About Applied Machine Learning  Beginner to Professional Course
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.
Prerequisites for the Applied Machine Learning course
This course requires no prior knowledge about Data Science or any tool.
Key Takeaways from Applied Machine Learning course

Understand how Machine Learning and Data Science are disrupting multiple industries today

Linear and Logistic Regression algorithms for building machine learning models

Understand how to solve Classification and Regression problems in machine learning
Know more about Applied Machine Learning
What will you learn in the ‘Applied Machine Learning’ course?
 100 Lesssons
 3 Real Life Projects from Industry
Tools Covered in Applied Machine Learning Course
Projects for Applied Machine Learning Course
Course curriculum

1
Introduction to Data Science and Machine Learning

2
Setting up your system
 Installation steps for Windows
 Installation steps for Linux
 Installation steps for Mac
 Course Handouts

3
Introduction to Python
 Introduction to Python
 Introduction to Jupyter Notebook
 Download Python Module Handouts
 Expert talk Andrew

4
Variables and Data Types
 Introduction to Variables
 Implementing Variables in Python

5
Operators
 Introduction to Operators
 Implementing Operators in Python
 Quiz: Operators

6
Conditional Statements
 Introduction to Conditional Statements
 Implementing Conditional Statements in Python
 Quiz: Conditional Statements

7
Looping Constructs
 Introduction to Looping Constructs
 Implementing Loops in Python
 Quiz: Loops in Python
 Break, Continue and Pass Statements
 Quiz: Break, Continue and Pass Statement

8
Data Structures
 Introduction to Data Structures
 List and Tuple
 Implementing List in Python
 Quiz: Lists
 List  Project in Python
 Implementing Tuple in Python
 Quiz: Tuple
 Introduction to Sets
 Implementing Sets in Python
 Quiz: Sets
 Introduction to Dictionary
 Implementing Dictionary in Python
 Quiz: Dictionary
 Assignment: Data Structures

9
String Manipulation
 Introduction to String Manipulation
 Quiz: String Manipulation

10
Functions
 Introduction to Functions
 Implementing Functions in Python
 Quiz: Functions in Python
 Lambda Expression
 Quiz: Lambda Expressions
 Recursion
 Implementing Recursion in Python
 Quiz: Recursion
 Expert Talk: Rajeev Shah

11
Modules, Packages and Standard Libraries
 Introduction to Modules
 Modules: Intuition
 Introduction to Packages
 Standard Libraries in Python
 User Defined Libraries in Python
 Quiz: Modules, Packages and Standard Libraries

12
Handling Text Files in Python
 Handling Text Files in Python
 Quiz: Handling Text Files

13
Introduction to Python Libraries for Data Science
 Important Libraries for Data Science
 Quiz: Important Libraries for Data Science

14
Python Libraries for Data Science
 Basics of Numpy in Python
 Basics of Scipy in Python
 Quiz: Numpy and Scipy
 Basics of Pandas in Python
 Quiz: Pandas
 Basics of Matplotlib in Python
 Basics of ScikitLearn in Python
 Basics of Statsmodels in Python
 Unlock the Data Science Universe with Andrew Engel: Insights, Innovations, and Beyond!

15
Reading Data Files in Python
 Reading Data in Python
 Reading CSV files in Python
 Reading Big CSV Files in Python
 Quiz: Reading CSV files in Python
 Reading Excel & Spreadsheet files in Python
 Quiz: Reading Excel & Spreadsheet files in Python
 Reading JSON files in Python
 Quiz: Reading JSON files in Python
 Assignment: Reading Data Files in Python

16
Preprocessing, Subsetting and Modifying Pandas Dataframes
 Subsetting and Modifying Data in Python
 Overview of Subsetting in Pandas I
 Overview of Subsetting in Pandas II
 Subsetting based on Position
 Subsetting based on Label
 Subsetting based on Value
 Quiz: Subsetting Dataframes
 Modifying data in Pandas
 Quiz: Modifying Dataframes
 Assignment: Subsetting and Modifying Pandas Dataframes

17
Sorting and Aggregating Data in Pandas
 Preprocessing, Sorting and Aggregating Data
 Sorting the Dataframe
 Quiz: Sorting Dataframes
 Concatenating Dataframes in Pandas
 Concept of SQLLike Joins in Pandas
 Implementing SQLLike Joins in Pandas
 Quiz: Joins in Pandas
 Aggregating and Summarizing Dataframes
 Preprocessing Timeseries Data
 Quiz: Preprocessing Timeseries Data
 Assignment: Sorting and Aggregating Data in Pandas

18
Visualizing Patterns and Trends in Data
 Visualizing Trends & Pattern in Data
 Basics of Matplotlib
 Data Visualization with Matplotlib
 Quiz: Matplotlib
 Basics of Seaborn
 Data Visualization with Seaborn
 Quiz: Seaborn
 Assignment: Visualizing Patterns and Trends in Data

19
Machine Learning Lifecycle
 6 Steps of Machine Learning Lifecycle
 Introduction to Predictive Modeling

20
Problem statement and Hypothesis Generation
 Defining the Problem statement
 Introduction to Hypothesis Generation
 Performing Hypothesis generation
 Quiz  Performing Hypothesis generation
 List of hypothesis
 Data Collection/Extraction
 Quiz  Data Collection/Extraction

21
Importance of Stats and EDA
 Introduction to Exploratory Data Analysis & Data Insights
 Quiz  Introduction to Exploratory Data Analysis & Data Insights
 Role of Statistics in EDA
 Descriptive Statistics
 Inferential Statistics
 Quiz  Descriptive and Inferential Statistics

22
Understanding Data
 Introduction to dataset
 Quiz  Introduction to dataset
 Reading data files into python
 Quiz  Reading data files into python
 Different Variable Datatypes
 Variable Identification
 Quiz  Variable Identification

23
Probability
 Probability for Data Science
 Quiz  Probability for Data Science
 Basic Concepts of Probability
 Quiz  Basic Concepts of Probability
 Axioms of Probability
 Quiz  Axioms of Probability
 Conditional Probability
 Quiz  Conditional Probability

24
Exploring Continuous Variable
 Data range for continuous variables
 Central Tendencies for continuous variables
 Spread of the data
 Central Tendencies and Spread of the data: Implementation
 Quiz: Central Tendencies and Spread of data
 KDE plots for continuous variable
 KDE plots : Implementation
 Overview of Distributions for Continuous Variables
 Normal Distribution
 Normality Check
 Skewed Distribution
 Skewness and Kurtosis
 Distributions for continuous variable
 Quiz: Distribution of Continuous variables
 Approaching Univariate Analysis
 Approaching Univariate Analysis: Numerical Variables
 Quiz: Univariate analysis for Continuous variables

25
Exploring Categorical Variables
 Central Tendencies for categorical variables
 Understanding Discrete Distributions
 Discrete Distributions Demonstration
 Performing EDA on Catagorical Variables
 Quiz: Univariate Analysis for Categorical Variables

26
Missing Values and Outliers
 Dealing with Missing values
 Understanding Outliers
 Identifying Outliers in data
 Identifying Outliers in data: Implementation
 Quiz: Identifying Outliers in datasets
 Quiz: Outlier treatment

27
Central Limit theorem
 Important Terminologies
 Central Limit Theorem
 CLT: Implementation
 Quiz: Central Limit Theorem
 Confidence Interval and Margin of error

28
Bivariate analysis  Introduction
 Introduction to Bivariate Analysis

29
Continuous  Continuous Variables
 Covariance
 Pearson Correlation
 Spearman's Correlation & Kendall's Tau
 Correlation versus Causation
 Tabular and Graphical Methods
 Performing Bivariate Analysis on Continuous  Continuous variables
 Quiz: ContinuousContinuous Variables

30
Continuous Categorical
 Tabular and Graphical Methods
 Introduction to hypothesis Testing
 PValue
 One Sample ztest
 Two Sampled ztest
 Quiz: Hypothesis Testing and Z scores
 TTest
 TTest vs ZTest
 Quiz: T tests
 Performing Bivariate Analysis on Catagorical  Continuous variables

31
Categorical Categorical
 Tabular and Graphical Methods
 ChiSquared Test
 Quiz: Chi squared tests
 Bivariate Analysis for Categorical Categorical Variables

32
Multivariate Analysis
 Multivariate Analysis
 Multivariate Analysis Implementation

33
Assignments
 Understanding the NYC Taxi Trip Duration Problem
 Assignment: EDA

34
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

35
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

36
Preprocessing Data
 Dealing with Missing Values in the Data
 Quiz: Dealing with missing values in the data
 Replacing Missing Values
 Quiz: Replacing Missing values
 Imputing Missing Values in data
 Quiz: Imputing Missing values in data
 Working with Categorical Variables
 Quiz: Working with categorical data
 Working with Outliers
 Quiz: Working with outliers
 Preprocessing Data for Model Building

37
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

38
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
 Implementing kfold Cross Validation
 Quiz: Understanding kfold Cross Validation
 Quiz: Implementing kfold Cross Validation
 Bias Variance Tradeoff
 Quiz: Bias Variance Tradeoff

39
Linear Models
 Introduction to Linear Models
 Quiz: Introduction to linear model
 Understanding Cost function
 Quiz: Understanding Cost function
 Understanding Gradient descent (Intuition)
 Maths behind gradient descent
 Convexity of cost function
 Quiz: Convexity of Cost function
 Quiz: Gradient Descent
 Assumptions of Linear Regression
 Quiz: Assumptions of linear model
 Implementing Linear Regression (Part1)
 Implementing Linear Regression (Part2)
 Generalized Linear Models
 Quiz: Generalized Linear Models
 Introduction to Logistic Regression
 Quiz: Introduction to logistic regression
 Quiz: Logistic Regression
 Odds Ratio
 Implementing Logistic Regression
 Multiclass using Logistic Regression
 Quiz: MultiClass Logistic Regression
 Challenges with Linear Regression
 Quiz: Challenges with Linear regression
 Introduction to Regularisation
 Quiz: Introduction to Regularization
 Implementing Regularisation
 Coefficient estimate for ridge and lasso (Optional)
 Understanding Cost function

40
Project: Customer Churn Prediction
 Predicting whether a customer will churn or not
 Assignment: NYC taxi trip duration prediction

41
Feedback about the course
 Rate your experience!!
Certificate of Completion
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. 
Senior Content Strategist and BA Program Lead, Analytics Vidhya
Pranav Dar
Pranav is the Senior Content Strategist and BA Program Lead at Analytics Vidhya. He has written over 300 articles for AV in the last 3 years and brings a wealth of experience and writing knowhow to this course. He has a decade of experience in designing courses, creating content and writing articles that people love to read. Pranav is also an instructor on 14+ courses on Analytics Vidhya and is a passionate sports analytics blogger as well.
Here's what our students say about Applied Machine Learning course

Easy and understandable for beginners
Aashish Dagar
I have not completed the course yet (finished till EDA) and these are my thought about the course. This course is quite beginnerfriendly , easy to follow a...
Read MoreI have not completed the course yet (finished till EDA) and these are my thought about the course. This course is quite beginnerfriendly , easy to follow and the material also a good helps in easily understand.
Read Less 
Variables and Data Types Great!
DANNY DOEGAN
Variables and Data Types Great!
Variables and Data Types Great!
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Awesome course
RAVI MISHRA
Learned practical dimensionality reduction, regularisation.
Learned practical dimensionality reduction, regularisation.
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A Must Learn Course for ALL
anant deo
I took this course and started learning as i am beginner for the Data Science field. And after completed just few lessons and modules of this course i felt t...
Read MoreI took this course and started learning as i am beginner for the Data Science field. And after completed just few lessons and modules of this course i felt that it is designed very beautifully for beginners. Every concepts described from basic level and easily understood. Recommending all to definitely go with this course . You will Learn a lot and will get the essential knowledge.
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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 prerequisites, 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 nonrefundable.

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
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