Game
Development, Data Visualization, Web Scraping, Security and Cryptography, and
Hacking--all at one course!
Rating of this
Course : 4.5//5
Instructor :
Start-tech Academy
What you'll learn
·
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
Requirements
·
Students will need to
install Anaconda software but we have a separate lecture to guide you install
the same
Description
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 a 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
bi-variate 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.
Who this course is for:
·
People pursuing a
career in data science
·
Working Professionals
beginning their Data journey
·
Statisticians needing
more practical experience
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