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, 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 techniques like Bagging, Boosting, Support Vector Machines (SVM) and Kernel Tricks.

Learn dimensionality reduction techniques like Principal Component Analysis (PCA) and tSNE

Evaluate your machine learning models and improve them through Feature Engineering

Learn Unsupervised Machine Learning Techniques like kmeans 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
Know more about Applied Machine Learning
What will you learn in the ‘Applied Machine Learning’ course?
 557 Lesssons
 6 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
Introduction to the Course

3
Setting up your system
 Installation steps for Windows
 Installation steps for Linux
 Installation steps for Mac

4
Introduction to Python
 Introduction to Python
 Introduction to Jupyter Notebook
 Download Python Module Handouts

5
Variables and Data Types
 Introduction to Variables
 Implementing Variables in Python

6
Operators
 Introduction to Operators
 Implementing Operators in Python
 Quiz: Operators

7
Conditional Statements
 Introduction to Conditional Statements
 Implementing Conditional Statements in Python
 Quiz: Conditional Statements

8
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

9
Data Structures
 Introduction to Data Structures
 List and Tuple
 Implementing List in Pyhton
 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

10
String Manipulation
 Introduction to String Manipulation
 Quiz: String Manipulation

11
Functions
 Introduction to Functions
 Implementing Functions in Python
 Quiz: Functions in Python
 Lambda Expression
 Quiz: Lambda Expressions
 Recursion
 Implementing Recursion in Python
 Quiz: Recursion

12
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

13
Handling Text Files in Python
 Handling Text Files in Python
 Quiz: Handling Text Files

14
Introduction to Python Libraries for Data Science
 Important Libraries for Data Science
 Quiz: Important Libraries for Data Science

15
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

16
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

17
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

18
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

19
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

20
Machine Learning Lifecycle
 7 Steps of Machine Learning Lifecycle
 Introduction to Predictive Modeling

21
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

22
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

23
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

24
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

25
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

26
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

27
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

28
Central Limit theorem
 Important Terminologies
 Central Limit Theorem
 CLT: Implementation
 Quiz: Central Limit Theorem
 Confidence Interval and Margin of error

29
Bivariate analysis  Introduction
 Introduction to Bivariate Analysis

30
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

31
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

32
Categorical Categorical
 Tabular and Graphical Methods
 ChiSquared Test
 Quiz: Chi squared tests
 Bivariate Analysis for Categorical Categorical Variables

33
Multivariate Analysis
 Multivariate Analysis
 Multivariate Analysis Implementation

34
Assignments
 Understanding the NYC Taxi Trip Duration Problem
 Assignment: EDA

35
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

36
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

37
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

38
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

39
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

40
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
 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)

41
Project: Customer Churn Prediction
 Predicting whether a customer will churn or not
 Assignment: NYC taxi trip duration prediction

42
Dimensionality Reduction (Part I)
 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
 Quiz: Backward Feature Elimination
 Backward Feature Elimination Implementation
 Forward Feature Selection
 Forward Feature Selection Implementation
 Quiz: Forward Feature Selection

43
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
 ChiSquare
 Quiz: ChiSquare
 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

44
Feature Engineering
 Introduction to Feature Engineering
 Quiz: 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

45
Share your Learnings
 Write for Analytics Vidhya's Medium Publication

46
Project: NYC Taxi Trip Duration prediction
 Exploring the NYC dataset
 Predicting the NYC taxi trip duration
 Predicting the NYC taxi trip duration

47
Working with Text Data
 Introduction to Text Feature Engineering
 Quiz: Introduction to Text Feature Engineering
 Create Basic Text Features
 Quiz: Create Basic Text Features
 Extract Information using Regular Expressions
 Quiz: Extract Information using Regular Expressions
 Learn to use Regular Expressions in Python
 Quiz: Learn to use Regular Expressions in Python
 Text Cleaning
 Quiz: Text Cleaning
 Create Linguistic Features
 Quiz: Create Linguistic Features
 BagofWords
 Quiz: BagofWords
 Text Preprocessing
 Quiz: Text Preprocessing
 TFIDF Features
 Quiz: TFIDF Features
 Word Embeddings
 Create word2vec Features
 Quiz: Word Embeddings

48
Naïve Bayes
 Introduction to Naive Bayes
 Quiz: Introduction to Naive Bayes
 Conditional Probability and Bayes Theorem
 Working of Naive Bayes
 Quiz: Conditional Probability and Naive Bayes
 Math Behind Naive Bayes
 Types of Naive Bayes
 Implementing Naive Bayes
 Quiz: Types of Naive Bayes

49
Multiclass and Multilabel
 Understanding how to solve Multiclass and Multilabel Classification Problem
 Quiz: Multiclass and Multilabel
 Evaluation Metrics: Multi Class Classification
 Quiz: Evaluation Metrics for Multi Class Classification

50
Project: Web Page Classification
 Understanding the Problem Statement
 Understanding the Data
 Building a Web Page Classifier

51
Basics of Ensemble Techniques
 Introduction to Ensemble
 Quiz: Introduction to Ensemble
 Basic Ensemble Techniques
 Quiz: Basic Ensemble Techniques
 Implementing Basic Ensemble Techniques
 Why Ensemble Models Work Well?

52
Advance Ensemble Techniques
 Introduction to Stacking
 Implementing Stacking
 Variants of Stacking
 Implementing Variants of Stacking
 Quiz: Variants of Stacking
 Introduction to Blending
 Implementation: Blending
 Quiz: Introduction to Blending
 Bootstrap Sampling
 Quiz: Bootstrap Sampling
 Introduction to Random Forest
 Quiz: Introduction to Random Forest
 Hyperparameters of Random Forest
 Quiz: Hyperparameters of Random Forest
 Implementing Random Forest
 Introduction to boosting
 Quiz: Introduction to Boosting
 Gradient Boosting Algorithm (GBM)
 Quiz: Gradient Boosting Algorithm
 Math Behind GBM
 Implementing GBM
 Extreme Gradient Boosting (XGBM)
 Implementing XGBM
 Quiz: Extreme Gradient Boosting
 Adaptive Boosting
 Implementing Adaptive Boosting
 Quiz: Adaptive Boosting

53
Project: Ensemble Model on NYC Taxi Trip Duration Prediction
 Predicting the NYC Taxi Trip Duration
 Prediction the NYC Taxi Trip Duration: Dataset

54
Share your Learnings
 Write for Analytics Vidhya's Medium Publication

55
Hyperparameter Tuning
 Introduction to Hyperparameter Tuning
 Different Hyperparameter Tuning methods
 Quiz: Hyperparameter Tuning
 Implementing different Hyperparameter Tuning methods

56
Support Vector Machine
 Understanding SVM Algorithm
 Quiz: Support Vector Machine
 SVM Kernel Tricks
 Kernels and Hyperparameters in SVM
 Implementing Support Vector Machine
 Quiz: Kernel Tricks

57
Working with Image Data
 Introduction to Images
 Understanding the Image data
 Quiz: Understanding the Image Data
 Understanding transformations on Images
 Understanding Edge Features
 Histogram of Oriented Features (HOG)
 Quiz: Image Features

58
Project: Malaria Detection using Blood Cell Images
 Understanding the Problem Statement
 Detecting Malaria using Blood Cell Images
 Dataset: Malaria Detection using Blood Cell Images

59
Advance Dimensionality Reduction
 Introduction to Principal Component Analysis
 Steps to perform Principal Component Analysis
 Quiz: Principal Component Analysis
 Computation of the Covariance Matrix
 Finding the Eigenvectors and Eigenvalues
 Understanding the MNIST dataset
 Implementing Principal Component Analysis
 Quiz: Steps to perform PCA
 Introduction to Factor Analysis
 Steps to perform Factor Analysis
 Quiz: Factor Analysis
 Implementing Factor Analysis
 Quiz: Implementing Factor Analysis

60
Unsupervised Machine Learning Methods
 Introduction to Clustering
 Quiz: Introduction to Clustering
 Applications of Clustering
 Evaluation Metrics for Clustering
 Quiz: Evaluation Metrics for Clustering
 Understanding KMeans
 KMeans from Scratch Implementation
 Quiz: Understanding KMeans
 Challenges with KMeans
 How to Choose Right kValue
 KMeans Implementation
 Quiz: KMeans Implementation
 Hierarchical Clustering
 Implementation Hierarchical Clustering
 Quiz: Hierarchical Clustering
 How to Define Similarity between Clusters

61
Working with Large Datasets: Dask
 Introduction to Dask
 Quiz: Introduction to dask
 Understanding Dask Array and Dataframes
 Quiz: Understanding Dask arrays and dataframes
 Implementing Dask Array and Dataframes
 Quiz: Implementing Dask array and Dask dataframe
 Machine Learning using Dask
 Quiz: Machine Learning using Dask
 Implementing Linear Regression model using Dask

62
Automated Machine Learning
 Introduction to Automated Machine Learning
 Quiz: Introduction to automated machine learning
 Introduction to MLBox
 Implementing MLBox
 Quiz: MLBox

63
Introduction to Neural Network
 Perceptron FREE PREVIEW
 Quiz  Perceptron
 Weights in Perceptron FREE PREVIEW
 Quiz  Weights in Perceptron
 Multi Layer Perceptron FREE PREVIEW
 Quiz  Multi Layer Perceptron
 Visualizing the neural network FREE PREVIEW
 Understanding Decision Boundary
 Quiz: Understanding the decision boundary
 Quiz  Visualizing the neural network
 Forward and Backward Prop Intuition
 Quiz  Forward and Backward Prop Intuition
 Gradient Descent Algorithm
 Quiz  Gradient Descent Algorithm

64
Forward and Backward Propagation
 Understanding Forward Propagation Mathematically
 Quiz  Understanding Forward Propagation Mathematically
 Understanding Backward Propagation Mathematically
 Quiz  Understanding Backward Propagation Mathematically
 Backward Propagation: Matrix Form
 Why Numpy?
 Quiz: Why Numpy?
 Neural Network From scratch Using Numpy
 Quiz: Implementation of Neural Network
 Forward Propagation (using Numpy)
 Backward Propagation (using Numpy)
 Training network (using Numpy)

65
Activation Functions
 Why do we need activation functions?
 Quiz  Why do need activation functions
 Linear Activation Function
 Quiz  Linear Activation Function
 Sigmoid and tanh
 Quiz  Sigmoid and tanh
 ReLU and Leaky ReLU
 Quiz  ReLU and LeakyReLU
 Softmax
 Quiz  Softmax
 Tips to selecting right Activation Function
 Quiz: Tips to selecting right activation function

66
Optimizers
 Variants of Gradient Descent
 Quiz  Variants of Gradient Descent
 Problems with Gradient Descent
 Quiz  Problems with Gradient Descent
 RMSProp
 Quiz  RMSPro
 Adam
 Quiz: Adam

67
Loss Function
 Introduction to loss function
 Quiz  Introduction to Loss Function
 Binary and Categorical Cross entropy / log loss
 Quiz  Binary and Categorical cross entropy / log loss

68
Project: NN on structured Data
 Overview of Deep Learning Frameworks
 Quiz  Overview of deep learning frameworks
 Understanding important Kears modules
 Quiz: Understanding important Keras Modules
 Understanding the problem statement: Loan Prediction
 Quiz: Understanding the problem statement : loan prediction
 Data Preprocessing: Loan Prediction
 Quiz  Data Preprocessing: Loan Prediction
 Steps to solve Loan Prediction Challenge
 Loading loan prediction dataset
 Defining the Model Architecture for loan prediction problem
 Quiz: Defining the model architecture for loan prediction problem
 Training and Evaluating model on Loan Prediction Challenge
 Quiz  Training and Evaluating model on Loan Prediction Challenge

69
Assignment 1  Big Mart
 Assignment: Big Mart Sales Prediction

70
Model Deployment
 Introduction
 Applications of real time machine learning systems
 Offline Batch Processing vs Real Time Systems
 How to make real time systems
 Fundamentals of Memory and Storage
 Client Server Architecture
 Exposing our API to the world
 Assessing the scale of the problem
 Requirements and Implementation Strategy of our Article Recommender System
 Dataset: Data and Code for Recommendation System
 Simple Text Matching System
 Text Similarity Between Two Articles
 Creating Similarity Model
 Creating APIs for our application
 Performance Analysis of APIs
 Introduction to Git and Collaboration
 Resource: Model Deployment
 Module Test: Model Deployment

71
Interpretability of Machine Learning Models
 Introduction to Machine Learning Interpretability
 Quiz: Introduction to ML Interpretability
 Framework and Interpretable Models
 Model Agnostic Methods for Interpretability
 Quiz: Model Agnostic Methods for interpretability
 Implementing Interpretable Model
 Quiz: Implementing Interpretable model
 Implementing Global Surrogate and LIME
 Quiz: Implementing Global Surrogate and LIME
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. 
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.
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!
Read Less 
Awesome course
RAVI MISHRA
Learned practical dimensionality reduction, regularisation.
Learned practical dimensionality reduction, regularisation.
Read Less 
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.
Read Less
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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