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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

Your Ultimate Learning Path to Become a Data Scientist and Machine Learning Expert in 2020

Pranav Dar
Pranav Dar Last Updated : 21 Apr, 2020
4 min read

Introducing the Learning Path to become a Data Scientist in 2020!

Learning paths are easily one of the most popular and in-demand resources we curate at the start of the new year. We’ve received a ton of queries recently asking when we would be releasing the learning paths for 2020.

And here we are – we are thrilled to present the first learning path of 2020 for our community!

data science learning path

The learning path for 2020 is the ultimate and most comprehensive collection of resources put together in a structured manner. This learning path is for anyone who wants to make a career in data science. So whether you are a fresher, have a few years of work experience, or are a mid-level professional – this data science learning path is for you.

This year, based on the wonderful reception we received last year, we have expanded the scope of these learning paths. We will be releasing 4 different learning paths, each focused on where you stand in your learning journey:

 

Why you Should Enroll in this Data Science Learning Path

There are way too many learning resources out there. I’m sure anyone who has tried to study from different sources will understand this. It can be overwhelming for anyone starting out, regardless of where you stand in your career.

And hence we created these learning paths.

We take away the pain, time and effort of going through hundreds of resources on data science, machine learning, deep learning, Natural Language Processing (NLP) and Computer Vision. Our aim is to bring forward the best learning resources for you to help you streamline your data science learning journey.

 

You can access the complete and most comprehensive learning path to become a data scientist in 2020 here. You would need to register yourself on the Courses platform to enroll yourself. This will enable you to keep track of what you have covered as you progress in your machine learning journey.

 

Summary – Learning Path to become a Data Scientist in 2020

Here’s a summary of what you can expect to learn (and the steps you should follow) using this learning path:

  • Getting Started with Data Science and Python: The start of your journey to becoming a data scientist! Understand what a data scientist does, the various terms associated with data science, and start getting acquainted with the Python programming language
  • Statistics and Mathematics: The backbone of data science. Some of the key concepts you’ll cover are probability, inferential statistics, and get a hang of how to perform exploratory data analysis (EDA). This will also include the basics of linear algebra (another core machine learning topic)
  • Machine Learning Basics: Welcome to the world of machine learning! This section is all about introducing you to the basic machine learning algorithms and techniques, including linear regression, logistic regression, decision trees, Naive Bayes, support vector machines (SVM), among others
  • Ensemble Learning: Time to deep dive into advanced machine learning topics. Understand what ensembling is, the different ensemble techniques, and start working on datasets to gain a hands-on practical experience
  • Time Series: One of the more complex topics in this space. This deserves an entire section by itself so we have also included a hands-on project to get you familiar with the various time series concepts and how they work in the real-world
  • Matrix Algebra and Recommendation Systems: Why matrix algebra, you ask? Well, you can’t really get serious about learning how recommendation engines work without it! So this section, covered in June in the learning path, is all about these two trending and relevant concepts. This will cover dimensionality reduction techniques like Principal Component Analysis (PCA) along with recommendation engine projects
  • Neural Networks (and Deep Learning): Yes, deep learning is part of the data science learning path. Given the rapid rise and adoption of deep learning applications, this is potentially a very relevant part of your role as a data scientist. You would learn about neural networks and will also pick up a popular deep learning framework called Keras (you can choose others based on your preference – such as PyTorch)
  • Computer Vision: Computer vision is easily the most in-demand deep learning field in the industry. After you’re familiar with the above section, dive into different kinds of computer vision problems and learn as you go
  • Natural Language Processing (NLP): The hottest field in any industry. Businesses are tripping over each other to land the best NLP talent – this is a great time to start working in NLP! From Google’s BERT to Facebook’s RoBERTa, start getting acquainted with the state-of-the-art NLP frameworks

 

As mentioned above, you can access the full data science learning path here. Register yourself and begin your machine learning journey today! You can track your progress throughout the year as you check off milestones and inch closer to your dream role.

We have also provided an illustrated version of this data science learning path below which paints a month-by-month picture. You can print this out and use this as a checklist. And if you put forward your best efforts and follow this learning path – you’d be in a great position to start cracking data science interviews by the end of 2020.

You can download a high definition version of the infographic here.

data science learning path

data science learning pathdata scientistlearning pathlearning path 2020machine learningmachine learning path
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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