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.