About Applied Machine Learning 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, Logistic Regression, Decision Tree and Random Forest algorithms for building machine learning models
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Understand how to solve Classification and Regression problems in machine learning
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Ensemble Modeling techniques like Bagging, Boosting, Support Vector Machines (SVM) and Kernel Tricks.
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Learn dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE
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Evaluate your machine learning models and improve them through Feature Engineering
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Learn Unsupervised Machine Learning Techniques like k-means clustering and Hierarchical Clustering
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Learn how to work with different kinds of data for machine learning problems (tabular, text, unstructured)
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Improve and enhance your machine learning model’s accuracy through feature engineering
Tools Covered in Applied Machine Learning Course
Projects for Applied Machine Learning Course
NYC Taxi Trip Duration Prediction
Customer Churn Prediction
Web Page Classification
Malaria Detection from blood cell Images
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
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3
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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4
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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5
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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6
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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7
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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8
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
- Quiz: Implementing Linear Regression
- Generalized Linear Models
- Quiz: Generalized Linear Models
- Introduction to Logistic Regression
- Quiz: 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
- Quiz: Challenges with Linear regression
- Introduction to Regularisation
- Quiz: Introduction to Regularization
- Implementing Regularisation
- Coefficient estimate for ridge and lasso (Optional)
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9
Project: Customer Churn Prediction
- Predicting whether a customer will churn or not
- Assignment: NYC taxi trip duration prediction
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10
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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11
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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12
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
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13
Share your Learnings
- Write for Analytics Vidhya's Medium Publication
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14
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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15
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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16
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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17
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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18
Project: Web Page Classification
- Understanding the Problem Statement
- Understanding the Data
- Building a Web Page Classifier
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19
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?
- Quiz: Why do ensemble models work well?
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20
Advance Ensemble Techniques
- Introduction to Stacking
- Implementing Stacking
- Quiz: Introduction to 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
- Quiz: Implementing Random forest
- Introduction to boosting
- Quiz: Introduction to Boosting
- Gradient Boosting Algorithm (GBM)
- Quiz: Gradient Boosting Algorithm
- Math Behind GBM
- Implementing GBM
- Quiz: Implementing GBM
- Extreme Gradient Boosting (XGBM)
- Implementing XGBM
- Quiz: Implementing XGBM
- Quiz: Extreme Gradient Boosting
- Adaptive Boosting
- Implementing Adaptive Boosting
- Quiz: Adaptive Boosting
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21
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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22
Share your Learnings
- Write for Analytics Vidhya's Medium Publication
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23
Hyperparameter Tuning
- Introduction to Hyperparameter Tuning
- Different Hyperparameter Tuning methods
- Quiz: Hyperparameter Tuning
- Implementing different Hyperparameter Tuning methods
- Quiz: Implementing different Hyperparameter tuning
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24
Support Vector Machine
- Understanding SVM Algorithm
- Quiz: Support Vector Machine
- SVM Kernel Tricks
- Kernels and Hyperparameters in SVM
- Quiz: Kernels and Hyperparameters in SVM
- Implementing Support Vector Machine
- Quiz: Kernel Tricks
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25
Working with Image Data
- Introduction to Images
- Understanding the Image data
- Quiz: Understanding the Image Data
- Understanding transformations on Images
- Understanding Edge Features
- Quiz: Understanding Edge Features
- Histogram of Oriented Features (HOG)
- Quiz: HOG
- Quiz: Image Features
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26
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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27
Advance Dimensionality Reduction
- Introduction to Principal Component Analysis
- Steps to perform Principal Component Analysis
- Quiz: Principal Component Analysis
- Computation of the Covariance Matrix
- Quiz: Covariance Matrix
- Finding the Eigenvectors and Eigenvalues
- Quiz: Finding eigenvectors and eigenvalues
- Understanding the MNIST dataset
- Quiz: Introduction to 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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28
Unsupervised Machine Learning Methods
- Introduction to Clustering
- Quiz: Introduction to Clustering
- Applications of Clustering
- Quiz: 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
- Quiz: Challenges with K means clustering
- How to Choose Right k-Value
- Quiz: How to choose the right value of k
- K-Means Implementation
- Quiz: K-Means Implementation
- Hierarchical Clustering
- Implementation Hierarchical Clustering
- Quiz: Hierarchical Clustering
- How to Define Similarity between Clusters
- Quiz: How to define similarity between two clusters
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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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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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.
Applied Machine Learning Assessment Test
Customer Support for our Courses & Programs
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