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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.
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
- Prior to building your machine learning model, learn how to reduce dimensions using techniques like Principal Component Analysis (PCA) and t-SNE
- 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
- 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
Pre-requisites for the Applied Machine Learning course
This course requires no prior knowledge about Data Science or any tool.
Know more about Applied Machine Learning
Tools Covered in Applied Machine Learning Course
Course curriculum
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1
Welcome to the Applied Machine Learning Course
- Welcome to the Course
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2
Introduction to Data Science and Machine Learning
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3
Introduction to the Course
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4
Setting up your system
- Installation steps for Windows
- Installation steps for Linux
- Installation steps for Mac
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5
Python for Data Science
- Brief Introduction to Python
- Quiz: Introduction to Python
- Theory of Operators
- Understanding Operators in Python
- Quiz: Theory of Operators
- Understanding variables and data types
- Variables and Data Types in Python
- Quiz: Understanding variables and data types
- Understanding Conditional Statements
- Implementing Conditional Statements in Python
- Quiz: Conditional Statements
- Understanding Looping Constructs FREE PREVIEW
- Implementing Looping Constructs in Python
- Quiz: Looping Constructs
- Understanding Functions
- Implementing Functions in Python
- Quiz: Functions in Python
- A brief introduction to data structure
- Quiz: Data Structure
- Understanding the concept of Lists
- Implementing Lists in Python
- Quiz: Lists in Python
- Understanding the concept of Dictionaries
- Implementing Dictionaries in Python
- Quiz: Dictionaries in Python
- Understanding the concept of Standard Libraries
- Quiz: Standard Libraries
- Reading a CSV File in Python - Introduction to Pandas
- Reading a CSV file in Python - Implementation
- Quiz: Reading a csv file in Python
- Understanding dataframes and basic operations
- Reading dataframes and conduct basic operations in Python
- Quiz: DataFrames and basic operations
- Indexing a Dataframe
- Quiz: Indexing DataFrames
- Exercise
- Instructions
- Quiz - Python Module
- Python Coding Challenge
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6
Statistics For Data Science
- Introduction to statistics
- Mode of the data
- Understanding the various variable types
- Quiz: Understanding Variable Types
- Mean of the data
- Outliers in the datasets
- Quiz: Outlier in the datasets
- Median of the dataset
- Quiz: Mode , Mean and Median
- Spread of the data
- Quiz: Spread of the data
- Variance of the data
- Quiz: Variance of the data
- Standard Deviation of the data
- Quiz: Standard Deviation of the data
- Frequency Tables
- Quiz: Frequency Tables
- Histograms
- Quiz: Histograms
- Introduction to Probability
- Quiz: Introduction to probability
- Calculating Probabilities of events
- Quiz: Calculating Probabilities of events
- Bernoulli Trials and Probability Mass Function
- Quiz: Bernoulli Trials and PMF
- Probabilities for Continuous Random Variables
- Quiz: Probabilities for continuous random variable
- The Central Limit Theorem
- Quiz: Central Limit Theorem
- Properties of the Normal Distribution
- Quiz: Properties of Normal distribution
- Using the Normal Curve for Calculations
- Quiz: Normal Curve for calculations
- Z score Part 1
- Understanding the Z tables
- Quiz: Z scores
- Z score part 2
- Introduction to Inferential Statistics
- Quiz: Introduction to Inferential Statistics
- Short Review
- Quiz: Review
- Mean Estimation
- Confidence Interval and Margin of Error
- Quiz: CI and Margin of error
- Introduction to Hypothesis Testing
- Quiz: Hypothesis testing
- Steps to perform hypothesis testing
- Directional Non Directional hypothesis
- Quiz: Directional and Non Directional hypothesis
- Understanding Errors while Hypothesis Testing
- Quiz: Errors while Hypothesis testing
- Understanding T tests
- Quiz: Understanding T tests
- Degree of Freedom
- T-Critical Value
- Quiz: T-Critical Value
- Steps to perform T-Test
- Quiz: Steps to perform T-Test
- Conducting One sample T test
- Quiz: One sample T tests
- Paired T tests
- Quiz: Paired T tests
- 2 Sample T tests
- Quiz: 2 sample T tests
- Chi Squared Tests
- Quiz: Chi squared tests
- Correlation
- Quiz: Correlation
- Conclusion
- Module Test
- Instructions
- Quiz - Statistics Module
- Statistics Coding Challenge
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7
Basics Steps of Machine Learning and EDA
- Introduction to Predictive Modeling
- Quiz: Introduction to Predictive Modeling
- Types of Predictive Models
- Quiz: Types of Prediction Models
- Stages of Predictive Modeling FREE PREVIEW
- Quiz: Stages of Predictive Modeling
- Understanding Hypothesis Generation
- Quiz: Hypothesis Generation
- Data Extraction
- Understanding Data Exploration
- Quiz: Data Extraction and Exploration
- Reading the data into Python
- Reading the data into Python : Implementation
- Quiz: Reading Data into Python
- Variable Identification
- Variable Identification : Implementation
- Quiz: Variable Identification
- Univariate analysis for Continuous Variables
- Univariate Analysis for Continuous Variables : Implementation
- Quiz: Univariate analysis for Continuous variables
- Understanding Univariate Analysis for Categorical Variables
- Univariate analysis for Categorical Variables : Implementation
- Quiz: Univariate Analysis for Categorical Variables
- Understanding Bivariate Analysis
- Quiz: Bivariate Analysis
- Bivariate Analysis : Implementation
- Quiz: Bivariate Analysis - Implementation
- Understanding and treating missing values
- Quiz: Treating missing values
- Treating missing values : Implementation
- Quiz: Treating missing values - Implementation
- Understanding Outlier Treatment
- Quiz: Outlier treatment
- Outlier Treatment in Python
- Quiz: Outlier Treatment in Python
- Understanding Variable Transformation
- Quiz: Transforming variables
- Variable Transformation in Python
- Quiz: Variable Transformation in Python
- Basics of Model Building
- Quiz: Basics of Model Building
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8
Data Manipulation and Visualization
- Sorting Dataframes
- Merging Dataframes
- Quiz: Sorting and Merging dataframes
- Aggregating data
- Apply function
- Quiz: Aggregating data and Apply function
- Basics of Matplotlib
- Data Visualization using Matplotlib
- Quiz: Matplotlib
- Basics of Seaborn
- Data Visualization using Seaborn
- Quiz: Seaborn
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9
Project: EDA - Customer Churn Analysis
- Understanding the Problem Statement
- Understanding the Data
- Understanding the NYC Taxi Trip Duration Problem
- Assignment: EDA
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10
Share your Learnings
- Write for Analytics Vidhya's Medium Publication
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11
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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12
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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13
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
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14
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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15
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
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16
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)
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17
Project: Customer Churn Prediction
- Predicting whether a customer will churn or not
- Assignment: NYC taxi trip duration prediction
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18
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
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19
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
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20
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
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21
Share your Learnings
- Write for Analytics Vidhya's Medium Publication
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22
Project: NYC Taxi Trip Duration prediction
- Exploring the NYC dataset
- Predicting the NYC taxi trip duration
- Predicting the NYC taxi trip duration
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23
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
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24
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
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25
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
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26
Project: Web Page Classification
- Understanding the Problem Statement
- Understanding the Data
- Building a Web Page Classifier
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27
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?
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28
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
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29
Project: Ensemble Model on NYC Taxi Trip Duration Prediction
- Predicting the NYC Taxi Trip Duration
- Prediction the NYC Taxi Trip Duration: Dataset
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30
Share your Learnings
- Write for Analytics Vidhya's Medium Publication
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31
Hyperparameter Tuning
- Introduction to Hyperparameter Tuning
- Different Hyperparameter Tuning methods
- Quiz: Hyperparameter Tuning
- Implementing different Hyperparameter Tuning methods
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32
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
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33
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
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34
Project: Malaria Detection using Blood Cell Images
- Understanding the Problem Statement
- Detecting Malaria using Blood Cell Images
- Dataset: Malaria Detection using Blood Cell Images
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35
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
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36
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
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37
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
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38
Automated Machine Learning
- Introduction to Automated Machine Learning
- Introduction to MLBox
- Implementing MLBox
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39
Introduction to Neural Network
- Understanding the Problem
- Introduction to Neural Network
- Quiz: Introduction to Neural Network
- Understanding Forward Propagation
- Quiz: Forward Propogation
- Math Behind Forward Propagation
- Quiz: Math Behind Forward Propagation
- Error and Reason for Error
- Quiz: Error and Reason for Error
- Gradient Descent Intuition
- Understanding Maths Behind Gradient Descent
- Quiz: Gradient Descent
- Optimizers
- Quiz: Optimizer
- Back Propagation
- Quiz: Back Propagation
- Why Numpy?
- Quiz: Why Numpy?
- Understanding the Steps in Numpy
- Quiz: Understanding the Steps in Numpy
- Defining Parameters in Numpy
- Quiz: Defining Parameters in Numpy
- Implementing Forward Propagation
- Quiz: Implementing Forward Propagation
- Implementing Backward Propagation
- Quiz: Implementing Backward Propagation
- Notebook: Neural network from scratch
- Why Keras?
- Quiz: Why Keras?
- Neural Network in Keras
- Quiz: Neural Network in Keras
- Dataset: Emergency vs Non-Emergency Classification dataset
- How to handle image data?
- Quiz: How to handle Image data
- Exploring the Emergency Classification Dataset
- Quiz: Exploring the Emergency Classification Dataset
- Loading and Pre-Processing Dataset
- Quiz: Loading and Pre-Processing Dataset
- Solving the Challenge
- Quiz: Solving the challenge
- Hyperparameter Tuning
- Quiz: Hyperparameter Tuning
- Notebook: Simple Neural Network using keras
- Installation Steps
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40
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
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41
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
Machine Learning Project 1
NYC Taxi Trip Duration Prediction
Uber, Lyft, Ola and many more online ride hailing services are trying hard to use their extensive data to create data products such as pricing engines, driver allotment etc. To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied or in other words the trip duration. This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from TLC commission New York.
Machine Learning Project 2
Customer Churn Prediction
A Bank wants to take care of customer retention for their product; savings accounts. The bank wants you to identify customers likely to churn balances below the minimum balance in next quarter. You have the customers information such as age, gender, demographics along with their transactions with the bank. Your task as a data scientist would be to predict the propensity to churn for each customer.
Machine Learning Project 3
Web Page Classification
Classification of Web page content is vital to many tasks in Web information retrieval such as maintaining Web directories and focused crawling which is used to selectively seek out web pages that are relevant to a pre-defined set of topics. In this project, you will learn to build a web page classifier that can classify the web pages into their respective classes.
Machine Learning Project 4
Malaria diagnosis involves close examination of the blood smear at 100x magnification. This is followed by a manual counting process wherein experts count the number of Red blood cells impacted by parasites. Automatic detection of Malaria from blood smear image is a scalable solution and can save a lot of hours for healthcare industry going a long way in our battle against this deadly disease. In this project, we try to identify from blood smears using deep learning to predict whether the sample is taken from an infected person.
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.
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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