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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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Tools Covered in Applied Machine Learning Course

Course curriculum

  • 1
    Welcome to the Applied Machine Learning Course
    • Welcome to the Course
  • 4
    Setting up your system
  • 5
    Python for Data Science
  • 6
    Statistics For Data Science
  • 7
    Basics Steps of Machine Learning and EDA
  • 8
    Data Manipulation and Visualization
  • 9
    Project: EDA - Customer Churn Analysis
  • 10
    Share your Learnings
  • 11
    Build Your First Predictive Model
  • 12
    Evaluation Metrics
  • 13
    Preprocessing Data
  • 14
    Build Your First ML Model: k-NN
  • 15
    Selecting the Right Model
  • 16
    Linear Models
  • 17
    Project: Customer Churn Prediction
  • 18
    Dimensionality Reduction (Part I)
  • 19
    Decision Tree
  • 20
    Feature Engineering
  • 21
    Share your Learnings
  • 22
    Project: NYC Taxi Trip Duration prediction
  • 23
    Working with Text Data
  • 24
    Naïve Bayes
  • 25
    Multiclass and Multilabel
  • 26
    Project: Web Page Classification
  • 27
    Basics of Ensemble Techniques
  • 28
    Advance Ensemble Techniques
  • 29
    Project: Ensemble Model on NYC Taxi Trip Duration Prediction
  • 30
    Share your Learnings
  • 31
    Hyperparameter Tuning
  • 32
    Support Vector Machine
  • 33
    Working with Image Data
  • 34
    Project: Malaria Detection using Blood Cell Images
  • 35
    Advance Dimensionality Reduction
  • 36
    Unsupervised Machine Learning Methods
  • 37
    Working with Large Datasets: Dask
  • 38
    Automated Machine Learning
  • 39
    Introduction to Neural Network
  • 40
    Model Deployment
  • 41
    Interpretability of Machine Learning Models

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

Learn Machine Learning techniques and tools and apply them in real-world business problems

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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)

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.

Applied Machine Learning Assessment Test

Applied Machine Learning assessment test is specially designed to help you choose the right path in your journey of becoming a data scientist. Check if you are the right fit for the course.
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