What is Machine Learning?
Machine Learning is the science of teaching machines how to learn by themselves. Machine Learning is reshaping and revolutionizing the world and disrupting industries and job functions globally.
Machine learning is so extensive that you probably use it numerous times a day without knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage.
In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them to realworld business problems.
What will I learn from this course?

Python libraries like Numpy, Pandas, etc. to analyze your data efficiently.

Importance of Statistics and Exploratory Data Analysis (EDA) in the data science field.

Linear Regression, Logistic Regression, and Decision Trees for building machine learning models.

Understand how to solve Classification and Regression problems using machine learning

How to evaluate your machine learning models using the right evaluation metrics?

Improve and enhance your machine learning model’s accuracy through feature engineering
Prerequisites:
This course requires no prior knowledge of Data Science or any tool.
Projects covered in this course
1. Customer Churn Prediction
2. NYC Taxi Trip Duration Prediction
Tools Covered in this Course
Course curriculum

1
Overview of the Course
 Overview of the Course FREE PREVIEW
 Knowing each other FREE PREVIEW
 AI&ML Blackbelt Plus Program (Sponsored)

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
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

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

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

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

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

20
Machine Learning Lifecycle
 6 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
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

24
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

25
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

26
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

27
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

28
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)

29
Project: Customer Churn Prediction
 Predicting whether a customer will churn or not

30
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

31
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
 Quiz: 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
 Quiz: Implementing Feature Tools

32
Project: NYC Taxi Trip Duration prediction
 Exploring the NYC dataset
 Predicting the NYC taxi trip duration
 Predicting the NYC taxi trip duration
Certificate of Completion
Instructor
Kunal Jain, Founder & CEO, Analytics Vidhya
FAQs

Who should take the Free Machine Learning Certification Course for Beginners?
This course is meant for people looking to learn Machine Learning. We will start with understanding Python for Data Science, the importance of statistics and EDA, the underlying intuition behind several machine learning algorithms and then go on to solve case studies using Machine Learning concepts.

Do I need to install any software before starting the course?
You will get information about all installations as part of the course.

Do I need to take the modules in a specific order?
It is highly recommended to take 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 Free Machine Learning Certification Course for Beginners.