Machine Learning Made Easy - Beginner to Expert using Python

Machine Learning Made Easy : Beginner to Expert using Python, available at $44.99, has an average rating of 4.6, with 130 lectures, 10 quizzes, based on 40 reviews, and has 226 subscribers.

You will learn about Python Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced Machine Learning Algorithms, Ensemble Learning. Write your own Python scripts and work in Python Environment. Import, manipulate, clean up, sanitize and export datasets. Understand basic statistics and implement using Python. Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models. Do powerful analysis on data, find insights and present them in visual manner. Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means. Know how each machine learning algorithm works and which one to choose according to the type of problem. Build more than one powerful machine learning model and be able to select the best one and improve it further. This course is ideal for individuals who are Anyone interested in Data Science and Machine Learning. or Students who want a head start in Data Science field. or Data analysts who want to upgrade their skills in Machine Learning. or People who want to add value to their work and business by using Machine Learning. or People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it. or People interested in understanding application of machine learning algorithms on real business problems. or People interested in understanding how a machine learning algorithm works and whats the math behind it. It is particularly useful for Anyone interested in Data Science and Machine Learning. or Students who want a head start in Data Science field. or Data analysts who want to upgrade their skills in Machine Learning. or People who want to add value to their work and business by using Machine Learning. or People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it. or People interested in understanding application of machine learning algorithms on real business problems. or People interested in understanding how a machine learning algorithm works and whats the math behind it.

Enroll now: Machine Learning Made Easy : Beginner to Expert using Python

Summary

Title: Machine Learning Made Easy : Beginner to Expert using Python

Price: $44.99

Average Rating: 4.6

Number of Lectures: 130

Number of Quizzes: 10

Number of Published Lectures: 130

Number of Published Quizzes: 10

Number of Curriculum Items: 140

Number of Published Curriculum Objects: 140

Original Price: ?6,500

Quality Status: approved

Status: Live

What You Will Learn

Python Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced Machine Learning Algorithms, Ensemble Learning.

Write your own Python scripts and work in Python Environment.

Import, manipulate, clean up, sanitize and export datasets.

Understand basic statistics and implement using Python.

Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models.

Do powerful analysis on data, find insights and present them in visual manner.

Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.

Know how each machine learning algorithm works and which one to choose according to the type of problem.

Build more than one powerful machine learning model and be able to select the best one and improve it further.

Who Should Attend

Anyone interested in Data Science and Machine Learning.

Students who want a head start in Data Science field.

Data analysts who want to upgrade their skills in Machine Learning.

People who want to add value to their work and business by using Machine Learning.

People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.

People interested in understanding application of machine learning algorithms on real business problems.

People interested in understanding how a machine learning algorithm works and whats the math behind it.

Target Audiences

Anyone interested in Data Science and Machine Learning.

Students who want a head start in Data Science field.

Data analysts who want to upgrade their skills in Machine Learning.

People who want to add value to their work and business by using Machine Learning.

People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.

People interested in understanding application of machine learning algorithms on real business problems.

People interested in understanding how a machine learning algorithm works and whats the math behind it.

Want to know how Machine Learning algorithms work and how people apply it to solve data science problems??You are looking at right course!? ?? ?

This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.? ?

We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.? ?

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.? ?

Here is how the course is going to work:? ?

Part 1? ? ? – Introduction to Python Programming.?

This is the part where you will learn basic of python programming and familiarize yourself with Python environment.?

Be able to import, export, explore, clean and prepare the data for advance modeling.?

Understand the underlying statistics of data and how to report/document the insights.?

Part 2? ? ? – Machine Learning using Python?

Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.?

Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it.?

Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting

Features:? ?

Fully packed with LAB Sessions. One to learn from and one for you? to do it yourself.? ?

Course includes Python source code, Datasets and other supporting material at the beginning of each section for you to download and use on your own.

Quiz after each section to test your learning.

Bonus:? ?

This course is packed with 5 projects on real data related to different domains to prepare you for wide variety of business problems.

These projects will serve as your step by step guide to solve different business and data science problems.

Course Curriculum

Chapter 1: Introduction to Python Programming

Lecture 1: Python and Its IDE

Lecture 2: Basic Commands in Python

Lecture 3: Objects, Numbers and Strings

Lecture 4: Objects, List, Tuples & Dictionaries

Lecture 5: If, Else & Loop

Lecture 6: Functions and Packages

Lecture 7: Important Packages

Lecture 8: End Note

Chapter 2: Data Handling in Python

Lecture 1: Introduciton to DataHandling

Lecture 2: Basic Commands and Checklist

Lecture 3: Subsetting the Dataset

Lecture 4: Calculated Field Sort Duplicates

Lecture 5: Merge and Exporting

Chapter 3: Descriptive Statistics Plots

Lecture 1: Basic Statistics and Sampling

Lecture 2: Discriptive Statistics

Lecture 3: Percentile and Boxplot

Lecture 4: Graphs Plots and Conclusion

Chapter 4: Data Cleaning and Treatement

Lecture 1: Data cleaning Introduction and Model Building Cycle

Lecture 2: Model Building Cycle

Lecture 3: Data Cleaning Case Study

Lecture 4: LAB – Step1 Basic Content of Dataset

Lecture 5: Variable Level Exploration Catagorical

Lecture 6: Reading Data Dictionary

Lecture 7: LAB – Step2 Catagorical Variable Exploration

Lecture 8: Step3 Variable Level Exploration – continuous

Lecture 9: LAB – Step3 Variable Level Exploration – continuous

Lecture 10: Data Cleaning and Treatments

Lecture 11: Step4 Treatment – scenario1

Lecture 12: LAB – Step4 Treatment – scenario1

Lecture 13: Step4 Treatment – scenario2

Lecture 14: LAB – step4 Treatment – scenario2

Lecture 15: Data Cleaning scenario 3

Lecture 16: LAB – Data Cleaning scenario 3

Lecture 17: Some Other variables

Lecture 18: Conclusion

Chapter 5: Linear Regression

Lecture 1: Introduction and Correlation

Lecture 2: LAB_ Correlation

Lecture 3: Beyond Pearson Correlation

Lecture 4: From Correlation to Regression

Lecture 5: Regression _ LAB

Lecture 6: How Good is My Line

Lecture 7: R Squared

Lecture 8: Multiple Regression Model

Lecture 9: Adjusted R Squared

Lecture 10: Multiple Regression Issues

Lecture 11: Multicolinearity LAB

Lecture 12: Conclusion

Chapter 6: Logistic Regression

Lecture 1: Introduction and Need of Logistic Regression

Lecture 2: A Logistic function

Lecture 3: Building a Logistic Regression Line in Python

Lecture 4: Multiple Logistic Regression Model

Lecture 5: Goodness of fit Logistic Regression

Lecture 6: Multicollinearity in Logistic Regression

Lecture 7: Individual Impact of Variables

Lecture 8: Model Selection

Lecture 9: Conclusion

Chapter 7: Decision Trees

Lecture 1: Introduction to Decision Tree & Segmentation

Lecture 2: The Decision Tree Philosophy & The Decision Tree Approach

Lecture 3: The Splitting criterion & Entropy Calculation

Lecture 4: Information Gain & Calculation

Lecture 5: The Decision Tree Algorithm

Lecture 6: Many Splits for a Variable

Lecture 7: Decision Tree Fitting and Interpretation

Lecture 8: Decision Tree Validation

Lecture 9: Decision Tree Overfitting

Lecture 10: Pruning and Pruning Parameters

Lecture 11: Tree Building & Model Selection-Lab1

Lecture 12: Tree Building & Model Selection-Lab2

Lecture 13: Conclusion

Chapter 8: Model Selection and Cross Validation

Lecture 1: Introduction to Model selection

Lecture 2: Sensitivity Specificity

Lecture 3: LAB – Sensitivity and Specificity in Python

Lecture 4: Sensitivity Specificity Contd p.1

Lecture 5: Sensitivity Specificit Contd p.2

Lecture 6: ROC AUC

Lecture 7: LAB- ROC AUC

Lecture 8: The best model

Lecture 9: The best Model Lab

Lecture 10: Errors

Lecture 11: Overfitting Underfitting p.1

Lecture 12: Overfitting Underfitting p.2

Lecture 13: Overfitting Underfitting p.3

Lecture 14: Overfitting Underfitting p.4

Lecture 15: Bias-Variance Treadoff

Lecture 16: Holdout data Validation

Lecture 17: LAB Holdout data Validation

Instructors

Venkata Reddy AI Classes
Data Science starts here!

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 5 votes

4 stars: 11 votes

5 stars: 20 votes

Frequently Asked Questions

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