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 what's 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 what's 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 what's 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 what's 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:
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Part 1 – Introduction to Python Programming.
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This is the part where you will learn basic of python programming and familiarize yourself with Python environment.
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Be able to import, export, explore, clean and prepare the data for advance modeling.
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Understand the underlying statistics of data and how to report/document the insights.
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Part 2 – Machine Learning using Python
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Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
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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.
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Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting
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Features:
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Fully packed with LAB Sessions. One to learn from and one for you to do it yourself.
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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.
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Quiz after each section to test your learning.
Bonus:
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This course is packed with 5 projects on real data related to different domains to prepare you for wide variety of business problems.
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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 It's 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
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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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