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Course Starts on 15th June 2019
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
Basics of Python programming in the context of Data Science. Please complete Python for Data Science before starting this course.
Course curriculum

1
Introduction to Machine Learning
 Instructor Introduction
 Overview of Machine Learning/ Data Science
 Common Terminology used in AI/ ML
 Applications of Machine Learning

2
Python for Data Science
 Introduction to Python
 Introduction to Python test
 Installing Python
 Installation steps for Mac
 Installation steps for Linux
 Installation steps for Windows
 Setting Up System
 Operator and Variables with their type
 Understanding Conditional and Iterative Statements
 Implementing Functions in Python
 A brief introduction to data structure (List & Dictionary)
 Understanding the concept of Standard Libraries
 Reading a CSV File in Python  Introduction to Pandas
 Understanding dataframes and basic operations
 Regular Expressions
 Challenge: Python Coding Challenge
 Theory of Operators
 Exercise
 Understanding Operators in Python
 Operators test
 Understanding variables and data types

3
Statistics For Data Science
 Introduction to statistics
 Central Tendency of Data (Mean/ Median/ Mode)
 Understanding the various variable types
 Spread of the data (Variance/ Standard Deviation)
 Frequency Tables and Histograms
 Introduction to Probability
 Bernoulli Trials and Probability Mass Function
 Probabilities for Continuous Random Variables
 The Central Limit Theorem
 Properties of the Normal Distribution
 Using the Normal Curve for Calculations
 Introduction to Inferential Statistics
 Confidence Interval and Margin of Error
 Introduction to Hypothesis Testing
 Directional Non Directional hypothesis
 Understanding Errors while Hypothesis Testing
 Understanding T tests
 Chi Squared Tests
 Correlation
 Challenge: Statistics Challenge

4
Basics Steps of Machine Learning
 Introduction to Predictive Modeling
 Types of Predictive Models
 Stages of Predictive Modeling
 Understanding Hypothesis Generation
 Data Extraction
 Understanding Data Exploration
 Reading the data into Python
 Variable Identification
 Univariate analysis for Continuous Variables
 Understanding Univariate Analysis for categorical variables
 Understanding Bivariate Analysis
 Understanding and treating missing values
 Understanding Outlier Treatment
 Understanding Variable Transformation

5
Build Your First Model and Evaluate the Performance
 Understanding the Problem Statement (Classification)
 What is Variance in Machine Learning Models
 Build a benchmark model (Classification)
 Understanding the Problem Statement (Regression)
 Build a benchmark model (Regression)
 Methods to Evaluate model results
 Introduction to Evaluation Metrics
 Confusion Matrix
 Accuracy
 Alternatives of Accuracy
 Precision and Recall
 Thresholding
 AUCROC
 Log loss
 Evaluation Metrics for Regression
 R2, Adjusted R2

6
Build Your First ML Model: kNN
 What Next to Benchmark Model
 Introduction to kNearest Neighbours
 Building a kNN model
 Determining value of k
 kNearest Neighbours Implementation
 Underfitting and Overfitting
 Undedrstanding Validation
 Holdout Validation
 kfold Validation
 Bias and Variance
 Different Approach to create test set in real life versus data sciecne competition
 Project1: Classification
 Project2: Regression

7
Common Machine Learning Models
 Linear Regression
 Project 1
 Logistic Regression
 Project 2
 Ridge Regression
 Lasso Regression
 Decision Tree
 Continuing Project 1
 Continuing Project 2

8
Advanced Machine Learning (Ensemble Models)
 Introduction to Ensemble Models
 Basic Ensemble Models (Average, Median, Mode, Weighted Average and Rank Average)
 Stacking and Blending
 Bootstrap aggregating (bagging)
 Bagging Meta Estimator
 Random Forest
 Hyperparameter Tuning for random forest
 Introduction to Boosting
 AdaBoost
 GMB and XGBM
 LightBoost
 CatBoost
 Project3: Predict that customer will default or not

9
Support Vector Machine and Naïve Bayes
 Understanding SVM Algorithm
 SVM kernel Tricks
 Understanding Naive Bayes
 Naive Bayes Implementation
 Project3: Predict that customer will default or not

10
Multiclass and Multilable
 Understnading Multiclass and Multilabel
 Multiclass Implementation
 Multilabel Implementation
 Project4: Project on Multiclass

11
Feature Engineering
 What is Feature Engineering
 Basic Feature Engineering Implementation
 How to deal with categorical and continuos variables
 Feature Engineering on Time based data
 Feature Engineering on text data (Count Vector, tfidf/ word vector)
 Feature Engineering on image data
 Project5: Twitter sentiment analysis

12
Dimentionality Reduction
 Curse of Dimesionality
 Common Feature Selection and Feature Reduction Methods
 Project 5
 PCA
 tSNE
 Project6: Dimentionality Reduction

13
Unsupervised Machine Learning Methods
 Introduction to Clustering
 kmeans Clustering
 hierarchical clustering
 Dbscan

14
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

15
AutoML and Dask
 Basics of Auto ML
 Introduction to H2O/ MLBox
 Introduction to Dask and Handle large data

16
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

17
Interpretability of Machine Learning Models
 Different ways to interpret Machine Learning Models
 LIME
Project: NYC Taxi Trip Duration Prediction
Project: Telecom 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. 
Anand Mishra
Anand Mishra is Head of Engineering at Analytics Vidhya. He is an entrepreneur, an engineer and a data science professional all rolled into one. He cofounded MudraCircle, the true lending marketplace leveraging machine learning to fulfill SME loans. Before MudraCircle, Anand has worked across several companies like Lendingkart, HTMedia as Head of Data Science, Tickled Media, Infoedge India and Opera Solutions. He brings experience across several domains including ECommerce, Fashion and Retail. Anand earned his B.Tech and M.Tech in Electrical Engineering at IIT Kanpur. Anand specializes in analytical problem solving, especially machine learning, classification, regression, and decision optimization. His thesis focused on automatically annotating large image collections on the web using a combination of weighted featureclassifier pairs.
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.