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Every organization is betting big on machine learning to fuel their growthand the demand for data scientists has skyrocketed. Machine Learning isnot only the most lucrative career option today (average salary forData Science roles in India is 10LPA+ as per Glassdoor) but will soonbecome an essential skill for everyone.

Hence investing time and e�ort to learn Machine Learning will give everystudent a competitive advantage when they step out in thejob market tomorrow.

Machine LearningSpecializationLearn and build a career in Data Science

6 MonthsDuration

CertifiedFormat

Why Machine Learning?

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TRAININGS

Table of Content

1. MACHINE LEARNING

1. Introduction to Machine LearningUnderstand the basics and applications of Machine Learning.

1.1. Overview of Machine Learning 1.2. Terminologies in Data Science 1.3. Applications of Data Science

2. Python for Machine LearningLearn the basics of Python programming, data types in Python and how to work with Data Frames.

2.1. Introduction to Python 2.2. Setting up the System 2.3. Operators in Python 2.4. Data Types in Python 2.5. Conditional Statements 2.6. Looping Constructs 2.7. Functions in Python 2.8. Data Structures in Python 2.9. Standard Libraries 2.10. Reading CSV Files in Python 2.11. Working with Data Frames

3. Machine Learning Life CycleLearn steps to build Machine Learning models and understand variousvisualization techniques.

3.1. Introduction to Predictive Modeling 3.2. Understanding Hypothesis Generation 3.3. Data Extraction 3.4. Understanding Data Exploration 3.5. Reading Data in Python 3.6. Variable Identification 3.7. Univariate Analysis

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3.8. Bivariate Analysis 3.9. Missing Values and Outlier Treatment 3.10. Variable Transformation 3.11. Basics of Model Building

4. Data Exploration and ManipulationLearn data exploration and manipulation using univariate and bivariateanalysis.

4.1. Problem Statement and Univariate Analysis 4.2. Data Manipulation and Bivariate Analysis

5. Data Manipulation and VisualizationLearn data visualization techniques.

5.1. Data Frames 5.2. Apply Function 5.3. Aggregating Data 5.4. Basics of Matplotlib 5.5. Data Visualization using Matplotlib 5.6. Basics of Seaborn 5.7. Data Visualization using Seaborn

6. Build Your First ModelLearn to prepare a dataset and build your first model for regression andclassification problem.

6.1. Introduction and Overview 6.2. Preparing The Dataset 6.3. Building a Regression Model 6.4. Building a Classification Model

7. Evaluation MetricsLearn how to evaluate metrics for classification and regression tasks.

7.1. Introduction to Evaluation Metrics 7.2. Evaluation Metrics for Classification Task 7.3. Evaluation Metrics for Regression Task

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8. k-NNLearn how to build a kNN model and understand multiple distance metrics.

8.1. Building a kNN Model 8.2. Introduction to sklearn 8.3. Implementing kNN Algorithm

9. Selecting the Right ModelLearn how to visualize overfitting and underfitting using kNN and understand various validation techniques.

9.1. Overfitting and Underfitting 9.2. Di�erent Validation Techniques 9.3. Bias Variance Tradeo�

10. Linear ModelsLearn how to build and implement linear regression, logistic regression andregularisation.

10.1. Introduction to Linear Models 10.2. Cost Function and Gradient Descent 10.3. Building a Linear Regression 10.4. Generalized Linear Models 10.5. Building a Logistic Regression 10.6. Multiclass using Logistic Regression 10.7. Introduction to Regularisation 10.8. Implementing Regularisation

11. Decision TreesUnderstand how the decision tree algorithm works and learn about thedi�erent techniques used for splitting. Build a decision tree model.

11.1. Basics of Decision Trees 11.2. Selecting the Best Split Point 11.3. Building a Decision Tree Model

12. Feature EngineeringPerform feature engineering for numerical, categorical, and date-time based features.

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12.1. Introduction to Feature Engineering 12.2. Feature Preprocessing 12.3. Feature Generation 12.4. Feature Engineering with Date-Time Variables 12.5. Automated Feature Engineering

2. ADVANCED MACHINE LEARNING

1. Welcome to the Applied Machine Learning Course

2. Basic Ensemble Model and Advance Ensemble TechniquesLearn about what are basic ensemble models, advance ensemble models and how to implement the ensemble techniques

2.1. Introduction to Basic Ensemble Models 2.2. Implementing Basic Ensemble Techniques 2.3. Introduction to Stacking 2.4. Implementing Stacking 2.5. Variants of Stacking 2.6. Implementing Variants of Stacking 2.7. Introduction to Blending 2.8. Implementation: Blending 2.9. Bootstrap Sampling 2.10. Introduction to Random Forest 2.11. Hyper-parameters of Random Forest 2.12. Implementing Random Forest 2.13. Introduction to boosting 2.14. Gradient Boosting Algorithm (GBM) 2.15. Math Behind GBM 2.16. Implementing GBM 2.17. Extreme Gradient Boosting (XGBM) 2.18. Implementing XGBM 2.19. Adaptive Boosting 2.20. Implementing Adaptive Boosting

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3. Hyperparameter TuningLearn how to tune Machine Learning Models

3.1. Introduction to Hyperparameter Tuning 3.2. Di�erent Hyperparameter Tuning methods 3.3. Implementing di�erent Hyperparameter Tuning methods

4. Dimensionality Reduction (Part I)Understand the Importance of Dimensionality Reduction and learn the basic techniques for dimensionality reduction

4.1. Introduction to Dimensionality Reduction 4.2. Common Dimensionality Reduction Techniques 4.3. Missing Value Ratio 4.4. Missing Value Ratio Implementation 4.5. Low Variance Filter 4.6. Low Variance Filter Implementation 4.7. High Correlation Filter 4.8. High Correlation Filter Implementation 4.9. Backward Feature Elimination 4.10. Backward Feature Elimination Implementation 4.11. Forward Feature Selection 4.12. Forward Feature Selection Implementation

5. Working with Text DataLearn simple techniques to work with text-based data

5.1. Introduction to Text Feature Engineering 5.2. Create Basic Text Features 5.3. Extract Information using Regular Expressions 5.4. Learn to use Regular Expressions in Python 5.5. Text Cleaning 5.6. Create Linguistic Features 5.7. Bag-of-Words 5.8. Text Pre-processing 5.9. TF-IDF Features 5.10. Word Embeddings 5.11. Create word2vec Features

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6. Naïve BayesUnderstand the working of Naive Bayes Algorithm

6.1. Introduction to Naive Bayes 6.2. Conditional Probability and Bayes Theorem 6.3. Working of Naive Bayes 6.4. Math Behind Naive Bayes 6.5. Types of Naive Bayes 6.6. Implementing Naive Bayes

7. Support Vector MachineUnderstand the working of SVM Algorithm

7.1. Understanding SVM Algorithm 7.2. SVM Kernel Tricks 7.3. Kernels and Hyperparameters in SVM 7.4. Implementing Support Vector Machine

8. Working with Image DataLearn simple techniques to work with image-based data

8.1. Introduction to Images 8.2. Understanding the Image data 8.3. Understanding transformations on Images 8.4. Understanding Edge Features 8.5. Histogram of Oriented Features (HOG)

9. Advance Dimensionality ReductionExplore the Advanced Techniques for Dimensionality Reduction

9.1. Introduction to Principal Component Analysis 9.2. Steps to perform Principal Component Analysis 9.3. Computation of the Covariance Matrix 9.4. Finding the Eigenvectors and Eigenvalues 9.5. Understanding the MNIST dataset 9.6. Implementing Principal Component Analysis 9.7. Introduction to Factor Analysis 9.8. Steps to perform Factor Analysis 9.9. Implementing Factor Analysis

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10. Unsupervised Machine Learning MethodsLearn about the basics of clustering and how to evaluate clustering models.

10.1. Introduction and Recap to various Clustering Methods 10.2. Hierarchical Clustering 10.3. Implementation Hierarchical Clustering 10.4. How to Define Similarity between Clusters 10.5. Introduction to Clustering 10.6. Applications of Clustering 10.7. Evaluation Metrics for Clustering 10.8. Understanding K-Means 10.9. K-Means from Scratch Implementation 10.10. Challenges with K-Means 10.11. How to Choose Right k-Value 10.12. K-Means Implementation 10.13. Hierarchical Clustering 10.14. Implementation Hierarchical Clustering 10.15. How to Define Similarity between Clusters

11. Automated Machine LearningUnderstand what is automated machine learning and learn about a popular python library for automated ML

11.1. Introduction to Automated Machine Learning 11.2. Introduction to MLBox 11.3. Implementing MLBox

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3. Structured Query Language (SQL) for Data Science

1. Introduction to DatabasesUnderstand what are databases and its di�erent types. Learn how to store data in databases.

1.1. Introduction 1.2. Why do we need databases? 1.3. What is a database? 1.4. Some properties of a Good Database 1.5. Types of Databases 1.6. How data is Stored in Relational Databases 1.7. How data is stored in NoSQL databases 1.8. Companies using MySQL

2. Installing MySQL/MariaDBGet your system ready. Learn how to install MySQL on your PC and how to access a remote MySQL server.

2.1. Introduction 2.2. Architecture: Client and Server 2.3. MySQL Distributions 2.4. Local Installation on Mac 2.5. Local Installation on Linux 2.6. Local Installation on PC 2.7. Licensing 2.8. Accessing a remote MySQL Server 2.9. Graphical user interfaces

3. Getting Started

3.1. Introduction 3.2. What exactly is SQL? 3.3. History of SQL 3.4. Connecting to MySQL 3.5. Types of Commands - DDL (Creation/Deletion/Updating of Schema) 3.6. Types of Commands - DML (Manipulating data in tables) 3.7. Types of Commands - DCL (Managing Access control) 3.8. Exploring databases

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3.9. Creating tables 3.10. Inserting data in tables 3.11. SELECT Statement- Introduction 3.12. Datatypes in MySQL 3.13. NULL vs NOT NULL

4. Modifying Database StructuresLearn the di�erent techniques to modify a database like updating, deleting and altering data.

4.1. Introduction 4.2. Update command - Concept 4.3. Update command - Example 4.4. Delete command - Concept 4.5. Delete command - Example 4.6. Describe command - Concept 4.7. Describe command - Example 4.8. Alter command - Concept and Example

5. Importing and Exporting DataLearn how to import and export data using MySQL.

5.1. Introduction 5.2. Importing data from CSV to MySQL 5.3. Exporting data from MySQL to CSV 5.4. Backing up databases 5.5. Restoring Databases

6. Data AnalysisLearn the di�erent techniques for data analysis in MySQL.

6.1. Introduction 6.2. Counting Rows and Items 6.3. Aggregation Functions - SUM, AVG, STDDEV 6.4. Extreme Values Identification - MIN, MAX 6.5. Slicing data 6.6. Limiting data 6.7. Sorting data 6.8. Filtering Patterns

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6.9. Groupings, Rolling up data and Filtering in Groups

7. Real Life Project - Descriptive Analytics of FIFA 19 PlayersImplement all the techniques learned on a real-world dataset.

7.1. Introduction 7.2. Data Eyeballing 7.3. Data Dictionary 7.4. Questions we need answers of 7.5. Analyzing data and creating table structure 7.6. Loading data to our MySQL table 7.7. Data Analysis - Simple Queries 7.8. Data Analysis - Advanced Queries

8. Getting Data from Multiple TablesLearn how to work with multiple tables. Understand the di�erent types of joins and how to use them in SQL.

8.1. Introduction 8.2. The need for joins 8.3. Di�erent type of joins 8.4. The Left Join - Concept 8.5. The Left Join - Practical Example 8.6. The Inner Join 8.7. The Cross Join 8.8. The Right Join 8.9. The Self Join

9. Introduction to IndexingUnderstand the concept of Indexing and Relationships in MySQL. Learn about the di�erent types of Relationships in detail.

9.1. Introduction 9.2. Introduction to Indexing 9.3. How indexing works (Basics) 9.4. Relationships 9.5. Types of Relationships 9.6. Table Constraints - PRIMARY KEY, FOREIGN KEY, UNIQUENESS and AUTO INCREMENT

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10. MySQL built-in functionsLearn about the di�erent functions in MySQL and how to use them.

10.1. String functions - CONCAT 10.2. String functions - Case Conversion 10.3. String functions - Trimming Strings 10.4. String functions - Extracting Substrings 10.5. Date/Time functions - Current date and time 10.6. Date/Time functions - Extracting date and time from field 10.7. Date/Time functions - Formatting date and time as Strings 10.8. Numeric functions

11. Manipulate MySQL from PythonSet up a virtual environment and install required libraries. Learn how to write Queries in Database.

11.1. Introduction 11.2. Setting up a virtual environment 11.3. Installing the required packages 11.4. Connecting to MySQL 11.5. Connecting to database table and pulling data 11.6. Querying the database - INSERT 11.7. Querying the database - DELETE 11.8. Querying the database - SEARCH 11.9. Querying the database - INDEXING 11.10. Notes and Resources

4. Ace Data Science Interviews

1. Overview - Ace Data Science InterviewsUnderstand the idea behind the course and get familiar with the coursestructure.

1.1. Instructor Introduction 1.2. Why did we create this course 1.3. How did we create this course 1.4. Who should take this course

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2. Overview - The 7 step Data Science Interviews ProcessUnderstand the steps in any data science interview process.

2.1. Overview - 7 step process 2.2. Infographic - The 7 Step Framework for Data Science Interviews

3. Step 1 - Understanding Roles, skills, Interviews FrameworkLearn about the di�erent roles available in Data Science and the skills required. Have a good understanding of the di�erent types of interviews.

3.1. Overview of Module 3 3.2. Overview of Di�erent Roles 3.3. Senior Roles in Data Science 3.4. Mid-Management Roles in Data Science 3.5. Individual Contributors in Data Science 3.6. Overview of Di�erent Types of Interviews 3.7. Technical Interviews 3.8. Assignments 3.9. HR Assessment 3.10. Business Case Studies 3.11. Guesstimates 3.12. Puzzles 3.13. Di�erent Interviews for Di�erent Job Roles 3.14. Exercise : Identify Roles

4. Step 2 - Building Your Digital PresenceLearn the importance of building a strong online profile and go through the must-have checklist.

4.1. Building your Digital Presence 4.2. Ace Data Science Interviews - GitHub Checklist 4.3. Ace Data Science Interviews - LinkedIn Checklist

5. Step 3 - Building Resume and Applying for JobsLearn how to build a perfect resume and apply for jobs online. Find out what are the dos and don’ts through live resume screening.

5.1. Importance of Resume 5.2. 6 Step Process for Crafting your Resume

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5.3. Examples of Stand out Resumes 5.4. Live Resume Screening - Example 1 5.5. Live Resume Screening - Example 2 5.6. Live Resume Screening - Example 3 5.7. Overview of the Various Paths to Apply 5.8. Applying to Online Portals 5.9. Networking Based Applications 5.10. Work Based Applications

6. Step 4 - Telephonic InterviewsLearn the tips to ace a telephonic interview and important steps to follow before, during and after a telephonic interview.

6.1. Why Companies Ask for Telephonic Interviews 6.2. Telephonic Interview Checklist - BEFORE the Interview 6.3. Telephonic Interview Checklist - DURING the Interview 6.4. Telephonic Interview Checklist - POST the Interview 6.5. Additional Tips for Video Interviews 6.6. Common Questions Interviewers Ask 6.7. Questions you can Ask the Interviewer

7. Step 5 - AssignmentsLearn about the importance of assignments in an interview and the tips and tricks to crack the interview rounds.

7.1. Why Companies Hand Out Assignments 7.2. Assignments for Di�erent Roles 7.3. Tips to Ace the Interview Round

8. Step 6 - In Person InterviewFamiliarity with the di�erent interview types. Learn the dos and don’ts from a live interview examples.

8.1. Overview of the Di�erent Data Science Interview Types 8.2. Technical Interviews 8.3. Puzzle-Based Interview Rounds 8.4. Tips to Solve Puzzles 8.5. Cracking In-Person Case Studies 8.6. Live In-Person Case Study - Example 1

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8.7. Feedback on the Case Study (Example 1) 8.8. Live In-Person Case Study - Example 2 8.9. Feedback of the Case Study (Example 2) 8.10. Guesstimates 8.11. HR Round

9. Step 7 - Post Interview Follow upsUnderstand the general steps to follow after the interview process.

9.1. Post-Interview Steps 9.2. Understanding the Di�erent Post-Interview Steps

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Project 1 - Sales Prediction for a large SupermarketThe data scientists at BigMart have collected sales data for 1559 products

across 10 stores in di�erent cities for an entire year. You will build a predictive

model to forecast the sales of each product at a particular store.

Project 2 - Predict survivors from the Titanic tragedyWe are provided with information about people on the Titanic. Based on the

given data, we will apply machine learning algorithms to predict which passen-

gers survived the tragedy.

Project 3 - Customer Churn PredictionA bank wants to retain its customers and you have the customers’ demograph-

ics such as age, gender & their transaction history. Your task as a data scientist

would be to predict which customers will leave the bank.

Project 4 - NYC Taxi Trip Duration PredictionPredicting trip duration is important for many online cab services such as Uber

& Ola. This project will cover techniques to accurately predict trip duration for

taxi trips in New York using data from TLC commission New York.

Project 5 - Web Page ClassificationClassification of a Web page content is important for search engines to identify

web pages that are relevant to a particular topic. In this project, you will build a

web page classifier that achieves just that.

Projects you will build

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Project 6 - Malaria DetectionAutomatic detection of Malaria from blood smear images can go a long way in

our battle against this deadly disease. In this project, using deep learning, we

will try to predict whether the sample is from an infected person.

Project 7 - Food Demand ForecastingA meal delivery company has a number of dispatch centers in multiple cities.

You have to help these centers with demand forecasting for upcoming weeks

so that these centers can plan the stock of raw materials accordingly.

Project 8 - Real-Life Project - Descriptive Analytics of FIFA 19 PlayersIn this project, we will take a real-life dataset of FIFA19 players and do descrip-

tive analytics on it. We will go through the process of how data is understood,

transformed, and how we get answers and insights from our data.

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FAQs

What is this specialization about?This is an online specialization in you will learn about Machine Learning. You will learn Python programming, various Machine Learning algorithms, SQL for Data Science, as well as learn toace data science interviews. At the end of this specialization, you will have a solid understanding of Machine Learning and thus, can start building your career in the field ofMachine Learning.

How will the training be imparted?You will be taught using pre-recorded videos and text tutorials. The training has quizzes, assignments, and tests to help you learn better. At the end of the training, you will attempt aproject to get hands-on practice of what you learned during your training.

What is the duration of this training?This is a 6-month long training program.

What are the timings of this training program?As this is a purely online training program, students can choose to learn at any time of the day. The students can decide the timing according to theirconvenience.

Who can join? I am a beginner/advanced user, can I learnMachine Learning?This training is suited for beginners (who have no prior knowledge of Machine Learning) as well as advanced users (with some knowledge of Machine Learn-ing). The concepts in the training program will be taught to you from scratch. Thus, anyone who is willing to learn and has interest to build a career in the field of Machine Learning can opt for this training program.

Are there any prerequisites for joining this program?This program is for beginners. There are no prerequisites.

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Will there be any project that I will get to work on?Yes, you will build 8 projects during the specialization.

What hardware/software are required for doing this training?You would require a computer with a working internet connection (minimum system requirements are 4GB RAM and i3 processor). All the necessarysoftware (if required) are uploaded online which can be downloaded during the training.

Will there be a certificate provided at the end of the training?Yes, a certificate will be provided by Internshala upon completion of the spe-cialization. Students may download a soft copy of the certificate through our portal.

Will I be able to download the training content?Yes, you will be able to download

Can the material be used by a group of students?No. These training programs are meant for individual users. Multiple users will not be allowed to access the portal using the same account.

If you have any queries or doubts regarding the training, please write to us attrainings@internshala.com or call us at +91 844 844 4853