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.

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Key Takeaways from Applied Machine Learning 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 techniques like Bagging, Boosting, Support Vector Machines (SVM) and Kernel Tricks.

  • Learn dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE

  • Evaluate your machine learning models and improve them through Feature Engineering

  • Learn Unsupervised Machine Learning Techniques like k-means clustering and Hierarchical Clustering

  • Learn how to work with different kinds of data for machine learning problems (tabular, text, unstructured)

  • Improve and enhance your machine learning model’s accuracy through feature engineering

Know more about Applied Machine Learning

 

What will you learn in the ‘Applied Machine Learning’ course?

  • 557 Lesssons
  • 6 Real Life Projects from Industry
Download detailed Curriculum
What will you learn in the ‘Applied Machine Learning’ course?

Industries using Machine Learning

  • Retail

  • Health Care

  • Finance

  • Manufacturing

  • Entertainment

  • Education

  • Telecom

  • Banking

  • and many more...

Tools Covered in Applied Machine Learning Course

  • python
  • pandas
  • numpy
  • matplotlib
  • keras
  • scikitlearn
  • seaborn
  • dask
  • mlbox

Projects for Applied Machine Learning Course

NYC Taxi Trip Duration Prediction
NYC Taxi Trip Duration Prediction
Customer Churn Prediction
Customer Churn Prediction
Web Page Classification
Web Page Classification
Malaria Detection from blood cell Images
Malaria Detection from blood cell Images
Predict Survivors from Titanic
Predict Survivors from Titanic
Sales Prediction for Large Super Markets
Sales Prediction for Large Super Markets

 

Download Project Details

Course curriculum

  • 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
    • Assignment: Data Structures
  • 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 Scikit-Learn 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
    • Assignment: Reading Data 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
    • Assignment: Subsetting and Modifying Pandas 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 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
  • 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
    • Assignment: Visualizing Patterns and Trends in Data
  • 20
    Machine Learning Lifecycle
    • 7 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
    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
  • 24
    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
  • 25
    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
  • 26
    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
  • 27
    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
  • 28
    Central Limit theorem
    • Important Terminologies
    • Central Limit Theorem
    • CLT: Implementation
    • Quiz: Central Limit Theorem
    • Confidence Interval and Margin of error
  • 29
    Bivariate analysis - Introduction
    • Introduction to Bivariate Analysis
  • 30
    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
  • 31
    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
  • 32
    Categorical Categorical
    • Tabular and Graphical Methods
    • Chi-Squared Test
    • Quiz: Chi squared tests
    • Bivariate Analysis for Categorical Categorical Variables
  • 33
    Multivariate Analysis
    • Multivariate Analysis
    • Multivariate Analysis Implementation
  • 34
    Assignments
    • Understanding the NYC Taxi Trip Duration Problem
    • Assignment: EDA
  • 35
    Build Your First Predictive Model
  • 36
    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
  • 37
    Preprocessing Data
    • Dealing with Missing Values in the Data
    • Replacing Missing Values
    • Imputing Missing Values in data
    • Working with Categorical Variables
    • Working with Outliers
    • Preprocessing Data for Model Building
  • 38
    Build Your First ML Model: k-NN
  • 39
    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
    • Quiz: Understanding k-fold Cross Validation
    • Implementing k-fold Cross Validation
    • Quiz: Implementing k-fold Cross Validation
    • Bias Variance Tradeoff
    • Quiz: Bias Variance Tradeoff
  • 40
    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
    • Implementing Logistic Regression
    • Quiz: Logistic Regression
    • Multiclass using Logistic Regression
    • Quiz: Multi-Class Logistic Regression
    • Challenges with Linear Regression
    • Introduction to Regularisation
    • Quiz: Introduction to Regularization
    • Implementing Regularisation
    • Coefficient estimate for ridge and lasso (Optional)
  • 41
    Project: Customer Churn Prediction
    • Predicting whether a customer will churn or not
    • Assignment: NYC taxi trip duration prediction
  • 42
    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
  • 43
    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
    • Chi-Square
    • Quiz: Chi-Square
    • 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
  • 44
    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
  • 45
    Share your Learnings
    • Write for Analytics Vidhya's Medium Publication
  • 46
    Project: NYC Taxi Trip Duration prediction
    • Exploring the NYC dataset
    • Predicting the NYC taxi trip duration
    • Predicting the NYC taxi trip duration
  • 47
    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
    • Bag-of-Words
    • Quiz: Bag-of-Words
    • Text Pre-processing
    • Quiz: Text Pre-processing
    • TF-IDF Features
    • Quiz: TF-IDF Features
    • Word Embeddings
    • Create word2vec Features
    • Quiz: Word Embeddings
  • 48
    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
  • 49
    Multiclass and Multilabel
    • Understanding how to solve Multiclass and Multilabel Classification Problem
    • Quiz: Multiclass and Multilabel
    • Evaluation Metrics: Multi Class Classification
    • Quiz: Evaluation Metrics for Multi Class Classification
  • 50
    Project: Web Page Classification
    • Understanding the Problem Statement
    • Understanding the Data
    • Building a Web Page Classifier
  • 51
    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?
  • 52
    Advance Ensemble Techniques
    • Introduction to Stacking
    • Implementing 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
    • Hyper-parameters of Random Forest
    • Quiz: Hyper-parameters 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
  • 53
    Project: Ensemble Model on NYC Taxi Trip Duration Prediction
    • Predicting the NYC Taxi Trip Duration
    • Prediction the NYC Taxi Trip Duration: Dataset
  • 54
    Share your Learnings
    • Write for Analytics Vidhya's Medium Publication
  • 55
    Hyperparameter Tuning
    • Introduction to Hyperparameter Tuning
    • Different Hyperparameter Tuning methods
    • Quiz: Hyperparameter Tuning
    • Implementing different Hyperparameter Tuning methods
  • 56
    Support Vector Machine
    • Understanding SVM Algorithm
    • Quiz: Support Vector Machine
    • SVM Kernel Tricks
    • Kernels and Hyperparameters in SVM
    • Implementing Support Vector Machine
    • Quiz: Kernel Tricks
  • 57
    Working with Image Data
    • Introduction to Images
    • Understanding the Image data
    • Quiz: Understanding the Image Data
    • Understanding transformations on Images
    • Understanding Edge Features
    • Histogram of Oriented Features (HOG)
    • Quiz: Image Features
  • 58
    Project: Malaria Detection using Blood Cell Images
    • Understanding the Problem Statement
    • Detecting Malaria using Blood Cell Images
    • Dataset: Malaria Detection using Blood Cell Images
  • 59
    Advance Dimensionality Reduction
    • Introduction to Principal Component Analysis
    • Steps to perform Principal Component Analysis
    • Quiz: Principal Component Analysis
    • Computation of the Covariance Matrix
    • Finding the Eigenvectors and Eigenvalues
    • Understanding the MNIST dataset
    • Implementing Principal Component Analysis
    • Quiz: Steps to perform PCA
    • Introduction to Factor Analysis
    • Steps to perform Factor Analysis
    • Quiz: Factor Analysis
    • Implementing Factor Analysis
    • Quiz: Implementing Factor Analysis
  • 60
    Unsupervised Machine Learning Methods
    • Introduction to Clustering
    • Quiz: Introduction to Clustering
    • Applications of Clustering
    • Evaluation Metrics for Clustering
    • Quiz: Evaluation Metrics for Clustering
    • Understanding K-Means
    • K-Means from Scratch Implementation
    • Quiz: Understanding K-Means
    • Challenges with K-Means
    • How to Choose Right k-Value
    • K-Means Implementation
    • Quiz: K-Means Implementation
    • Hierarchical Clustering
    • Implementation Hierarchical Clustering
    • Quiz: Hierarchical Clustering
    • How to Define Similarity between Clusters
  • 61
    Working with Large Datasets: Dask
    • Introduction to Dask
    • Understanding Dask Array and Dataframes
    • Implementing Dask Array and Dataframes
    • Machine Learning using Dask
    • Implementing Linear Regression model using Dask
  • 62
    Automated Machine Learning
    • Introduction to Automated Machine Learning
    • Introduction to MLBox
    • Implementing MLBox
  • 63
    Introduction to Neural Network
  • 64
    Forward and Backward Propagation
    • Understanding Forward Propagation Mathematically
    • Quiz - Understanding Forward Propagation Mathematically
    • Understanding Backward Propagation Mathematically
    • Quiz - Understanding Backward Propagation Mathematically
    • Backward Propagation: Matrix Form
    • Why Numpy?
    • Neural Network From scratch Using Numpy
    • Forward Propagation (using Numpy)
    • Backward Propagation (using Numpy)
    • Training network (using Numpy)
  • 65
    Activation Functions
    • Why do we need activation functions?
    • Quiz - Why do need activation functions
    • Linear Activation Function
    • Quiz - Linear Activation Function
    • Sigmoid and tanh
    • Quiz - Sigmoid and tanh
    • ReLU and Leaky ReLU
    • Quiz - ReLU and LeakyReLU
    • Softmax
    • Quiz - Softmax
    • Tips to selecting right Activation Function
  • 66
    Optimizers
    • Variants of Gradient Descent
    • Quiz - Variants of Gradient Descent
    • Problems with Gradient Descent
    • Quiz - Problems with Gradient Descent
    • RMSProp
    • Quiz - RMSPro
    • Adam
  • 67
    Loss Function
    • Introduction to loss function
    • Quiz - Introduction to Loss Function
    • Binary and Categorical Cross entropy / log loss
    • Quiz - Binary and Categorical cross entropy / log loss
  • 68
    Project: NN on structured Data
    • Overview of Deep Learning Frameworks
    • Quiz - Overview of deep learning frameworks
    • Understanding important Kears modules
    • Understanding the problem statement: Loan Prediction
    • Data Preprocessing: Loan Prediction
    • Quiz - Data Preprocessing: Loan Prediction
    • Steps to solve Loan Prediction Challenge
    • Loading loan prediction dataset
    • Defining the Model Architecture for loan prediction problem
    • Training and Evaluating model on Loan Prediction Challenge
    • Quiz - Training and Evaluating model on Loan Prediction Challenge
  • 69
    Assignment 1 - Big Mart
    • Assignment: Big Mart Sales Prediction
  • 70
    Model Deployment
    • Introduction
    • Applications of real time machine learning systems
    • Offline Batch Processing vs Real Time Systems
    • How to make real time systems
    • Fundamentals of Memory and Storage
    • Client Server Architecture
    • Exposing our API to the world
    • Assessing the scale of the problem
    • Requirements and Implementation Strategy of our Article Recommender System
    • Dataset: Data and Code for Recommendation System
    • Simple Text Matching System
    • Text Similarity Between Two Articles
    • Creating Similarity Model
    • Creating APIs for our application
    • Performance Analysis of APIs
    • Introduction to Git and Collaboration
    • Resource: Model Deployment
    • Module Test: Model Deployment
  • 71
    Interpretability of Machine Learning Models
    • Introduction to Machine Learning Interpretability
    • Framework and Interpretable Models
    • Model Agnostic Methods for Interpretability
    • Implementing Interpretable Model
    • Implementing Global Surrogate and LIME

Certificate of Completion

Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
Certificate of Completion

Instructor(s)

  • Kunal Jain

    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.
  • Sunil Ray

    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 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 globally-known 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.

Here's what our students say about Applied Machine Learning course

  • 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...

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    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 and the material also a good helps in easily understand.

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  • Variables and Data Types Great!

    DANNY DOEGAN

    Variables and Data Types Great!

    Variables and Data Types Great!

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  • 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...

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    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 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.

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FAQ

  • 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.

  • 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 non-refundable.

  • 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 machine learning certificate upon completion of the course?

    Yes, you will be given a certificate upon satisfactory completion of the Applied Machine Learning course.

  • Which machine learning tools are we using in this course?

    Fee for this course is INR 14,999

  • 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.

Applied Machine Learning Assessment Test

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