About Applied Machine Learning - Beginner to Professional Course
Machine Learning is re-shaping 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 real-world business problems.
Pre-requisites 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
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Understand how Machine Learning and Data Science are disrupting multiple industries today
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Linear and Logistic Regression algorithms for building machine learning models
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Understand how to solve Classification and Regression problems in machine learning
Know more about Applied Machine Learning
What will you learn in the ‘Applied Machine Learning’ course?
- 100 Lesssons
- 3 Real Life Projects from Industry
Tools Covered in Applied Machine Learning Course
Projects for Applied Machine Learning Course
NYC Taxi Trip Duration Prediction
Customer Churn Prediction
Predict Survivors from Titanic
Course curriculum
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1
Introduction to Data Science and Machine Learning
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2
Setting up your system
- Installation steps for Windows
- Installation steps for Linux
- Installation steps for Mac
- Course Handouts
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3
Introduction to Python
- Introduction to Python
- Introduction to Jupyter Notebook
- Download Python Module Handouts
- Expert talk Andrew
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4
Variables and Data Types
- Introduction to Variables
- Implementing Variables in Python
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5
Operators
- Introduction to Operators
- Implementing Operators in Python
- Quiz: Operators
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6
Conditional Statements
- Introduction to Conditional Statements
- Implementing Conditional Statements in Python
- Quiz: Conditional Statements
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7
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
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8
Data Structures
- Introduction to Data Structures
- List and Tuple
- Implementing List in Python
- 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
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9
String Manipulation
- Introduction to String Manipulation
- Quiz: String Manipulation
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10
Functions
- Introduction to Functions
- Implementing Functions in Python
- Quiz: Functions in Python
- Lambda Expression
- Quiz: Lambda Expressions
- Recursion
- Implementing Recursion in Python
- Quiz: Recursion
- Expert Talk: Rajeev Shah
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11
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
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12
Handling Text Files in Python
- Handling Text Files in Python
- Quiz: Handling Text Files
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13
Introduction to Python Libraries for Data Science
- Important Libraries for Data Science
- Quiz: Important Libraries for Data Science
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14
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 Scikit-Learn in Python
- Basics of Statsmodels in Python
- Unlock the Data Science Universe with Andrew Engel: Insights, Innovations, and Beyond!
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15
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
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16
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
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17
Sorting and Aggregating Data in Pandas
- Preprocessing, Sorting and Aggregating Data
- Sorting the Dataframe
- Quiz: Sorting Dataframes
- Concatenating Dataframes in Pandas
- Concept of SQL-Like Joins in Pandas
- Implementing SQL-Like 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
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18
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
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19
Machine Learning Lifecycle
- 6 Steps of Machine Learning Lifecycle
- Introduction to Predictive Modeling
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20
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
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21
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
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22
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
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23
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
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24
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
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25
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
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26
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
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27
Central Limit theorem
- Important Terminologies
- Central Limit Theorem
- CLT: Implementation
- Quiz: Central Limit Theorem
- Confidence Interval and Margin of error
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28
Bivariate analysis - Introduction
- Introduction to Bivariate Analysis
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29
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: Continuous-Continuous Variables
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30
Continuous Categorical
- Tabular and Graphical Methods
- Introduction to hypothesis Testing
- P-Value
- One Sample z-test
- Two Sampled z-test
- Quiz: Hypothesis Testing and Z scores
- T-Test
- T-Test vs Z-Test
- Quiz: T tests
- Performing Bivariate Analysis on Catagorical - Continuous variables
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31
Categorical Categorical
- Tabular and Graphical Methods
- Chi-Squared Test
- Quiz: Chi squared tests
- Bivariate Analysis for Categorical Categorical Variables
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32
Multivariate Analysis
- Multivariate Analysis
- Multivariate Analysis Implementation
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33
Assignments
- Understanding the NYC Taxi Trip Duration Problem
- Assignment: EDA
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34
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
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35
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
- AUC-ROC
- Quiz: AUC-ROC
- Log loss
- Quiz: Log loss
- Evaluation Metrics for Regression
- Quiz: Evaluation Metrics for Regression
- R2 and Adjusted R2
- Quiz: R2 and Adjusted R2
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36
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
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37
Build Your First ML Model: k-NN
- Introduction to k-Nearest Neighbours FREE PREVIEW
- Quiz: Introduction to k-Nearest 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 k-Nearest Neighbours algorithm
- Quiz: Implementing k-Nearest Neighbours algorithm
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38
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 Hold-Out Validation
- Quiz: Understanding Hold-Out Validation
- Implementing Hold-Out Validation
- Quiz: Implementing Hold-Out Validation
- Understanding k-fold Cross Validation
- Implementing k-fold Cross Validation
- Quiz: Understanding k-fold Cross Validation
- Quiz: Implementing k-fold Cross Validation
- Bias Variance Tradeoff
- Quiz: Bias Variance Tradeoff
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39
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 (Part-1)
- Implementing Linear Regression (Part-2)
- 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: Multi-Class 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)
- Understanding Cost function
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40
Project: Customer Churn Prediction
- Predicting whether a customer will churn or not
- Assignment: NYC taxi trip duration prediction
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41
Feedback about the course
- Rate your experience!!
Certificate of Completion
Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
Instructor(s)
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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. -
Senior Content Strategist and BA Program Lead, Analytics Vidhya
Pranav Dar
Pranav is the Senior Content Strategist and BA Program Lead at Analytics Vidhya. He has written over 300 articles for AV in the last 3 years and brings a wealth of experience and writing know-how to this course. He has a decade of experience in designing courses, creating content and writing articles that people love to read. Pranav is also an instructor on 14+ courses on Analytics Vidhya and is a passionate sports analytics blogger as well.
Here's what our students say about Applied Machine Learning course
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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 beginner-friendly , 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 beginner-friendly , 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.
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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
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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 pre-requisites, the underlying intuition behind several machine learning models and then go on to solve case studies using Machine Learning concepts.
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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.
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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.
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Do I need to install any software before starting the course ?
You will get information about all installations as part of the course.
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What is the refund policy?
The fee for this course is non-refundable.
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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.
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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.
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Which machine learning tools are we using in this course?
Fee for this course is INR 14,999
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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
Customer Support for our Courses & Programs
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