

·
**Section 16 -
Pre-processing Time Series Data**

In this section, you will learn how to visualize time series,
perform feature engineering, do re-sampling of data, and various other tools to
analyze and prepare the data for models

·
**Section 17 - Time Series
Forecasting**

In this section, you will learn common time series models such
as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.

By
the end of this course, your confidence in creating a Machine Learning or Deep
Learning model in Python and R will soar. You'll have a thorough understanding
of how to use ML/ DL models to create predictive models and solve real world
business problems.

Below
is a list of popular** FAQs **of
students who want to start their Machine learning journey-

**What is Machine
Learning?**

Machine
Learning is a field of computer science which gives the computer the ability to
learn without being explicitly programmed. It is a branch of artificial
intelligence based on the idea that systems can learn from data, identify
patterns and make decisions with minimal human intervention.

**Why use Python for
Machine Learning?**

Understanding
Python is one of the valuable skills needed for a career in Machine Learning.

Though
it hasn’t always been, Python is the programming language of choice for data
science. Here’s a brief history:

In 2016, it overtook R on Kaggle, the premier platform for data science
competitions.

In 2017, it overtook R on KDNuggets’s annual poll of data scientists’
most used tools.

In 2018, 66% of data scientists reported using Python daily, making it the
number one tool for analytics professionals.

Machine
Learning experts expect this trend to continue with increasing development in
the Python ecosystem. And while your journey to learn Python programming may be
just beginning, it’s nice to know that employment opportunities are abundant
(and growing) as well.

**Why use R for Machine
Learning?**

Understanding
R is one of the valuable skills needed for a career in Machine Learning. Below
are some reasons why you should learn Machine learning in R

1.
It’s a popular language for Machine Learning at top tech firms. Almost all of
them hire data scientists who use R. Facebook, for example, uses R to do
behavioral analysis with user post data. Google uses R to assess ad
effectiveness and make economic forecasts. And by the way, it’s not just tech
firms: R is in use at analysis and consulting firms, banks and other financial
institutions, academic institutions and research labs, and pretty much
everywhere else data needs analyzing and visualizing.

2.
Learning the data science basics is arguably easier in R. R has a big
advantage: it was designed specifically with data manipulation and analysis in
mind.

3.
Amazing packages that make your life easier. Because R was designed with
statistical analysis in mind, it has a fantastic ecosystem of packages and
other resources that are great for data science.

4.
Robust, growing community of data scientists and statisticians. As the field of
data science has exploded, R has exploded with it, becoming one of the
fastest-growing languages in the world (as measured by StackOverflow). That
means it’s easy to find answers to questions and community guidance as you work
your way through projects in R.

5.
Put another tool in your toolkit. No one language is going to be the right tool
for every job. Adding R to your repertoire will make some projects easier – and
of course, it’ll also make you a more flexible and marketable employee when
you’re looking for jobs in data science.

**What is the difference
between Data Mining, Machine Learning, and Deep Learning?**

Put
simply, machine learning and data mining use the same algorithms and techniques
as data mining, except the kinds of predictions vary. While data mining
discovers previously unknown patterns and knowledge, machine learning
reproduces known patterns and knowledge—and further automatically applies that
information to data, decision-making, and actions.

Deep
learning, on the other hand, uses advanced computing power and special types of
neural networks and applies them to large amounts of data to learn, understand,
and identify complicated patterns. Automatic language translation and medical
diagnoses are examples of deep learning.

**What
you Learn from this Course?**

·
Learn how to solve
real life problem using the Machine learning techniques

·
Machine Learning
models such as Linear Regression, Logistic Regression, KNN etc.

·
Advanced Machine
Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.

·
Understanding of
basics of statistics and concepts of Machine Learning

·
How to do basic
statistical operations and run ML models in Python

·
Indepth knowledge of
data collection and data preprocessing for Machine Learning problem

·
How to convert
business problem into a Machine learning problem

**Who can Enroll in this
Course?**

·
People pursuing a
career in data science

·
Working Professionals
beginning their Data journey

·
Statisticians needing
more practical experience

**What
Requirement for this Course?**

·
Students will need to
install Anaconda software but we have a separate lecture to guide you install
the same

**How can Learn Course
this Course?**

1.
**Create
Account / Login on Udemy.com**

2.
**Learn
Course by Enroll in this Course **

** **

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