Applied Machine Learning Assessment Test

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About Applied Machine Learning - Beginner to Professional Course

Machine Learning is re-shaping and revolutionising the world and disrupting industries and job functions globally. It is no longer a buzzword - many different industries have already seen automation of business processes and disruptions from Machine Learning. In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them in real-world business problems.


Key Takeaways from this course:

  • Understand how Machine Learning and Data Science are disrupting multiple industries today.
  • Linear, Logistic Regression, Decision Tree and Random Forest algorithms for building machine learning models.
  • Understand how to solve Classification and Regression problems in machine learning
  • Ensemble Modeling and techniques like Bagging and Boosting
  • Support Vector Machines (SVM) and Kernel Tricks
  • Prior to building your machine learning model, learn how to reduce dimensions using techniques like Principal Component Analysis (PCA) and t-SNE
  • How to evaluate your machine learning models and improve them through Feature Engineering
  • Learn Unsupervised Machine Learning Techniques like k-means clustering and Hierarchical Clustering
  • Learn how to work with different kinds of data for machine learning problems (tabular, text, unstructured)
  • Improve and enhance your machine learning model’s accuracy through feature engineering


Pre-requisites for the Applied Machine Learning course

This course requires no prior knowledge about Data Science or any tool.

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Course curriculum

  • 2
    Introduction to the Course
    • Overview of the Course
  • 3
    Setting up your system
    • Installation steps for Windows
    • Installation steps for Linux
    • Installation steps for Mac
  • 4
    Python for Data Science
    • Brief Introduction to Python
    • Quiz: Introduction to Python
    • Theory of Operators
    • Understanding Operators in Python
    • Quiz: Theory of Operators
    • Understanding variables and data types
    • Variables and Data Types in Python
    • Quiz: Understanding variables and data types
    • Understanding Conditional Statements
    • Implementing Conditional Statements in Python
    • Quiz: Conditional Statements
    • Understanding Looping Constructs FREE PREVIEW
    • Implementing Looping Constructs in Python
    • Quiz: Looping Constructs
    • Understanding Functions
    • Implementing Functions in Python
    • Quiz: Functions in Python
    • A brief introduction to data structure
    • Quiz: Data Structure
    • Understanding the concept of Lists
    • Implementing Lists in Python
    • Quiz: Lists in Python
    • Understanding the concept of Dictionaries
    • Implementing Dictionaries in Python
    • Quiz: Dictionaries in Python
    • Understanding the concept of Standard Libraries
    • Quiz: Standard Libraries
    • Reading a CSV File in Python - Introduction to Pandas
    • Reading a CSV file in Python - Implementation
    • Quiz: Reading a csv file in Python
    • Understanding dataframes and basic operations
    • Reading dataframes and conduct basic operations in Python
    • Quiz: DataFrames and basic operations
    • Indexing a Dataframe
    • Quiz: Indexing DataFrames
    • Exercise
    • Instructions
    • Quiz
    • Python Coding Challenge
  • 5
    Basics Steps of Machine Learning and EDA
    • Introduction to Predictive Modeling
    • Quiz: Introduction to Predictive Modeling
    • Types of Predictive Models
    • Quiz: Types of Prediction Models
    • Stages of Predictive Modeling FREE PREVIEW
    • Quiz: Stages of Predictive Modeling
    • Understanding Hypothesis Generation
    • Quiz: Hypothesis Generation
    • Data Extraction
    • Understanding Data Exploration
    • Quiz: Data Extraction and Exploration
    • Reading the data into Python
    • Reading the data into Python : Implementation
    • Quiz: Reading Data into Python
    • Variable Identification
    • Variable Identification : Implementation
    • Quiz: Variable Identification
    • Univariate analysis for Continuous Variables
    • Univariate Analysis for Continuous Variables : Implementation
    • Quiz: Univariate analysis for Continuous variables
    • Understanding Univariate Analysis for Categorical Variables
    • Univariate analysis for Categorical Variables : Implementation
    • Quiz: Univariate Analysis for Categorical Variables
    • Understanding Bivariate Analysis
    • Quiz: Bivariate Analysis
    • Bivariate Analysis : Implementation
    • Quiz: Bivariate Analysis - Implementation
    • Understanding and treating missing values
    • Quiz: Treating missing values
    • Treating missing values : Implementation
    • Quiz: Treating missing values - Implementation
    • Understanding Outlier Treatment
    • Quiz: Outlier treatment
    • Outlier Treatment in Python
    • Quiz: Outlier Treatment in Python
    • Understanding Variable Transformation
    • Quiz: Transforming variables
    • Variable Transformation in Python
    • Quiz: Variable Transformation in Python
    • Basics of Model Building
    • Quiz: Basics of Model Building
  • 6
    Data Manipulation and Visualization
    • Sorting Dataframes
    • Merging Dataframes
    • Quiz: Sorting and Merging dataframes
    • Aggregating data
    • Apply function
    • Quiz: Aggregating data and Apply function
    • Basics of Matplotlib
    • Data Visualization using Matplotlib
    • Quiz: Matplotlib
    • Basics of Seaborn
    • Data Visualization using Seaborn
    • Quiz: Seaborn
  • 7
    Project: EDA - Customer Churn Analysis
    • Understanding the Problem Statement
    • Understanding the Data
    • Understanding the NYC Taxi Trip Duration Problem
    • Assignment: EDA
  • 8
    Build Your First Predictive Model
  • 9
    Evaluation Metrics
    • Introduction to Evaluation Metrics
    • Quiz: Introduction to Evaluation Metrics
    • Confusion Matrix
    • Quiz: Confusion Matrix
    • Accuracy
    • Quiz: Accuracy
    • Alternatives of Accuracy
    • Quiz: Alternatives of Accuracy
    • Precision and Recall
    • Quiz: Precision and Recall
    • Thresholding
    • Quiz: Thresholding
    • AUC-ROC
    • Quiz: AUC-ROC
    • Log loss
    • Quiz: Log loss
    • Evaluation Metrics for Regression
    • Quiz: Evaluation Metrics for Regression
    • R2 and Adjusted R2
    • Quiz: R2 and Adjusted R2
  • 10
    Build Your First ML Model: k-NN
  • 11
    Selecting the Right Model
    • Introduction to Overfitting and Underfitting Models
    • Quiz: Introduction to Overfitting and Underfitting Models
    • Visualizing overfitting and underfitting using knn
    • Quiz: Visualizing overfitting and underfitting using knn
    • Selecting the Right Model
    • What is Validation?
    • Quiz: What is Validation
    • Understanding Hold-Out Validation
    • Quiz: Understanding Hold-Out Validation
    • Implementing Hold-Out Validation
    • Quiz: Implementing Hold-Out Validation
    • Understanding k-fold Cross Validation
    • Quiz: Understanding k-fold Cross Validation
    • Implementing k-fold Cross Validation
    • Quiz: Implementing k-fold Cross Validation
    • Bias Variance Tradeoff
    • Quiz: Bias Variance Tradeoff
  • 12
    Linear Models
    • Introduction to Linear Models
    • Understanding Cost function
    • Quiz: Understanding Cost function
    • Understanding Gradient descent (Intuition)
    • Maths behind gradient descent
    • Convexity of cost function
    • Quiz: Gradient Descent
    • Assumptions of Linear Regression
    • Implementing Linear Regression
    • Generalized Linear Models
    • Quiz: Generalized Linear Models
    • Introduction to Logistic Regression
    • Odds Ratio
    • Implementing Logistic Regression
    • Quiz: Logistic Regression
    • Multiclass using Logistic Regression
    • Quiz: Multi-Class Logistic Regression
    • Challenges with Linear Regression
    • Introduction to Regularisation
    • Quiz: Introduction to Regularization
    • Implementing Regularisation
    • Coefficient estimate for ridge and lasso (Optional)
  • 13
    Project: Customer Churn Prediction
    • Predicting whether a customer will churn or not
    • Assignment: NYC taxi trip duration prediction
  • 14
    Decision Tree
    • Introduction to Decision Trees
    • Quiz: Introduction to Decision Trees
    • Purity in Decision Trees
    • Quiz: Purity in Decision Trees
    • Terminologies Related to Decision Trees
    • Quiz: Terminologies Related to Decision Trees
    • How to Select the Best Split Point in Decision Trees
    • Quiz: How to Select the Best Split Point in Decision Trees
    • Chi-Square
    • Quiz: Chi-Square
    • Information Gain
    • Quiz: Information Gain
    • Reduction in Variance
    • Quiz: Reduction in Variance
    • Optimizing Performance of Decision Trees
    • Quiz: Optimizing Performance of Decision Trees
    • Decision Tree Implementation
  • 15
    Feature Engineering
    • Introduction to Feature Engineering
    • Exercise on Feature Engineering
    • Overview of the module
    • Feature Transformation
    • Quiz: Feature Transformation
    • Feature Scaling
    • Quiz: Feature Scaling
    • Feature Encoding
    • Quiz: Feature Encoding
    • Combining Sparse classes
    • Quiz: Combining Sparse classes
    • Feature Generation: Binning
    • Feature Interaction
    • Quiz: Feature Interaction
    • Generating Features: Missing Values
    • Frequency Encoding
    • Quiz: Frequency Encoding
    • Feature Engineering: Date Time Features
    • Implementing DateTime Features
    • Quiz: Implementing DateTime Features
    • Automated Feature Engineering : Feature Tools
    • Implementing Feature tools
  • 16
    Project: NYC Taxi Trip Duration prediction
    • Exploring the NYC dataset
    • Predicting the NYC taxi trip duration
    • Predicting the NYC taxi trip duration
  • 17
    Final Assessment
    • Final Assessment
  • 18
    Share your Learnings
    • Write for Analytics Vidhya's Medium Publication

Machine Learning Project 1

NYC Taxi Trip Duration Prediction

Uber, Lyft, Ola and many more online ride hailing services are trying hard to use their extensive data to create data products such as pricing engines, driver allotment etc. To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied or in other words the trip duration. This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from TLC commission New York.
Machine Learning Project 1

Machine Learning Project 2

Customer Churn Prediction

A Bank wants to take care of customer retention for their product; savings accounts. The bank wants you to identify customers likely to churn balances below the minimum balance in next quarter. You have the customers information such as age, gender, demographics along with their transactions with the bank. Your task as a data scientist would be to predict the propensity to churn for each customer.
Machine Learning Project 2

Machine Learning Project 3

Web Page Classification

Classification of Web page content is vital to many tasks in Web information retrieval such as maintaining Web directories and focused crawling which is used to selectively seek out web pages that are relevant to a pre-defined set of topics. In this project, you will learn to build a web page classifier that can classify the web pages into their respective classes.
 Machine Learning Project 3

Machine Learning Project 4

Malaria diagnosis involves close examination of the blood smear at 100x magnification. This is followed by a manual counting process wherein experts count the number of Red blood cells impacted by parasites. Automatic detection of Malaria from blood smear image is a scalable solution and can save a lot of hours for healthcare industry going a long way in our battle against this deadly disease. In this project, we try to identify from blood smears using deep learning to predict whether the sample is taken from an infected person.
Machine Learning Project 4

Instructor(s)

  • Kunal Jain

    Founder & CEO

    Kunal Jain

    Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch.
  • Sunil Ray

    Chief Content Officer

    Sunil Ray

    Sunil Ray is Chief Content Officer of Analytics Vidhya. He brings years of experience of using data to solve business problems for several Insurance companies. Sunil has a knack of taking complex topics and then breaking them into easy and simple to understand concepts - a unique skill which comes in handy in his role at Analytics Vidhya. Sunil also follows latest developments in AI & ML closely and is always up for having a discussion on impact of technology on years to come.
  • Pranav  Dar

    Pranav Dar

    Pranav is a data scientist and Senior Editor for Analytics Vidhya. He has experience in data visualization and data science. Pranav has previously worked for a number of years in the learning and development field for a globally-known MNC. He brings a wealth of instructor experience to this course as he has taken multiple trainings on data science, statistics and presentation skills over the years. He is passionate about writing and has penned over 200 articles on data science for Analytics Vidhya.

FAQ

  • Who should take the Applied Machine Learning course?

    This course is meant for people looking to learn Machine Learning. We will start out to understand the pre-requisites, the underlying intuition behind several machine learning models and then go on to solve case studies using Machine Learning concepts.

  • When will the classes be held in this course?

    This is a self paced course, which you can take any time at your convenience over the 6 months after your purchase.

  • How many hours per week should I dedicate to complete the course?

    If you can put between 8 to 10 hours a week, you should be able to finish the course in 6 to 8 weeks.

  • Do I need to install any software before starting the course ?

    You will get information about all installations as part of the course.

  • What is the refund policy?

    The fee for this course is non-refundable.

  • Do I need to take the modules in a specific order?

    We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

  • Do I get a machine learning certificate upon completion of the course?

    Yes, you will be given a certificate upon satisfactory completion of the Applied Machine Learning course.

  • Which machine learning tools are we using in this course?

    Fee for this course is INR 14,999

  • How long I can access the course?

    You will be able to access the course material for six months since the start of the course.

Support for Applied Machine Learning - Beginner to Professional