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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Announcing the Machine Learning Starter Program! [FREE Access for 14 Days!]

Pranav Dar
Pranav Dar Last Updated : 01 Apr, 2020
4 min read

Ideal Time to Start your Machine Learning Journey!

Picture this – you want to learn all about machine learning but just can’t find the time. There’s too much to do, whether that’s our professional work or your exams are around the corner. Suddenly, you have a lot of time on your hands and a once-in-a-lifetime opportunity to learn machine learning and apply it!

That’s exactly the opportunity in front of you right now. We are living in unprecedented times with half the world in complete lockdown and following social distancing protocols. There are two types of people emerging during this lockdown:

  • Those who are watching movies and surfing the internet to pass the time
  • Those who are eager to pick up a new skill, learn a new programming language, or apply your machine learning knowledge

If you’re in the latter category – we are thrilled to announce the:

Machine Learning Starter Program!

machine_learning_starter_program

You can use the code ‘LOCKDOWN’ to enroll in the Machine Learning Starter Program for FREE! You will have access to the course for 14 days from the day of your enrollment. Post this, the fee of the Program will be Rs. 4,999 (or $80).

 

What is the Machine Learning Starter Program?

The Machine Learning Starter Program is a step-by-step online starter program to learn the basics of Machine Learning, hear from industry experts and data science professionals, and apply your learning in machine learning hackathons!

This is the perfect starting point to ignite your fledging machine learning career and take a HUGE step towards your dream data scientist role.

The aim of the Machine Learning Starter Program is to:

  • Help you understand how this field is transforming and disrupting industries
  • Acquaint you with the core machine learning algorithms
  • Enhance and complement your learning through competition and hackathon exposure

We believe in a holistic learning approach and that’s how we’ve curated the Machine Learning Starter Program.

 

What does the Machine Learning Starter Program include?

There are several components in the Machine Learning Starter Program:

  • Machine Learning Basics Course
  • Expert Talks on various machine learning topics by industry practitioners
  • 2 awesome machine learning hackathons
  • E-book on “Machine Learning Simplified”

Let’s explore each offering in a bit more detail.

 

Machine Learning Basics Course

machine_learning_starter_program_course

This course provides you all the tools and techniques you need to apply machine learning to solve business problems. Here’s what you’ll learn in the Machine Learning Basics 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
  • How to evaluate your machine learning models and improve them through Feature Engineering
  • Improve and enhance your machine learning model’s accuracy through feature engineering

Expert Talks

talks_machine_learning_starter_program

There is no substitute for experience.

This course is an amalgamation of various talks by machine learning experts, practitioners, professionals and leaders who have decades upon decades of learning experience with them. They have already gone through the entire learning process and they showcase their work and thought process in these talks.

This course features rockstar data science experts like Sudalai Rajkumar (SRK), Professor Balaraman Ravindran, Dipanjan Sarkar, Kiran R and many more!

 

Machine Learning Hackathons

machine_learning_starter_pack

The Machine Learning Starter Program features two awesome hackathons to augment your learning:

  • JanataHack
  • Machine Learning Starter Program hackathon

Come, interact with the community, apply your machine learning knowledge, hack, have fun and stay safe.

E-Book on “Machine Learning Simplified”

ebook_machine_learning_starter_program

This e-book aims to provide an overview of machine learning, recent developments and current challenges in Machine Learning. Here’s a quick summary of what’s included:

  • What is Machine Learning?
  • Applications of Machine Learning
  • How do Machines Learn?
  • Why is Machine Learning getting so much attention?
  • Steps required to Build a Machine Learning Model
  • How can one build a career in Machine learning?

and much more!

 

Who is the Machine Learning Starter Program for?

The Machine Learning Starter Program is for anyone who:

  • Is a beginner in machine learning
  • Wants to kick start their machine learning journey
  • Wants to learn about core machine learning algorithms
  • Is interested in a practical learning environment
  • Wants to practice and enhance their existing machine learning knowledge

 

So, what are you waiting for? Enroll in the Machine Learning Starter Program for FREE using the code ‘LOCKDOWN’ and begin your learning journey today!

Machine Learning Coursemachine learning ebookmachine learning hackathonmachine learning starter program
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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Responses From Readers

Clear

Chandan
Chandan

Today I received a mail from analytics vidya regarding free 14 days trial to their ML Course. But unfortunately unable to enroll due to some cookies issues. I cleared the cookies of my browser checked in other browser as well but not able to enroll. Error: Too many redirects. Guys atleast make your website functional to use. If a person cannot enroll in the course. Then what will you teach in the course. Good bye 👋

123

Pranav Dar
Pranav Dar

Hi Chandan, The link is working now. It was a temporary technical glitch that has been corrected so you can go ahead and enroll yourself.

123
Sudeep
Sudeep

Hi, Unable to register. It gives an error "This page isn’t working id.analyticsvidhya.com redirected you too many times."

123

Pranav Dar
Pranav Dar

Hi Sudeep - The page is working - can you please check again?

123

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