About ML for Deep Learning
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 realworld business problems.
Key Takeaways from this course:
 Understand how Machine Learning and Data Science are disrupting multiple industries today.
 Basics of Machine Learning like various evaluation metrics, validation techniques, underfitting & overfitting.
 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
Prerequisites for the ML for Deep Learning
This course requires no prior knowledge about Data Science or any tool.
Course curriculum

1
Introduction to Data Science and Machine Learning
 Welcome to the Machine Learning Basic Course
 Overview of Machine Learning / Data Science FREE PREVIEW
 Common Terminology used in Data Science FREE PREVIEW
 Applications of Data Science FREE PREVIEW

2
Introduction to the Course
 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
 Introduction and Overview FREE PREVIEW
 Quiz: Introduction and Overview FREE PREVIEW
 Preparing the Dataset FREE PREVIEW
 Quiz: Preparing the dataset FREE PREVIEW
 Build a Benchmark Model: Regression FREE PREVIEW
 Quiz: Build a Benchmark Model  Regression
 Benchmark Model: Regression Implementation
 Quiz: Benchmark Model  Regression Implementation
 Build a Benchmark Model: Classification
 Quiz: Build a Benchmark Model  Classification
 Benchmark Model: Classification Implementation
 Quiz: Benchmark  Classification Implementation

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
 AUCROC
 Quiz: AUCROC
 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: kNN
 Introduction to kNearest Neighbours FREE PREVIEW
 Quiz: Introduction to kNearest Neighbours FREE PREVIEW
 Building a kNN model
 Quiz: Building a kNN model
 Determining right value of k
 Quiz: Determining right value of k
 How to calculate the distance
 Quiz: How to calculate the distance
 Issue with distance based algorithms
 Quiz: Issue with distance based algorithms
 Introduction to sklearn
 Implementing kNearest Neighbours algorithm
 Quiz: Implementing kNearest Neighbours algorithm

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 HoldOut Validation
 Quiz: Understanding HoldOut Validation
 Implementing HoldOut Validation
 Quiz: Implementing HoldOut Validation
 Understanding kfold Cross Validation
 Quiz: Understanding kfold Cross Validation
 Implementing kfold Cross Validation
 Quiz: Implementing kfold 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: MultiClass 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
Share your Learnings
 Write for Analytics Vidhya's Medium Publication
Machine Learning Project 1
NYC Taxi Trip Duration Prediction
Machine Learning Project 2
Customer Churn Prediction
Machine Learning Project 3
Big Mart Sales
Machine Learning Project 4
Titanic Survival Prediction
Instructor(s)

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. 
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 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 globallyknown 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.