# Machine Learning Algorithms: Basics to Advanced

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Try Udemy for Business- 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.

This video provides an overview of the entire course.

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

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

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

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

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

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

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

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

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

This video gives an overview of this section.

The video explains the different concepts of Linear Regression

â€¢ Talk about the major steps on estimation and prediction in Linear Regression

â€¢ Explains how Linear Regression can deal with the overfitting issue

â€¢ Discuss different methods of regularization to deal with the overfitting issue for Linear Regression

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

â€¢ A brief overview about the various components of Logistic Regression

â€¢ Explains why Linear Regression canâ€™t be a suitable approach even for linear classification

â€¢ Example showing the working logic of Logistic Regression

The video explains the different concepts of Logistic Regression

â€¢ Talk about major steps on estimation and prediction in Logistic Regression

â€¢ Explain how Logistic Regression can deal with overfitting issue

â€¢ Explain different methods of regularization to deal with the overfitting issue for Logistic Regression

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

â€¢ Talk about the various evaluation metrics of the modelâ€™s performance for Logistic Regression

â€¢ Discuss about different types of Logistic Regression

â€¢ Learn how Logistic Regression can deal with the class imbalance problem

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

â€¢ Discuss how we can apply Logistic Regression to solve a binary classification exercise

â€¢ Look at the examples of dealing with class Imbalance if any

â€¢ Talk about the performance evaluation of the Logistic Regression

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

â€¢ Talks about model-based clustering approaches like GMM etc

â€¢ Provides a quick introduction of different other types of clustering approaches

â€¢ Clustering in presence of mixed /categorical input features

The video explains what NaÃ¯ve Bayes algorithm is and how does it work.

â€¢ A brief overview about the NaÃ¯ve Bayes algorithm

â€¢ Talk about the two building blocks like what is Bayes rule

â€¢ Look at an example showing the working logic of NaÃ¯ve Bayes algorithm

The video explains the different concepts in NaÃ¯ve Bayes algorithm.

â€¢ Talk about major concepts like likelihood, prior probability, posterior probability in the context of NaÃ¯ve Bayes algorithm

â€¢ Understand what "NaÃ¯ve" in NaÃ¯ve Bayes algorithm is

â€¢ Discuss various types of NaÃ¯ve Bayes algorithms

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

This video provides an overview of the entire course.

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

- Prior knowledge of Pandas is necessary for this course.
- Basic knowledge of Machine Learning will be advantageous, but not necessary.

*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.

- 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).