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.
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
 Learn how to reduce dimensions using techniques like Principal Component Analysis (PCA) and tSNE
 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
Prerequisites
This course requires no prior knowledge about Data Science or any tool.
Course curriculum

1
Welcome to the Applied Machine Learning Course
 Welcome to the Course

2
Introduction to Data Science and Machine Learning
 Overview of Machine Learning / Data Science
 Common Terminology used in Data Science
 Applications of Data Science

3
Introduction to the Course
 Instructor Introduction
 Overview of the Course

4
Setting up your system
 Installation steps for Windows
 Installation steps for Linux
 Installation steps for Mac

5
Python for Data Science
 Brief Introduction to Python
 Quiz: Introduction to Python
 Theory of Operators
 Understanding Operators in Python
 Quiz: Theory of Operators
 Understanding variables and data types
 Variables and Data Types in Python
 Quiz: Understanding variables and data types
 Understanding Conditional Statements
 Implementing Conditional Statements in Python
 Quiz: Conditional Statements
 Understanding Looping Constructs FREE PREVIEW
 Implementing Looping Constructs in Python
 Quiz: Looping Constructs
 Understanding Functions
 Implementing Functions in Python
 Quiz: Functions in Python
 A brief introduction to data structure
 Quiz: Data Structure
 Understanding the concept of Lists
 Implementing Lists in Python
 Quiz: Lists in Python
 Understanding the concept of Dictionaries
 Implementing Dictionaries in Python
 Quiz: Dictionaries in Python
 Understanding the concept of Standard Libraries
 Quiz: Standard Libraries
 Reading a CSV File in Python  Introduction to Pandas
 Reading a CSV file in Python  Implementation
 Quiz: Reading a csv file in Python
 Understanding dataframes and basic operations
 Reading dataframes and conduct basic operations in Python
 Quiz: DataFrames and basic operations
 Indexing a Dataframe
 Quiz: Indexing DataFrames
 Exercise
 Instructions
 Quiz
 Python Coding Challenge

6
Statistics For Data Science
 Introduction to statistics
 Mode of the data
 Understanding the various variable types
 Quiz: Understanding Variable Types
 Mean of the data
 Outliers in the datasets
 Quiz: Outlier in the datasets
 Median of the dataset
 Quiz: Mode , Mean and Median
 Spread of the data
 Quiz: Spread of the data
 Variance of the data
 Quiz: Variance of the data
 Standard Deviation of the data
 Quiz: Standard Deviation of the data
 Frequency Tables
 Quiz: Frequency Tables
 Histograms
 Quiz: Histograms
 Introduction to Probability
 Quiz: Introduction to probability
 Calculating Probabilities of events
 Quiz: Calculating Probabilities of events
 Bernoulli Trials and Probability Mass Function
 Quiz: Bernoulli Trials and PMF
 Probabilities for Continuous Random Variables
 Quiz: Probabilities for continuous random variable
 The Central Limit Theorem
 Quiz: Central Limit Theorem
 Properties of the Normal Distribution
 Quiz: Properties of Normal distribution
 Using the Normal Curve for Calculations
 Quiz: Normal Curve for calculations
 Z score Part 1
 Understanding the Z tables
 Quiz: Z scores
 Z score part 2
 Introduction to Inferential Statistics
 Quiz: Introduction to Inferential Statistics
 Short Review
 Quiz: Review
 Mean Estimation
 Confidence Interval and Margin of Error
 Quiz: CI and Margin of error
 Introduction to Hypothesis Testing
 Quiz: Hypothesis testing
 Steps to perform hypothesis testing
 Directional Non Directional hypothesis
 Quiz: Directional and Non Directional hypothesis
 Understanding Errors while Hypothesis Testing
 Quiz: Errors while Hypothesis testing
 Understanding T tests
 Quiz: Understanding T tests
 Degree of Freedom
 TCritical Value
 Quiz: TCritical Value
 Steps to perform TTest
 Quiz: Steps to perform TTest
 Conducting One sample T test
 Quiz: One sample T tests
 Paired T tests
 Quiz: Paired T tests
 2 Sample T tests
 Quiz: 2 sample T tests
 Chi Squared Tests
 Quiz: Chi squared tests
 Correlation
 Quiz: Correlation
 Conclusion
 Module Test
 Instructions
 Quiz
 Statistics Coding Challenge

7
Basics Steps of Machine Learning and EDA
 Introduction to Predictive Modeling
 Quiz: Introduction to Predictive Modeling
 Types of Predictive Models
 Quiz: Types of Prediction Models
 Stages of Predictive Modeling FREE PREVIEW
 Quiz: Stages of Predictive Modeling
 Understanding Hypothesis Generation
 Quiz: Hypothesis Generation
 Data Extraction
 Understanding Data Exploration
 Quiz: Data Extraction and Exploration
 Reading the data into Python
 Reading the data into Python : Implementation
 Quiz: Reading Data into Python
 Variable Identification
 Variable Identification : Implementation
 Quiz: Variable Identification
 Univariate analysis for Continuous Variables
 Univariate Analysis for Continuous Variables : Implementation
 Quiz: Univariate analysis for Continuous variables
 Understanding Univariate Analysis for Categorical Variables
 Univariate analysis for Categorical Variables : Implementation
 Quiz: Univariate Analysis for Categorical Variables
 Understanding Bivariate Analysis
 Quiz: Bivariate Analysis
 Bivariate Analysis : Implementation
 Quiz: Bivariate Analysis  Implementation
 Understanding and treating missing values
 Quiz: Treating missing values
 Treating missing values : Implementation
 Quiz: Treating missing values  Implementation
 Understanding Outlier Treatment
 Quiz: Outlier treatment
 Outlier Treatment in Python
 Quiz: Outlier Treatment in Python
 Understanding Variable Transformation
 Quiz: Transforming variables
 Variable Transformation in Python
 Quiz: Variable Transformation in Python
 Basics of Model Building
 Quiz: Basics of Model Building

8
Data Manipulation and Visualization
 Sorting Dataframes
 Merging Dataframes
 Quiz: Sorting and Merging dataframes
 Apply function
 Aggregating data
 Quiz: Aggregating data and Apply function
 Basics of Matplotlib
 Data Visualization using Matplotlib
 Quiz: Matplotlib
 Basics of Seaborn
 Data Visualization using Seaborn
 Quiz: Seaborn

9
Project: EDA  Customer Churn Analysis
 Understanding the Problem Statement
 Understanding the Data
 Understanding the NYC Taxi Trip Duration Problem
 Assignment: EDA

10
Share your Learnings
 Write for Analytics Vidhya's Medium Publication

11
Build Your First Predictive Model
 Introduction and Overview
 Quiz: Introduction and Overview
 Preparing the Dataset
 Quiz: Preparing the dataset
 Build a Benchmark Model: Regression
 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

12
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

13
Build Your First ML Model: kNN
 Introduction to kNearest Neighbours
 Quiz: Introduction to kNearest Neighbours
 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
 Dealing with Missing Values and Strings
 Implementing kNearest Neighbours algorithm
 Quiz: Implementing kNearest Neighbours algorithm

14
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

15
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
 Quiz: Logistic Regression
 Implementing 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)

16
Project: Customer Churn Prediction
 Predicting whether a customer will churn or not
 Assignment: NYC taxi trip duration prediction

17
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
 Backward Feature Elimination Implementation
 Quiz: Backward Feature Elimination
 Forward Feature Selection
 Forward Feature Selection Implementation
 Quiz: Forward Feature Selection

18
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

19
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
 Automated Feature Engineering : Feature Tools
 Implementing Feature tools

20
Share your Learnings
 Write for Analytics Vidhya's Medium Publication

21
Project: NYC Taxi Trip Duration prediction
 Exploring the NYC dataset
 Predicting the NYC taxi trip duration
 Predicting the NYC taxi trip duration

22
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

23
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

24
Multiclass and Multilabel
 Understnading Multiclass and Multilabel Classification Problem
 Quiz: Multiclass and Multilabel
 Evaluation Metrics: Multi Class Classification
 Quiz: Evaluation Metrics for Multi Class Classification

25
Project: Web Page Classification
 Understanding the Problem Statement
 Understanding the Data
 Building a Web Page Classifier

26
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?

27
Advance Ensemble Techniques
 Introduction to Stacking
 Implementing Stacking
 Quiz: Introduction to 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

28
Share your Learnings
 Write for Analytics Vidhya's Medium Publication

29
Advance Dimensionality Reduction
 PCA
 tSNE
 Project6: Dimentionality Reduction

30
Support Vector Machine
 Understanding SVM Algorithm
 SVM kernel Tricks
 Project3: Predict that customer will default or not

31
Unsupervised Machine Learning Methods
 Introduction to Clustering
 kmeans Clustering
 hierarchical clustering

32
AutoML and Dask
 Basics of Auto ML
 Introduction to H2O/ MLBox
 Introduction to Dask and Handle large data

33
Neural Network
 Introduction to Neural Network
 Understanding Forward and Backward Propagation
 Understading Activation Functions, Optimizers and loss functions
 Introduction to Convolutional Neural Network
 Project 8: Image Classification

34
Model Deployment
 Online vs Offline Learning
 Scalable Machine Learning Introduction
 Creating APIs for ML model
 Accessing Scale of the Problem
 Performance Analysis of your Code
 Performance Analysis of APIs
 Impact of Concurrency
 Improving Performace of Code: Tricks and Techniques
 Code Versioning
 Introduction to Git
 Model Testing and Deployment
 Error Reporting

35
Interpretability of Machine Learning Models
 Different ways to interpret Machine Learning Models
 LIME
Project: NYC Taxi Trip Duration Prediction
Project: Customer Churn Prediction
Project: Grupo Bimbo Inventory demand prediction
Project: Detect Malaria using Deep Learning
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

Who should take this 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 certificate upon completion of the course?
Yes, you will be given a certificate upon satisfactory completion of the course.

What is the fee for this course?
Fee for this course is INR 12,999

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.
Support for Applied Machine Learning  Beginner to Professional
Support for Applied Machine Learning course can be availed through any of the following channels:
 Phone  9 a.m.  5 p.m. (IST) on Weekdays Monday  Friday on +918368253068
 Email training_support@analyticsvidhya.com (revert in 1 working day)
 Live interactive chat sessions (https://support.analyticsvidhya.com/ ), Monday to Friday between 7 PM to 8 PM IST.