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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

How Long Does It Take to Learn Machine Learning?

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Analytics Vidhya Last Updated : 21 Jul, 2023
5 min read

Introduction

Machine learning, a rapidly growing field, has captured the interest of countless aspiring individuals. However, the question of how much time it takes to learn machine learning is common and significant. In this article, we will delve into the factors that influence the learning duration, present a recommended learning path, estimate time requirements for various learning stages, explore strategies for optimizing the learning process, and showcase valuable learning resources and platforms. Whether you’re a beginner or seeking to enhance your skills, this guide will help you navigate your machine learning journey with clarity and confidence.

Table of contents

What is Machine Learning?

Machine learning is an AI discipline that empowers computers to learn and improve from experience without explicit programming. It involves algorithms that analyze data, identify patterns, and make predictions or decisions. Through iterative learning, machines adapt and optimize their performance, enabling them to handle complex tasks efficiently.

Checkout our MLCertification Course for Beginners to fastrack your upskill journey!

What is Machine Learning? 
Source: Mapendo

Factors Affecting Learning Duration

The time it takes to master ML depends on several factors. The following significant factors influence how long learning takes:

  1. Prior Knowledge: Individuals with a strong background in mathematics, statistics, and programming may find it easier to grasp the fundamentals of machine learning. Having prior experience in these domains can accelerate the learning process.
  2. Learning Commitment: The amount of time and effort dedicated to studying machine learning directly impacts learning speed. Consistent study practices, genuine interest in the subject, and regular practice are crucial in expediting learning.
  3. Complexity of Machine Learning: It is a vast field encompassing various subfields, techniques, and strategies. The complexity of the chosen topic or field of knowledge can influence the time required to learn. Some concepts may be more challenging to understand than others.
  4. Learning Style: Everyone has a unique learning style. Some prefer hands-on projects and practical applications, while others favor theoretical understanding. The time taken to learn machine learning can vary based on individual preferences and learning methods.

Time Estimates for Different Learning Stages

A well-defined learning path is crucial for embarking on a successful journey in machine learning. For those interested in exploring the world of machine learning, here is a recommended learning path:

Fundamentals of Mathematics and Statistics

Estimated Time: 1-2 months

Description: It is now essential to comprehend fundamental mathematical ideas like calculus, probability theory, and linear algebra. To understand ML algorithms, one must first understand these key concepts.

Online Resources:

Programming Skills

Estimated Time: 2-3 months

Description: Learning programming languages like Python and relevant libraries such as NumPy and Pandas is crucial for implementing ML algorithms. The duration may vary based on prior programming experience and the time devoted to practice.

Online Resources:

Exploratory Data Analysis

Estimated Time: 1-2 weeks

Description: This phase uses exploratory data analysis approaches to comprehend the available data. The key to effective model construction is learning to extract insights, spot trends, and preprocess the data.

Online Resources:

Machine Learning Algorithms

Estimated Time: 2-3 months

Description: ML requires knowing different algorithms, including supervised and unsupervised learning methods. Effective model selection and optimization require understanding the underlying principles, advantages, and disadvantages of various algorithms.

Online Resources:

Model Evaluation and Validation

Estimated Time: 2-4 weeks

Description: The capacity to evaluate the efficacy of machine learning models and validate their accuracy is essential. Cross-validation, precision-recall curves, confusion matrices, and other learning techniques enable the evaluation and improvement of model performance.

Machine Learning Full Course | 2 -ML Projects Included | Beginner Level

Strategies to Optimize the Learning Process

To optimize the learning process and expedite the journey towards mastering ML, consider the following strategies:

  • Practice and Hands-on Projects: Regularly practice coding and implement ML algorithms on real-world datasets. Hands-on projects provide invaluable experience and reinforce theoretical knowledge.
  • Join Online Communities and Forums: Engage with fellow learners and professionals in ML communities and forums. Participating in discussions and seeking guidance can enhance learning and provide valuable insights.
  • Follow Blogs and Online Tutorials: Stay updated with the latest advancements and best practices in ML by following reputable blogs and online tutorials. These resources can supplement formal learning and introduce practical applications.
  • Participate in Hackathons: Being a part of ML hackathons will give you the confidence to work on real world problems. Participating in competitions exposes learners to diverse datasets and encourages collaboration with like-minded individuals.

How Can We Help You Learn ML?

Are you eager to dive into the Machine Learning universe but unsure where to begin or how long it might take to master this field? Our Blackbelt Course is the perfect solution to accelerate your ML learning journey.

  1. Comprehensive Learning Path: We offer a well-structured and comprehensive learning path that covers all essential ML concepts, algorithms, and techniques. From fundamentals to advanced topics, the course is designed to cater to learners of all levels, providing a seamless learning experience.
  2. Hands-on Projects: Practice is key to mastering ML. The Blackbelt Course provides hands-on projects and assignments that challenge your skills and reinforce your learning. Working on real-life datasets and problem statements helps you build a strong foundation in ML.
  3. Personalized Learning: Blackbelt Course offers customized learning paths, allowing you to focus on areas of interest and tailor your learning journey. It adapts to your progress, ensuring you grasp concepts at your own pace for a smooth learning experience.
  4. Constant Support: Receive continuous support from the Analytics Vidhya team throughout your learning journey. Whether you have doubts, technical issues, or need guidance, the support team is always ready to assist you, making learning hassle-free.
  5. Learning Certifications: Upon successfully completing the Blackbelt Course, earn certifications validating your ML expertise. These certifications can bolster your resume and open doors to exciting career opportunities in the ML domain.

Conclusion

In summary, machine learning takes time depending on various variables, including past knowledge, dedication to education, subject complexity, and individual learning preferences. Speed up the process by following a specified learning path, assigning time to each learning stage, and optimizing the learning process using online resources. If you still have any doubts, drop them in the comment section. We will get back to you soon!

Frequently Asked Questions

Q1. How much time does it take to learn AI and machine learning?

A. Depending on a number of factors, learning machine learning and artificial intelligence can take anywhere from a few months to a year or more.

Q2. What is the learning time for Python for machine learning?

A. Based on one’s prior programming knowledge and learning zeal, learning Python for ML can take two to three months.

Q3. Is 3 months enough to learn machine learning?

A. While learning the basics of machine learning in 3 months is possible, becoming proficient and mastering the field usually requires a more extended period of continuous learning and practice.

Q4. Can I master ML in 6 months?

A. Being a ML expert in six months is challenging but possible. It necessitates a systematic approach to learning, steadfast effort, and a focus on practical tasks emphasizing real-world application.

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Analytics Vidhya

Analytics Vidhya Content team

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