Learn Complete Machine Learning Bootcamp with Python. Build 5 Complete Machine Learning Real World Projects with Python.
What you will learn
Theory and practical implementation of linear regression using sklearn
Theory and practical implementation of logistic regression using sklearn
Feature selection using RFECV
Data transformation with linear and logistic regression.
Evaluation metrics to analyze the performance of models
Industry relevance of linear and logistic regression
Mathematics behind KNN, SVM and Naive Bayes algorithms
Implementation of KNN, SVM and Naive Bayes using sklearn
Attribute selection methods- Gini Index and Entropy
Mathematics behind Decision trees and random forest
Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost
Different Algorithms for Clustering
Different methods to deal with imbalanced data
Correlation Filtering
Variance Filtering
PCA & LDA
Content and Collaborative based filtering
Singular Value Decomposition
Different algorithms used for Time Series forecasting
Case studies
Hands on Real-World examples.
Description
Crazy about Data Science and Machine Learning?
This course is a perfect fit for you.
This course will take you step by step into the world of Machine Learning.
Machine Learning is the study of computer algorithms that automates analytical model building. 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.
Machine Learning is actively being used today, perhaps in many more places than one world expects.
It contains a lot of topics and this course will cover all step by step.
This Machine Learning course will give you theoretical as well as practical knowledge of Machine Learning.
This Machine Learning course is fun as well as exciting.
It will cover all common and important algorithms and will give you the experience of working on some real-world projects.
This course will cover the following topics:-
1. Theory and practical implementation of linear regression using sklearn.
2. Theory and practical implementation of logistic regression using sklearn.
3. Feature selection using RFECV.
4. Data transformation with linear and logistic regression.
5. Evaluation metrics to analyze the performance of models
6. Industry relevance of linear and logistic regression.
7. Mathematics behind KNN, SVM, and Naive Bayes algorithms.
8. Implementation of KNN, SVM, and Naive Bayes using sklearn.
9. Attribute selection methods- Gini Index and Entropy.
10. Mathematics behind Decision trees and random forest.
11. Boosting algorithms:- Adaboost, Gradient Boosting, and XgBoost.
12. Different algorithms for clustering
13. Different methods to deal with imbalanced data.
14. Correlation filtering
15. Variance filtering
16. PCA & LDA
17. Content and Collaborative based filtering
18. Singular Value decomposition
19. Different algorithms used for Time Series forecasting.
20. Case studies
We have covered each and every topic in detail and also learned to apply them to real-world problems.
There are lots and lots of exercises for you to practice and also a 5 bonus Python Machine Learning Project “Employee Promotion Prediction“, “Predicting Medical Health Expenses“, “Determining Status for Loan Applicants” and “Optimizing Crop Production“.
In this Python Machine Learning Employee Promotion Prediction project, you will learn how to Implement a Predictive Model for Identifying the Right Employees deserving of Promotion. Also, learn how to balance Imbalanced Datasets.
In this Python Machine Learning Predicting Medical Health Expenses project, you will learn how to Implement a Regression Analysis Predictive Model for Predicting the Future Medical Expenses for People using Linear Regression, Random Forest, Gradient Boosting, etc.
In this Python Machine Learning Determining Status for Loan Applicants project, you will learn how to Implement a Classification Analysis Predictive Model for Determining whether a Person should be Granted a Loan or Not.
In this Python Machine Learning Optimizing Crop Production project, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity.
You will make use of all the topics read in this course.
You will also have access to all the resources used in this course.
Enroll now and become a master in machine learning.
English
language
Content
Python Fundamentals
Why should you learn Python?
Installing Python and Jupyter Notebook
Naming Convention for Variables
Built in Data Types and Type Casting
Scope of Variables
Quiz on Variables and Data Types
Quiz Solution
Arithmetic and Assignment Operators
Comparison, Logical, and Bitwise Operators
Identity and Membership Operators
Quiz on Operators
Quiz Solution
String Formatting
String Methods
User Input
Quiz on Strings
Quiz Solution
If, elif, and else
For and While
Break and Continue
Quiz on Loops and Conditionals
Quiz Solution
Python for Data Analysis
Differences between Lists and Tuples
Operations on Lists
Operations on Tuples
Quiz on Lists and Tuples
Quiz Solution
Introduction to Dictionaries
Nested Dictionaries
Introduction to Sets
Set Operations
Quiz on Sets and Dictionaries
Quiz Solution
Introduction to Stacks and Queues
Implementing Stacks and Queues using Lists
Implementing Stacks and Queues using Deque
Quiz on Stacks and Queues
Quiz Solution
Time Complexity
Linear Search
Binary Search
Bubble Sort
Insertion and Selection Sort
Merge Sort
Quiz on Searching, Sorting, and Time Complexity
Quiz Solution
Python Functions Deep Dive
Introduction to Functions
Default Parameters in Functions
Positional Arguments
Keyword Arguments
Python Modules
Quiz on Introduction to Functions
Quiz Solution
Lambda Functions
Filter, Map, and Zip Functions
List, set, and Dictionary Comprehensions
Quiz on Anonymous Functions
Quiz Solution
Introduction to Aggregate Functions
Introduction to Analytical Functions
Quiz on In Built Functions
Quiz Solution
Solving the Factorial Problem using Recursion
Solving the Fibonacci Problem using Recursion
Quiz on Recursions
Quiz Solution
Introduction to Classes and Objects
Inheritance
Encapsulation
Polymorphism
Quiz on Classes and Objects
Quiz Solution
Python for Data Science
Introduction to datetime
The date and time class
The datetime class
The timedelta class
Quiz on Dates and Times
Quiz Solution
Meta Characters for Regular Expressions
Built-in Functions for Regular Expressions
Special Characters for Regular Expressions
Sets for Regular Expressions
Quiz on Regular Expressions
Quiz Solution
Array Creation using Numpy
Mathematical Operations using Numpy
Built-in Functions in Numpy
Quiz on Introduction to Numpy
Quiz Solution
Reading Datasets using Pandas
Plotting Data in Pandas
Indexing, Selecting, and Filtering Data using Pandas
Merging and Concatenating DataFrames
Lambda, Map, and Apply Functions
Quiz on Introduction to Pandas
Quiz Solution
Data Cleaning
Causes and Impact of Missing Values
Types of Missing Values
When should we delete the Missing values
Imputing the Missing Values using the Business Logic
Imputing Missing Values using Mean/Median/Mode
Imputing Missing Values in a real-time scenario
Quiz on Missing Values Imputation
Quiz Solution
How Outliers can be harmful for Machine Learning Models
Finding out Outliers from the Data
Using Winsorization to deal with Outliers
Deleting and Capping the Outliers
Dealing with Outliers in a real-world scenario
Quiz on Outliers Treatment
Quiz Solution
Introduction to reindex, set_index, reset_index, and sort_index Functions
Introduction to Replace and Droplevel Function
Introduction to Split and Strip Function
Introduction to Stack, and Unstack Functions
Introduction to Melt, Explode, and Squeeze Functions
Data Cleaning on Big Mart Dataset
Data Cleaning on Movie Dataset
Data Cleaning on Melbourne Housing Dataset
Data Cleaning on Naukri Dataset
Data Visualizations
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Quiz on Basics of Visualization
Quiz Solution
Scatter Plots
Charts with Colorscale
Bar, Line, and Area Charts
Facet Grids
Statistical Charts
Polar Charts
Subplots
3D Charts
Waffle Charts
Maps
Quiz on Advanced Visualizations
Quiz Solution
Animation with Bubbleplot
Animation with Facets
Animation with Scatter Maps
Animation with Choropleth Maps
Quiz on Animated Visualizations
Quiz Solution
Introduction to Ipywidgets
Interactive Univariate Analysis
Interactive Bivariate Analysis
Interactive Multivariate Analysis
Quiz on Interactive Visualizations
Quiz Solution
Sunburst Charts
Parallel Co-ordinate Charts
Funnel Charts
Gantt Charts
Ternary Charts
Tree Maps
Network Charts
Quiz on Miscellaneous Charts
Quiz Solution
Feature Engineering
Introduction to Feature Engineering
Removing Unnecessary Columns
Decomposing Time and Date Features
Decomposing Categorical Features
Binning Numerical Features
Aggregating Features
Introduction to Feature Engineering on Text Data
Reading and Summarizing the Text
Finding the Length, Polarity and Subjectivity
Finding the Words, Characters, and Punctuation Count
Counting Nouns and Verbs in the Text
Counting Adjectives, Adverb, and Pronouns
Introduction to Assign and Update Functions
Introduction to at_time and between_time Functions
Introduction to nlargest and nsmallest Functions
Introduction to Expanding Function
Introduction to Cumulative Functions
Quiz on Feature Engineering Functions
Quiz Solution
Feature Engineering on Employee Data
Feature Engineering on FIFA Data
Feature Engineering on Hotel Reviews
Feature Engineering on Marketing Data
Feature Engineering on Titanic Data
Quiz on Feature Engineering on Real World Datasets
Quiz Solution
Data Processing
Types of Encoding Techniques
Label Encoding
Feature Mapping for Ordinal Variables
OneHot Encoding
Binary and BaseN Encoding
Mean and Frequency Encoding
Quiz on Dealing with Categorical data
Introduction to Skewness and Normal Distribution
Square and Cube Root Transformation
Log transformation
BoxCox transformation
Quiz on Data Transformation
Train, Test and Validation Split
Standardization and Normalization
Quiz on Data Splitting and Feature Scaling
Linear Regression
Introduction to Linear Regression
Implementing Linear Regression using Sklearn
Feature Selection using RFECV
Data Transformation with Linear Regression
Applying Cross Validation
Analyzing the performance of Regression models
R2 score and adjuted R2 score intuition
MAE, RMSE, R2 and Adjusted R2 in code
Applying real time prediction on our model
Industry relevance of linear regression
Quiz on Modelling with Linear Regression
Logistic Regression
Introduction to Logistic Regression
Implementing Logistic Regression using Sklearn
Feature Selection using RFECV
Hyperparameter tuning using Grid search
Applying Cross Validation
How to analyze performance of a classification model
Using accuracy score to analyze the performance of model
Using ROC-AUC score to analyze the performance of model
Real time prediction using logistic regression
Industry Relevance of Logistic Regression
Quiz on Modelling with Logistic Regression
Introduction to KNN, SVM, Naive Bayes
Introduction to Support Vector machines
The kermel trick for support vector machine
Implementing support vector machine using sklearn
Introduction to K nearest neighbors
Implementing KNN using Sklearn
Introduction to Naive Bayes
Implementing Naive Bayes using sklearn
When should we apply SVM, KNN and Naive bayes
Quiz on Other classification models
Tree Based Models
Intuition for decision trees
Attribute selection method- Gini Index and Entropy
Advantages and Issues with Decision trees
Implementing Decision tree using Sklearn
Understanding the concept of Bagging
Introduction to Random forest
Understanding the parameters of Random forest
Implementing random forest using Sklearn
Quiz on Tree based models
Boosting Models
Understading the concept of boosting
Intuition for Adaboost and Gradient Boosting
Implementing AdaBoost using sklearn
Implementing Gradient Boosting using sklearn
Getting High level intuition for XGBoost
Implementing XGBoost using sklearn
Introudction to Ensembling techniques
Quiz on Boosting Models
Imbalanced Machine Learning
Why Imbalanced Data needs extra attention?
Using Resampling Techniques to Balance the Data
Solving a Real World Problem
Preparing the Data for Predictive Modelling
Applying Logistic Regression using Sklearn
Applying Random Forest using Sklearn
Quiz on Introduction to Imbalanced Machine Learning
Implementing Random Over Sampling using Imblearn
Implementing Random Under Sampling using Imblearn
Implementing Synthetic Sampling using Imblearn
Implementing Neighbors based Sampling using Imblearn
Combination of Oversampling and Under sampling
Implementing Ensemble Models for Imbalanced Data
Introduction to XG Boost for Imbalanced Data
Comparing the Results
Quiz on Handling Imbalanced Datasets
Introduction to Clustering Analysis
Introduction to Clustering
Types of Clustering
Applications of Clustering
Quiz on Introduction to Clustering
Using the Elbow Method for Choosing the Best Value for K
Introduction to K Means Clustering
Solving a Real World Problem
Implementing K Means on the Mall Dataset
Using Silhouette Score to analyze the clusters
Clustering Multiple Dimensions
Quiz on K Means Clustering
Introduction to Hierarchal Clustering
Introduction to Dendrograms
Implementing Hierarchial Clustering
Introduction to DBSCAN Clustering
Implementing DBSCAN Clustering
Quiz on Advanced Clustering Techniques
Dimensionality Reduction
Why High Dimensional Datasets are a Problem
Methods to solve the problem of High Dimensionality
Solving a Real World Problem
Quiz on Introduction
Introduction to Correlation using Heatmap
Removing Highly Correlated Columns using Correlation
Quiz on Correlation Filtering
Introduction to Variance Inflation Filtering
Implementing VIF using statsmodel
Quiz on Variance Filtering
Introduction to Recursive Feature Selection
Implementing Recursive Feature Selection
Introduction the Boruta Algorithm
Implementing the Boruta Algorithm
Quiz on Feature Selection
Introduction to Principal Component Analysis
Implementing PCA
Introduction to t-SNE
Implementing t-SNE
Introduction to Linear Discriminant Analysis
Implementing LDA
Difference between PCA, t-SNE, and LDA
Quiz on Machine Learning
Recommendation Engines
Introduction to Recommender systems
What are it’s Use Cases
Types of Recommender Systems
Evaluating Recommender Systems
Introduction to Content Based Filtering
Preprocessing the Data for Content Based Filtering
Filtering Movies Based on Genres
Introduction to Transactional Encoder
Recommending Similar Movies to Watch
Quiz on Content Based Filtering
Quiz Solution
Introduction to Collaborative Filtering
Preprocessing the Data for Collaborative Filtering
Implementation of User Based Collaborative Filtering
Interpreting the Results obtained from User Based Filtering
Implementation of Item Based Collaborative Filtering
Quiz on Collaborative Based Filtering
Quiz Solution
Introduction to SVD
Implementing SVD using Surprise
Interpreting Results Obtained from SVD
Comparing Content, and Collaborative Based Filtering
Quiz on Singular Value Decomposition
Quiz Solution
Case Study for Netflix
Case Study for Youtube
Time Series Forecasting
What is a Time Series Data
Types of Forecasting
Regression Vs Time Series
Applications of Time Series
Components of Time Series
Quiz on Introduction to Time Series
Getting Time Series data
Handling Missing Values
Handling Outlier Values
Time Series Decomposition
Splitting Time Series Data
Quiz on Time Series Analysis
Basic Forecasting Techniques
Metrics for Time series Forecasting
Simple Moving Averages
Simple Exponential Smoothing
Holt and Holt Winter Exponential Smoothing
Quiz on Smoothing Techniques
Introduction to Auto Regressive Models
Checking for Stationarity Part 1
Checking for Stationarity using Statistical Methods Part 2
Checking for Stationary Implementation
Converting Non-Stationary Series into Stationary
Converting Non-Stationary Series into Stationary Implementation
Auto Correlation and Partial Correlation
Auto Correlation and Partial Correlation Implementation
The Simple Auto Regressive Model
The Simple Auto Regressive Model Implementation
Moving Average Model
Moving Average Model Implementation
Quiz on AR Models
Understanding ARMA Model
Implementing ARMA Model
Understanding ARIMA Model
Implementing ARIMA Model
Understanding SARIMA Model
Implementing SARIMA Model
Quiz on Advanced AR Models
Understanding ARIMAX Model
Implementing ARIMAX Model
Understanding SARIMAX Model
Implementing SARIMAX Model
Quiz on ARIMAX and SARIMAX Models
How to Choose the Right Model
Choosing the Right for Model Smaller Datasets
Choosing the Right Model for Larger Datasets
Best Practices while Choosing a Time series Model.
Quiz on Choosing the Right Model
Why do we Evaluate Performance
Mean Forecast Error
Mean Absolute Error
Mean Absolute Percentage Error
Root Mean Squared Error
Quiz on Why do we Evaluate Performance
Employee Promotion Prediction
Setting up the Environment
Understanding the Dataset
Understanding the Problem Statement
Performing Descriptive Statistics
Missing Values Treatment
Outlier Values Treatment
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Feature Engineering
Categorical Encoding
Data Processing
Feature Scaling
Predictive Modelling
Performance Analysis
Improvements Possible
Major Takeaways from the Project
Quiz on Employee Promotion Prediction
Predicting Health Expense of Customers
Setting up the Environment
Understanding the Dataset
Understanding the Problem Statement
Performing Univariate Analysis
Performing Bivariate Analysis
Performing Multivariate Analysis
Preparing the data for Modelling
Applying Linear Regression Model
Applying Random Forest Model
Applying Gradient Boosting Model
Creating Ensembles of Models
Comparing Performance of these Models
More things to Try
Major Takeaways from the Project
Quiz on Predicting Health Expense of Customers
Determining Whether a Person should be Granted Loan
Understanding the Problem Statement
Setting up the Environment
Understanding the Dataset
Performing Descriptive Statistics
Data Cleaning
Univariate Data Visualizations
Bivariate Data Analysis
Preparing the Data for Modelling
Applying Resampling
Applying Logistic Regression
Applying Gradient Boosting
Summary
Quiz on Determining Whether a Person should be Granted Loan
Optimizing Agricultural Production
Setting up the Environment
Understanding the Dataset
Understanding the Problem Statement
Performing Descriptive Statistics
Analyzing Agricultural Conditions
Clustering Similar Crops
Visualizing the Hidden Patterns
Predictive Modelling
Real Time Predictions
Summarizing the Key-Points
Quiz on Optimizing Agricultural Production