About Machine Learning Basics

Machine Learning is reshaping 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
  • How to evaluate your machine learning models and improve them through Feature Engineering
  • Improve and enhance your machine learning model’s accuracy through feature engineering


Pre-requisites for the Machine Learning Basics

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

Course curriculum

  • 2
    Introduction to the Course
    • Overview of the Course
    • Instructor Introduction
    • Course Handouts
  • 3
    Setting up your system
    • Installation steps for Windows
    • Installation steps for Linux
    • Installation steps for Mac
  • 4
    Build Your First Predictive Model
  • 5
    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
  • 6
    Preprocessing Data
    • Dealing with Missing Values in the Data
    • Replacing Missing Values
    • Imputing Missing Values in data
    • Working with Categorical Variables
    • Working with Outliers
    • Preprocessing Data for Model Building
  • 7
    Build Your First ML Model: k-NN
  • 8
    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
  • 9
    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)
  • 10
    Project: Customer Churn Prediction
    • Problem Statement - Customer Churn Prediction
    • Predicting whether a customer will churn or not
    • Assignment: NYC taxi trip duration prediction
  • 11
    Basic Dimentionaly Reduction Techniques
    • Introduction to Dimensionality Reduction
    • Quiz: Introduction to Dimensionality Reduction
    • Common Dimensionality Reduction Techniques
    • Quiz: Common Dimensionality Reduction Techniques
    • Missing Value Ratio
    • Missing Value Ratio Implementation
    • Quiz: Missing Value Ratio
    • Low Variance Filter
    • Low Variance Filter Implementation
    • Quiz: Low Variance Filter
    • High Correlation Filter
    • High Correlation Filter Implementation
    • Quiz: High Correlation Filter
    • Backward Feature Elimination
    • Backward Feature Elimination Implementation
    • Quiz: Backward Feature Elimination
    • Forward Feature Selection
    • Forward Feature Selection Implementation
    • Quiz: Forward Feature Selection
  • 12
    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
  • 13
    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
    • Introduction to Text Feature Engineering
    • Quiz: Introduction to Text Feature Engineering
    • Create Basic Text Features
    • Quiz: Create Basic Text Features
    • Automated Feature Engineering : Feature Tools
    • Implementing Feature tools
  • 14
    Project: NYC Taxi Trip Duration prediction
    • Exploring the NYC dataset
    • Predicting the NYC taxi trip duration (Decision tree)
    • Downloads Notebook and DataSets
  • 15
    Basic Ensemble Models
    • Introduction to Ensemble
    • Quiz: Introduction to Ensemble
    • Basic Ensemble Techniques
    • Quiz: Basic Ensemble Techniques
    • Implementing Basic Ensemble Techniques
    • Why Ensemble Models Work Well?
  • 16
    Bagging Technique and Random Forest
    • Bootstrap Sampling
    • Quiz: Bootstrap Sampling
    • Introduction to Random Forest
    • Quiz: Introduction to Random Forest
    • Hyper-parameters of Random Forest
    • Quiz: Hyper-parameters of Random Forest
    • Implementing Random Forest
  • 17
    Project - Ensemble Techniques on NYC Data
    • Predicting the NYC Taxi Trip Duration
  • 18
    Unsupervised Machine Learning
    • Introduction to Clustering
    • Quiz: Introduction to Clustering
    • Applications of Clustering
    • Evaluation Metrics for Clustering
    • Quiz: Evaluation Metrics for Clustering
    • Understanding K-Means
    • K-Means from Scratch Implementation
    • Quiz: Understanding K-Means
    • Challenges with K-Means
    • How to Choose Right k-Value
    • K-Means Implementation
    • Quiz: K-Means Implementation
    • Hierarchical Clustering
    • Implementation Hierarchical Clustering
    • Quiz: Hierarchical Clustering
    • How to Define Similarity between Clusters
  • 19
    Share your Learnings
    • Write for Analytics Vidhya's Medium Publication
  • 20
    Where to go from here?
    • Where to go from here?

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

Big Mart Sales

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.
Machine Learning Project 3

Machine Learning Project 4

Titanic Survival Prediction

The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. The task is to build a predictive model to understand who is more likely to survive.
Machine Learning Project 4

Machine Learning Project 5

The evaluation of instructors by their students has been practiced at most universities for many decades, and there has always been a great interest in a variety of aspects of the evaluations. We have a data set which was anonymously collected in recent years from Gazi University in Ankara (Turkey). Based on the student’s performance, the objective is to group similar performing students together.
Machine Learning Project 5

Certificate of Completion

Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
Certificate of Completion

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

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

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

  • 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.

  • How long I can access the course?

    You will be able to access the course material for next 180 days.

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