Machine Learning & Deep Learning Bootcamp IIT Alumni
Top Rated Bootcamp from IIT Alumni with hands on Practical Projects
 277 lectures
 34:56:28
 Beginner

Certificate
About This Course
You're looking for a complete Machine Learning and Deep Learning Bootcamp that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?
You've found the right Machine Learning and Deep Learning Bootcamp!
After completing this Bootcamp 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 Bootcamp will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning bootcamp.
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 bootcamp will give you a solid base for that by teaching you the most popular techniques of machine learning.
Why should you choose this bootcamp?
This bootcamp covers all the steps that one should take while solving a business problem through linear regression.
Most bootcamps 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 preprocessing 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 bootcamp 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 bootcamp
We are also the creators of some of the most popular online bootcamps  with over 1,800,000 enrollments and thousands of 5star 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 bootcamp. You are the best and this bootcamp 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 bootcamp content, practice sheet or anything related to any topic, you can always post a question in the bootcamp or send us a direct message.
Download Practice filesand complete Assignments
With each lecture, there are class notes attached for you to follow along. 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 univariate 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 KNearest 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, testtrain 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 grayscale 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 910% 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  EndtoEnd 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  Preprocessing Time Series Data
In this section, you will learn how to visualize time series, perform feature engineering, do resampling 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 Autoregression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.
By the end of this bootcamp, 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 fastestgrowing 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 bootcamp, 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, decisionmaking, 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.
Other Information
 Certificate will provided in this course on Completion
 Full lifetime access
 Available on Mobile & Laptop
What Students Will Learn In Your 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 statistical operations and run ML models in Python and R
 Indepth knowledge of data collection and data preprocessing for Machine Learning problem
 How to convert business problem into a Machine learning problem
Are There Any Course Requirements Or Prerequisites?
 Students will need to install Anaconda software but we have a separate lecture to guide you install the same.
Who Are Your Target Students?
 People pursuing a career in data science
 Students want to learn Machine Learning and Deep Learning from scratch
 Working Professionals beginning their Data journey
 Statisticians needing more practical experience
Course Content
 277 lectures
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StartTech Academy
StartTech Academy 15 Reviews
 101 Students
 3 Courses
StartTech Academy is a technologybased Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.
Founded by Abhishek Bansal and Pukhraj Parikh.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.