Machine Learning Algorithms: Basics to Advanced
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Machine Learning Algorithms: Basics to Advanced

Learn how to use Pandas and master the advanced algorithms to excel in Machine Learning
0.0 (0 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
22 students enrolled
Created by Packt Publishing
Last updated 6/2019
English
English [Auto-generated]
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 8.5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Master concepts involved in interacting with databases.
  • Learn to apply multiple and different functions to dataframe columns.
  • Implement the concept of exponentially weighted windows.
  • Build awesome ML solutions for your business problems.
  • Apply ML algorithms to design your own solution to business problems.
  • Transform your weak models to strong models using boosting.
  • Learn how to combine different types of model sequentially.
Course content
Expand all 88 lectures 08:19:58
+ Mastering Python Data Analysis with Pandas
17 lectures 01:16:56

This video provides an overview of the entire course.

Preview 03:50

The objective of this video is to explain and show how we read and write the text data from/to the local directory or desktop using Pandas.

  • How to find the current working directory, change directory, and list directories

  • How to read a text file from the local directory/desktop

  • How to write the text file in the local directory or the desktop

Reading and Writing Data in Text Format
04:35

The objective of this video is to explain how we read and display XML and HTML data.

  • What are the modules required to be imported for XML and HTML

  • How to read XML file and display the data

  • How to read HTML page and display the data

XML and HTML Web Scrapping
04:20

The objective of this video is to explain how we read the data from the databases.

  • How we establish connectivity with the database, for example, SQLite

  • How we retrieve the data from the database

  • How we display the data of the database

Interacting with Databases
03:42

The objective of this video is to explain how do we read and write Excel and HDF5 file to/from local directory or the desktop.

  • What are the modules and functions to be used for reading and writing Excel and HDF5 file

  • How do we read and write excel file to/from the local directory or the desktop

  • How do we read and write HDF5 file to/from the local directory or the desktop

Binary Data Formats (Excel and HDF5)
06:41

The objective of this video is to explain the concept of data wrangling/ munging and pandas data structure.

  • Explain the concept of data wrangling/munging

  • Explain the Pandas data structure

  • Explain how to use data wrangling/munging

Preview 08:00

The objective of this video is to explain how do we combine and merge data sets.

  • How to create multiple data sets using dataframes

  • Explain how to use concat function to join the data sets

  • Explain how to use merge function to merge the data sets

Combining and Merging Data Sets
04:13

The objective of this video is to explain how to reshape the data sets using pivot and set_index function.

  • Explain how to create data sets using dataframes

  • Explain how to use pivot function to reshape the data sets

  • Explain how to use set_index function to reshape the data sets

Reshaping, Pivoting, and Advanced Indexing Data Sets
03:36

The objective of this video is to explain how do we remove duplicates and replace values in the data sets.

  • Explain how to detect duplicates in the data sets

  • Explain how to remove duplicates from the data sets

  • Explain how to replace values in the data sets

Data Transformation on Data Sets
03:30

The objective of this video is to explain how we perform manipulation on string data sets.

  • Explain how to declare the string data sets

  • This step explains how to perform operation on the data sets

  • This step explains how to get details on string data sets

String Manipulations on Data Sets
04:10

The objective of this video is to explain how to identify the missing data sets, removing the missing data sets, adding values to the data sets.

  • Explain how to detect missing values in the data sets

  • Explain how to remove missing values in the data sets

  • Explain how to add values in the data sets

Working with Missing Data Sets
03:29

The objective of this video is to explain how aggregation is performed on the data sets.

  • Explain how to create the dataframe in the data sets

  • Explain how we use groupby function on the column of the dataframe

  • Explain how we use rolling mean function on the data set

Data Aggregation on Data Sets
04:04

The objective of this video is to explain how group-wise operation is performed on the data set.

  • Explain how to create the dataframe in the data sets

  • Explain how to use list() function to view grouping of the data sets

  • Explain how we use describe() function to get the descriptive statistics of the data sets

Group-Wise Operations on Data Sets
05:11

The objective of this video is to explain how we use different statistical function such as covariance, correlation and data ranking on the data sets.

  • Explain how to use covariance on the data sets

  • Explain how to use correlation on the data sets

  • Explain how to use data ranking on the data sets

Statistical Functions Example
04:49

The objective of this video is to explain how we use different windows function such as rolling , time aware and binary function on the data sets

  • Explain how to use rolling function on the data set

  • This step explains how to use time aware function on the data set

  • Explain how we use binary function on the data sets

Windows Functions Example
05:45

The objective of this video is to explain how we use multiple functions on the column or different columns of the dataframe.

  • This step explains how to create a dataframe

  • Explain how to apply multiple functions on the column of the dataframe

  • Explain how to apply multiple functions on the different columns of the dataframe

Applying Multiple and Different Functions to Dataframe Columns
03:26

The objective of this video is to explain how to use exponentially weighted windows.

  • Explain what is exponentially weighted windows

  • Explain what are the arguments for EWM

  • Explain how do we apply EWM function on the data set

Exponentially Weighted Windows
03:35
+ Machine Learning Algorithms in 7 Days
46 lectures 05:40:10

This video gives an overview of this section.

Preview 03:36

The video explains what Linear regression is and how does it work

   •  A brief overview about various components of Linear Regression

   •  Example showing the working logic of Linear Regression

   •  Example showing the working logic of Logistic Regression

Introduction to Linear Regression
08:58

The video explains the different concepts of Linear Regression

   •  Talk about the major steps on estimation and prediction in Linear Regression

   •  Explains how Linear Regression can deal with the overfitting issue

   •  Discuss different methods of regularization to deal with the overfitting issue for Linear Regression

Various concepts around Linear Regression
07:12

The video discusses about various types of extension in Linear Regression

   •  Talks about various evaluation metrics of model’s performance for Linear Regression

Using Linear Regression for prediction
14:23

This video discusses about the pros and cons of using Linear Regression

   •  Discuss the advantages of Linear Regression

   •  Note down the limitations of using Linear Regression

Advantages and Limitations of Linear Regression
03:44

The video talks about the case study on Linear Regression

   •  Overview the dataset

   •  Learn about the application of Linear Regression

   •  Talk about performance evaluation of the model

Case Study – Linear Regression
19:46

The video explains what Logistic Regression is and how does it work.

   •  A brief overview about the various components of Logistic Regression

   •  Explains why Linear Regression can’t be a suitable approach even for linear classification

   •  Example showing the working logic of Logistic Regression

Introduction to Logistic Regression
07:15

The video explains the different concepts of Logistic Regression

   •  Talk about major steps on estimation and prediction in Logistic Regression

   •  Explain how Logistic Regression can deal with overfitting issue

   •  Explain different methods of regularization to deal with the overfitting issue for Logistic Regression

Various Concepts around Logistic Regression
07:30

The video discusses about the various types of extension for multi-class classification exercise.

   •  Talk about the various evaluation metrics of the model’s performance for Logistic Regression

   •  Discuss about different types of Logistic Regression

   •  Learn how Logistic Regression can deal with the class imbalance problem

How Logistic Regression Can Be Used for Multi-Class Classification
22:13

This video discusses about the various pros and cons of Logistic Regression

   •  List down the advantages of Logistic Regression

   •  Discuss the cons on using Logistic Regression

Advantages and Limitations of Logistic Regression
03:18

The video talks about the case study on Logistic Regression using bank data

   •  Discuss how we can apply Logistic Regression to solve a binary classification exercise

   •  Look at the examples of dealing with class Imbalance if any

   •  Talk about the performance evaluation of the Logistic Regression

Case Study – Logistic Regression
22:56

The video aims at giving an assignment to the viewer.

   •  Explore the links mentioned

Homework Assignment – Linear Models
06:55

The video explains what Decision Tree algorithm is and how does it work

   •  A brief overview about the Decision Tree algorithm

   •  Talk about the working logic

   •  Discuss an example of a fully-grown Decision Tree

Introduction to Decision Tree
04:57

The video explains the overall landscape of Decision Tree (DT) and various nuances of it.

   •  Overview the Decision Tree landscape

   •  Understand the splitting logic

   •  Explain how does DT deal with the issue of overfitting

Concepts - Various Decision Tree Algorithms
05:36

The video explains the different types of knobs or hyperparameters of the DT algorithm.

   •  Understand the various knobs

   •  Overview of the method - CHAID

   •  An example to discuss in detail the working logic of DT

Various Components of Decision Tree
03:59

The video explains the various advantages and disadvantages of the DT algorithm.

   •  Discuss about the wide range of applications of Decision Tree in real world

   •  Learn how does DT deal with class imbalance problem

Advantages and Disadvantages of Decision Tree Algorithm
04:20

The video talks about the case study on DT using Customer Attrition data

   •  Overview the dataset and discuss the basic EDA

   •  Look at model development and model validation

   •  Visualize the Decision Tree

Case Study – IBM’s HR Attrition Data
24:47

The video aims at giving an assignment to the viewer.

   •  Explore the links mentioned

Homework Assignment – Decision Tree Algorithm
01:46

The video explains what is Random Forest (RF) algorithm and how does it work.

   •  A brief overview about the RF algorithm

   •  Talk about the working logic

   •  Look at an example of classification and regression tree

Introduction to Random Forest Algorithm
05:03

The video explains the different steps in RF algorithm

   •  Get introduced to OOB error in the context of RF algorithm

   •  Understand the splitting logic

   •  Look at an illustrative example of RF

Concepts of Random Forest Algorithm
05:26

The video explains different types of knobs or hyperparameters of the RF algorithm.

   •  Discuss various Knobs

   •  Overview of probability calibration

   •  Discuss the missing value imputation using RF and various applications of RF in real world

Various components of Random Forest Algorithm
05:39

The video explains various advantage and disadvantages of the RF algorithm.

   •  Discuss about various model evaluation metrics

Advantages and Disadvantages of Random Forest Algorithm
04:02

The video talks about the case study on RF using Customer Attrition Data

   •  Get an overview on the dataset

   •  Learn about model development and validation

   •  Discuss the multi-variate analysis

Case Study - IBM's HR Attrition Data
12:15

The video aims at giving an assignment to the viewer.

   •  Explore the links mentioned

Homework Assignment – Random Forest Algorithm
01:52

The video explains what is K-Means algorithm and how does it work.

   •  A brief overview about the K-Means algorithm

   •  Talks about the working logic

   •  Example showing the working logic of K-Means algorithm

Introduction to K-Means Clustering
04:43

The video explains the different concepts in K-Means algorithm.

   •  Talks about major data processing steps before applying K-Means algorithm

   •  Illustrative Example of K-Means algorithm

   •  Explains different methods to decide optimal value of K

Concepts of K-Means Clustering Algorithm
06:17

The video explains different types of clustering methods other than K-Means.

   •  Talks about model-based clustering approaches like GMM etc

   •  Provides a quick introduction of different other types of clustering approaches

   •  Clustering in presence of mixed /categorical input features

Different Clustering Methods
04:32

The video explains various advantages and disadvantages of the K-Means algorithm.

   •  Discuss the advantages of K-Means

   •  Look at the cons of using K-Means

Advantages and Disadvantages of K-Means Clustering Algorithm
01:04

The video talks about the case study on K-Means using Iris Dataset

   •  Dataset Overview

   •  Model-based clustering algorithm

   •  Comparison of various types of clustering approaches

Case Study – Iris Dataset
11:45

The video gives an exercise based on the theory discussed in previous videos

   •  Explore the exercise given

Homework Assignment - K-Means Clustering Algorithm
01:17

The video explains what KNN algorithm is and how does it work.

   •  A brief overview about the KNN algorithm

   •  Talks about the working logic

   •  Discuss an example showing the working logic of KNN algorithm

Introduction to KNN Algorithm
04:05

The video explains the different concepts in KNN algorithm

   •  Talks about major data processing steps before applying KNN Algorithm

   •  Learn how to choose the optimal value of K

   •  Discuss different methods to choose neighbors in KNN algorithm

Concepts of KNN Algorithm
05:52

The video explains various advantage and disadvantages of the KM algorithm.

   •  Discuss the advantages of using KNN algorithm

   •  List down the cons of using KNN algorithm

Advantages and Limitations of KNN Algorithm
02:36

The video talks about the case study on KNN using Income Census Dataset

   •  Overview the dataset and basic EDA

   •  Look at hyper-parameter tuning to obtain the best result for the dataset

   •  Compare the performance through various classification matrices

Case Study – Income Census Dataset
15:35

The video talks about the assignment given to the viewers.

   •  Explore additional knowledge on KNN

Homework Assignment – KNN Algorithm
01:52

The video explains what Naïve Bayes algorithm is and how does it work.

   •  A brief overview about the Naïve Bayes algorithm

   •  Talk about the two building blocks like what is Bayes rule

   •  Look at an example showing the working logic of Naïve Bayes algorithm

Introduction to Naïve Bayes Algorithm
03:04

The video explains the different concepts in Naïve Bayes algorithm.

   •  Talk about major concepts like likelihood, prior probability, posterior probability in the context of Naïve Bayes algorithm

   •  Understand what "Naïve" in Naïve Bayes algorithm is

   •  Discuss various types of Naïve Bayes algorithms

Concepts of Naïve Bayes Algorithm
05:42

The video explains various advantages and disadvantages of the Naïve Bayes algorithm.

   •  Discuss the advantages of Naïve Bayes algorithm

   •  List down the cons of using Naïve Bayes algorithm

Advantages and Limitations of Naïve Bayes Algorithm
03:02

The video talks about the case study on Naïve Bayes algorithm using the bank marketing dataset.

   •  Overview the dataset and understand the basic EDA

   •  Discuss the applications of Naïve Bayes algorithm

Case Study – Bank Marketing Dataset
14:16

In this video, you are asked to work on an exercise based on the concepts and NB methods learned in previous videos.

   •  Try other Naïve Bayes methods and compare the solutions

Homework Assignment - Naïve Bayes Algorithm
04:34

The video explains what Time Series Analysis and its various components are.

  • A brief overview about the Time Series analysis

  • Talks about various key components of Time Series data

  • Look at an example showing how various components can be combined to represent the overall Time Series

Introduction to Time Series Analysis
03:16

The video explains key mathematical concepts of Time Series model.

  • Talk about major concepts such as auto-correlation, stationarity, and so on

Various Concepts around Time Series Model
04:38

The video explains major steps to build an ARIMA / SARIMA Model.

  • Choose the parameters for an ARIMA model

  • Discuss the overall flow of the ARIMA model

Full overview of ARIMA/ SARIMA Model
08:28

This video aims at discussing some important measures of Forecast accuracy.

  • List down the important measure of Forecast accuracy

  • Look at the different types of Forecasting model

Forecast Accuracy Measure – Time Series Analysis
03:23

he video talks about the case study on Time Series Analysis.

  • Overview the dataset

  • Basic exploration of the data

  • Build ARIMA/SARIMA model using the statsmodels package in Python

Case Study – CPI Inflation Dataset
18:43

This video aims at giving an exercise to the viewers based on the concepts learned in previous videos.

  • Go through the link mentioned in the video

  • Apply Holt-Winter’s Exponential smoothing method to derive forecast for the series

Homework Assignment - Time Series Analysis
03:58
Test your knowledge
5 questions
+ Ensemble Machine Learning Techniques
25 lectures 01:22:52

This video provides an overview of the entire course.

Preview 01:31

This video aims to teach the viewer what ensemble learning is, so that the concepts of the rest of the course can be understood.

  • Define what ensemble methods are

  • Give a simple example to explain how ensemble learning works

  • Explain the different types of ensemble learning techniques

Introduction to Ensemble Learning
02:26

In this video, we will set up our environment so that we can use python to build ensemble learning models.

  • Learn about Anaconda and Python

  • See how to install the Anaconda distribution of Python

  • Learn about Jupyter notebook

Setting Up Python
03:17

In this video, we will look at scikit learn and how to build a simple ensemble model.

  • Get an introduction to scikit learn

  • Use scikit learn to write a simple ensemble learning model

  • Use scikit learn to evaluate the ensemble learning model

Setting Up Dependencies
02:39

This video talks about the advantages of using ensemble learning.

  • Define what is Bias, variance

  • Define what is Bias-Variance tradeoff

  • Look at the Advantage of using ensemble learning

Problems that Ensemble Learning Solves
02:56

This video aims to teach the viewer how to use Ensemble Learning for Classification.

  • We go into the details of Majority Voting

  • We see examples for majority voting in real life

  • Look at other combination techniques

Ensemble Learning for Classification
01:39

In this video, we will use python to write a simple ensemble learning model for classification.

  • We will use Jupyter Notebook to execute our code

  • Use Iris dataset to perform classification

  • Use hard voting and soft voting for classification

Implementing Ensemble Learning for Classification
04:30

This video aims to teach the viewer how to use Ensemble Learning for Regression.

  • We go into the details of averaging

  • We see examples for averaging in real life

  • Look at weighted averaging

Ensemble Learning for Regression
01:31

In this video, we will use python to write a simple ensemble learning model for Regression.

  • We will use Jupyter Notebook to execute our code

  • Use of Height vs weight to demonstrate the ensemble technique

  • Use different models instead of different datasets

Implementing Ensemble Learning for Regression
01:40

This video talks about the Building Block for Bagging, that is Boosting.

  • Understand what is Bootstrapping

  • Understand how Bootstrapping works

  • Look at the Advantages of using Bootstrapping

Basics of Bagging
04:42

This video aims to teach the viewer how to use Bootstrapping for Ensemble Learning.

  • We go into the details of application of Bootstrapping for ensemble Learning

  • Use understand about Bootstrap aggregating

  • Look at the Algorithm for bagging

How Bagging Works
02:07

In this video, we will use Python to implement the Bagging technique using SVM.

  • We will use Jupyter Notebook to execute our code

  • Use Movie Rating to perform classification

  • Implement Bagging in python for classification

Making Predictions on Movie Ratings Using SVM
07:59

This video aims to teach the viewer about a versatile and powerful algorithm called Random Forest.

  • We discuss what is Random Forest

  • We see the difference between Bagging Trees and Random Forest

  • We look at the advantages and disadvantages of Bagging

Random Forest
06:07

In this video, we will use python and sklearn to analyze sonar chirp data using Random Forest.

  • We will use Jupyter Notebook to execute our code

  • Use Random Forest implementation in Scikit learn

  • Test with different number of trees

Using Random Forest to Analyze Sonar Chirp Data
04:57

In this video, we will use python and Sci-kit learn to determine if a baby will be underweight at birth.

  • We will use Jupyter Notebook to execute our code

  • We will use Bagging with Decision Trees

  • We will compare the results with Random Forest

Using the Decision Tree to Determine Weight at Birth
02:35

This video talks about an ensemble technique called Boosting.

  • Define what is a weak classifier

  • Define what is Boosting ensemble learning

  • Look at the boosting algorithm

Introduction to Boosting
05:31

This video aims to teach the viewer details about one of the Boosting techniques called AdaBoost.

  • We go into the details of the AdaBoost algorithm

  • We see how to calculate model coefficients

  • We see how to calculate model weights

AdaBoost Algorithm
05:24

In this video, we will see some of the other Boosting techniques.

  • We will learn about gradient boosting

  • We learn about extreme gradient Boosting

  • We see the advantages of Light GBM and CatBoost techniques

Other Boosting Algorithms
02:28

This video aims to teach the viewer how to use Boosting in a real-world scenario.

  • We see the problem we are trying to solve

  • We use Boosting to find out customer churn

Predicting Churn Using Boosting
02:22

This video talks about a commonly used and helpful ensemble technique called stacking.

  • We get an introduction to stacking technique

  • We discuss how stacking works

  • Look at a similar approach to stacking called blending

Overview of Stacking Technique
03:49

This video aims to teach the viewer how to implement blending in Python.

  • We go into the details of blending algorithm

  • We write simple python code to perform blending

Implementing Blending in Python
02:19

In this video, we will see how stacking can be used to solve machine learning problems.

  • The viewer will be introduced to the problem at hand

  • We will then write a stacking implementation

  • We will combine other algorithms using our stacking implementation

How to Use Stacking
02:47

In this video, we will see some of the common pitfalls to avoid during ensembling.

  • We will see things to consider while building a stacked model

  • We will learn about the challenges and its solutions in Boosting

Practical Advice on Using Different Ensemble Learning Techniques
02:17

This video aims to make the user comfortable with creating ensembles of different other ensembles.

  • We will see that its possible to combine two ensemble models

  • We will write a python code to test the result of using an ensemble of ensembles

Combining Different Ensemble Models Together
02:26

We will get comfortable with using ensemble learning for competitive data science.

  • We will understand the dataset

  • We will create and train different models

  • We will combine the trained models using stacking and get our final result

Practical Example on Kaggle Competition
02:53
Requirements
  • Prior knowledge of Pandas is necessary for this course.
  • Basic knowledge of Machine Learning will be advantageous, but not necessary.
Description

Are you really keen to learn some cool Machine Learning algorithms along with mastering advanced data analysis using financial examples in Pandas? Then this Course is for you!

To address the complex nature of various real-world data problems, specialized Machine Learning algorithms have been developed that solve these problems perfectly. On the other hand, the Ensemble is a powerful way to upgrade your model as it combines models and doesn't assume a single model is the most accurate.

This well thought out sequential course takes a practical approach to Mastering Python Data Analysis with Pandas helping you exploring various Machine Learning algorithms to develop your own Ensemble Learning models and methods to use them efficiently. Then, you will learn how to pre-cluster your data to optimize and classify it for large datasets. Along with this, you will also focus on algorithms such as k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, and much more. Finally, you will combine various models to achieve higher accuracy than base models can and develop robust models using the bagging technique.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Mastering Python Data Analysis with Pandas, you will learn how to apply Pandas to important but simple financial tasks such as modeling portfolios, calculating optimal portfolios based upon risk, and more. This video not only teaches you why Pandas is a great tool for solving real-world problems in quantitative finance, it also takes you meticulously through every step of the way, with practical, real-world examples, especially from the financial domain where Pandas is a popular choice. By the end of this video, you will be an expert in using the Pandas library for any data analysis problem, especially related to finance.


The second course, Machine Learning Algorithms in 7 Days you'll learn about 7 key algorithms in the realm of Data Science and Machine Learning. You will learn how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on existing trends in your datasets. This video addresses problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. This course covers algorithms such as k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-Series. On completion of the course, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. You will be able to easily and confidently build and implement data science algorithms.

The third course, Ensemble Machine Learning Techniques will show you how to combine various models to achieve higher accuracy than base models can. This has been the case in various contests such as Netflix and Kaggle, where the winning solutions used ensemble methods. If you want more than a superficial look at machine learning models and wish to build reliable models, then this course is for you.

About the Authors:

  • Prabhat Ranjan has extensive industry experience in Python, R, and Machine Learning. He has a passion for using Python, Pandas, and R for various new, real-time project scenarios. He is a passionate and experienced trainer when it comes to teaching concepts and advanced scenarios in Python, R, data science, and big data Hadoop.His teaching experience and strong industry expertise make him the best in this arena.

  • Shovon Sengupta is an experienced data scientist with over 10 years' experience in advanced predictive analytics, machine learning, deep learning, and reinforcement learning. He has worked extensively in designing award winning solutions for various organizations, for different business problems in the realm of Finance. Currently, he works as Senior Lead Data Scientist at one of the leading NBFCs in USA. Shovon holds an MS in Advanced Econometrics from one of the leading universities in India.

  • Arish Ali started his machine learning journey 5 years ago by winning an All-India machine learning competition conducted by the Indian Institute of Science and Microsoft. He worked as a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has also worked on some cutting-edge problems in Multi-Touch Attribution Modeling, Market Mix Modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers a course in Predictive Business Analytics along with North-western University (SPS). Currently, he is working at a mental health startup called Bemo as an AI developer where his role is to help automate the therapy provided to users and make it more personalized.

Who this course is for:
  • Developers, aspiring Data Science Professionals who are currently implementing one or two data science algorithms and want to learn more to expand their skillset. This course will be a great enabler for those who aspire to master some of the most relevant and oft-used algorithms in Machine Learning.
  • Some programming knowledge in R or Python will be useful (some background about statistics).