Machine Learning Roadmap 2024
A curated list of Machine learning videos, links, projects and datasets to help you conquer the ML landscape in 6 months
Levels of Learning
1. Testing the waters (Est. time 6-8 Weeks)
The goal of this level is to get you familiar with the ML universe. You will learn a bit of everything.
1. Learn Python (Est. time - 2 weeks)
3. Advance Topics
2. Learn Numpy (Est. time 3 Days)
1. Numpy Playlist - https://www.youtube.com/watch?v=CpPLLp3snK4&list=PLKnIA16_Rmvb-ToL3RQ_bwxG4_ND-0-DT
2. Numpy Practice Problems - https://github.com/rougier/numpy-100
3. Learn Pandas (Est. time 4 Days)
1. Pandas Playlist - https://www.youtube.com/watch?v=kq9Vmg5d7Sk&list=PLKnIA16_RmvbR85fgbfVRKOiMokUKVupy
2. Pandas Problems - https://github.com/ajcr/100-pandas-puzzles
4. Learn Data Visualization (Est. time 1 Week)
5. Descriptive Statistics (Est. time 4 Days)
1. Statistics Playlist - https://www.youtube.com/watch?v=tPhzDKjQBpo&list=PLKnIA16_RmvbVrE0eZO2bCaFln6jaNq-1
6. Learn Data Analysis Process (Est. time 1 week)
7. Learn Exploratory Data Analysis (EDA) (Est. time 1 Week)
1. Understanding your data - https://www.youtube.com/watch?v=mJlRTUuVr04
2. Univariate Analysis - https://www.youtube.com/watch?v=4HyTlbHUKSw
3. Bivariate and Multivariate Analysis - https://www.youtube.com/watch?v=6D3VtEfCw7w
4. Pandas Profiling - https://www.youtube.com/watch?v=E69Lg2ZgOxg
5. EDA on House Prices Dataset - https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python
6. EDA on Titanic Dataset - https://www.kaggle.com/startupsci/titanic-data-science-solutions
7. EDA on Haberman's Survival Dataset - https://www.kaggle.com/gokulkarthik/haberman-s-survival-exploratory-data-analysis
8. EDA on Heart Disease Dataset - https://www.kaggle.com/kralmachine/analyzing-the-heart-disease
9. EDA on IPL Dataset - https://www.kaggle.com/ash316/let-s-play-cricket
10. EDA on Wine Review Dataset - https://www.kaggle.com/kabure/wine-review-s-eda-recommend-systems
11. EDA on PIMA Diabetes Dataset - https://www.kaggle.com/shrutimechlearn/step-by-step-diabetes-classification-knn-detailed
12. EDA on Breast Cancer Dataset - https://www.kaggle.com/kanncaa1/statistical-learning-tutorial-for-beginners
13. EDA on Olympics Dataset - https://www.youtube.com/watch?v=5nQXhusiu7s
14. EDA on Covid Data - https://www.youtube.com/watch?v=ll0aZVNnOP8
15. WhatsApp Chat Analysis Project - https://www.youtube.com/watch?v=Q0QwvZKG_6Q
8. Learn Machine Learning Basics (Est. time 1 Week)
1. What is Machine Learning? https://www.youtube.com/watch?v=ZftI2fEz0Fw
2. AI vs ML vs DL https://www.youtube.com/watch?v=1v3_AQ26jZ0
3. Types of Machine Learning - https://www.youtube.com/watch?v=81ymPYEtFOw
4. Batch Machine Learning - https://www.youtube.com/watch?v=nPrhFxEuTYU
5. Online Machine Learning - https://www.youtube.com/watch?v=3oOipgCbLIk
6. Instance based vs Model based learning - https://www.youtube.com/watch?v=ntAOq1ioTKo
7. Challenges in Machine Learning - https://www.youtube.com/watch?v=WGUNAJki2S4
8. Applications of Machine Learning - https://www.youtube.com/watch?v=UZio8TcTMrI
9. Machine Learning Development Lifecycle - https://www.youtube.com/watch?v=iDbhQGz_rEo
10. Data Engineer V Data Analyst V Data Scientist V ML Engineer - https://www.youtube.com/watch?v=93rKZs0MkgU
11. How to frame a Machine Learning problem? - https://www.youtube.com/watch?v=A9SezQlvakw
12. Installing and using software for data science - https://www.youtube.com/watch?v=82P5N2m41jE
13. How to work with CSV files? - https://www.youtube.com/watch?v=a_XrmKlaGTs
14. Working with JSON and SQL data - https://www.youtube.com/watch?v=fFwRC-fapIU
15. Building an End to End Machine Learning Project - https://www.youtube.com/watch?v=dr7z7a_8lQw
2. Gaining Conceptual depth (Est. time 6-8 Weeks)
The goal of this level is to learn the core machine learning concepts and algorithms
1. Learn about tensors (Est. time - 1 Day)
1. What are Tensors? - https://www.youtube.com/watch?v=vVhD2EyS41Y
2. Advance Statistics
1. Covariance
2. Pearson Correlation Coefficient
3. QQ Plot
4. Confidence Interval
5. Hypothesis Testing
6. Chisquare Test, Anova Test
7. Playlist link - https://www.youtube.com/watch?v=qtaqvPAeEJY&list=PLKnIA16_Rmvbe9wDJGXc28KKr6lp5Jn2g
3. Probability Basics
1. Conditional Probability
2. Independent Events
3. Bayes Theorem
4. Uniform Distribution
5. Binomial Distribution
6. Bernaulli Distribution
7. Poission Distribution
8. Playlist Link - https://www.youtube.com/watch?v=Ty7knppVo9E&list=PLKnIA16_RmvYNbPMB6ofVLRCcTPUAftdY
5. Linear Algebra Basics
1. Representing Tabular Data
2. Vectors
3. Matrices
4. Matrix Multiplication
5. Dot Product
6. Equation of line in N-dim
7. Eigen Vector and Eigen Values
8. Playlist Link - https://www.youtube.com/watch?v=e9h-ZZ_ahRg&list=PLKnIA16_RmvYu0fS_RuIB2eTbJcTFdrAA
6. Basics of Calculus
1. Big Picture of Derivatives
2. Maxima and Minima
3. Playlist link - (first 4 videos only) https://www.youtube.com/playlist?list=PLBE9407EA64E2C318
7. Machine Learning Algorithms
1. Linear Regression - https://www.youtube.com/watch?v=UZPfbG0jNec&list=PLKnIA16_Rmva-wY_HBh1gTH32ocu2SoTr
2. Gradient Descent - https://www.youtube.com/watch?v=ORyfPJypKuU&list=PLKnIA16_RmvZvBbJex7T84XYRmor3IPK1
3. Logistic Regression - https://www.youtube.com/watch?v=XNXzVfItWGY&list=PLKnIA16_Rmvb-ZTsM1QS-tlwmlkeGSnru
4. Support Vector Machines - https://www.youtube.com/watch?v=ugTxMLjLS8M&list=PLKnIA16_RmvbOIFee-ra7U6jR2oIbCZBL
5. Naive Bayes - https://www.youtube.com/watch?v=Ty7knppVo9E&list=PLKnIA16_RmvZ67wQaHoBuzXaDAfPz-a6l
6. K Nearest Neighbors - https://www.youtube.com/watch?v=BYaoDZM1IcU&list=PLKnIA16_RmvZiE-lEdN5RDi18-u-T43zd
7. Decision Trees - https://www.youtube.com/watch?v=gwgmSSTdiXs&list=PLKnIA16_RmvYGY_n9PP8zN-0LG9MoZRjU
8. Random Forest - https://www.youtube.com/watch?v=bHK1fE_BUms&list=PLKnIA16_RmvZyqP3WGUo7iVziIIea_1bp
11. Gradient Boosting - https://www.youtube.com/watch?v=fbKz7N92mhQ&list=PLKnIA16_RmvaMPgWfHnN4MXl3qQ1597Jw
13. Principle Component Analysis (PCA) - https://www.youtube.com/watch?v=ToGuhynu-No&list=PLKnIA16_RmvYHW62E_lGQa0EFsph2NquD
14. KMeans Clustering - https://www.youtube.com/watch?v=5shTLzwAdEc&list=PLKnIA16_RmvbA_hYXlRgdCg9bn8ZQK2z9
15. Heirarchical Clustering - https://www.youtube.com/watch?v=Ka5i9TVUT-E
16. DBSCAN - https://www.youtube.com/watch?v=RDZUdRSDOok
17. T-sne - https://www.youtube.com/watch?v=NEaUSP4YerM and
8. Machine Learning Metrics
9. Bias Variance Tradeoff
10. Regularization
11. Cross-Validation
3. Learn Practical Concepts (Est. time 6-8 Weeks)
The goal of this level is to get you introduced to the practical side of machine learning. What you learn at this level would really help you out there in the wild.
1. Data Acquisition (Est. time - 2 Days)
1. Web Scraping - https://www.youtube.com/watch?v=8NOdgjC1988
Project - Create a Pandas dataframe of Indian cuisines from some website using web scraping.
2. Fetch data from API - https://www.youtube.com/watch?v=roTZJaxjnJc
Project - Create a Pandas dataframe of movies from TMDB API.
领英推荐
2. Working with missing values (Est. time - 3 Days)
1. Complete Case Analysis - https://www.youtube.com/watch?v=aUnNWZorGmk
2. Handling missing numerical data - https://www.youtube.com/watch?v=mCL2xLBDw8M
3. Handling missing categorical data - https://www.youtube.com/watch?v=l_Wip8bEDFQ
4. Missing indicator - https://www.youtube.com/watch?v=Ratcir3p03w
5. KNN Imputer - https://www.youtube.com/watch?v=-fK-xEev2I8
7. Kaggle Notebooks and Practice Datasets - https://docs.google.com/document/d/1_9Y6kxNc6QTym2Y2JGEBbnCUbE1qZWLVzVXlT2eX_FQ/edit?usp=sharing
3. Feature Scaling/Normalization (Est. time - 2 Days)
1. Standarization - https://www.youtube.com/watch?v=1Yw9sC0PNwY
2. Normalization - https://www.youtube.com/watch?v=eBrGyuA2MIg
4. Feature Encoding Techniques (Est. time - 2 Days)
1. Ordinal Enconding and Label Encoding - https://www.youtube.com/watch?v=w2GglmYHfmM
2. One Hot Encoding - https://www.youtube.com/watch?v=U5oCv3JKWKA
3. Encoding high cardinality categorical features - https://www.kaggle.com/general/16927
4. Feature hashing - https://datasciencestunt.com/dealing-with-categorical-features-with-high-cardinality-feature-hashing/
5. Feature Transformation(Est. time - 2 Days)
1. Log Transform - https://www.youtube.com/watch?v=cTjj3LE8E90
2. Box Cox Transform - https://www.youtube.com/watch?v=lV_Z4HbNAx0
3. Yeo Johnson Transform - https://www.youtube.com/watch?v=lV_Z4HbNAx0
4. Discretization - https://www.youtube.com/watch?v=kKWsJGKcMvo
6. Working with Pipelines(Est. time - 2 Days)
1. Column Transformer - https://www.youtube.com/watch?v=5TVj6iEBR4I
2. Sklearn Pipelines - https://www.youtube.com/watch?v=xOccYkgRV4Q
7. Handing Time and Date data(Est. time - 1 Day)
1. Working with time and date data - https://www.youtube.com/watch?v=J73mvgG9fFs
8. Working with Outliers (Est. time - 3 Days)
1. What are Outliers? - https://www.youtube.com/watch?v=Lln1PKgGr_M
2. Outlier detection and removal using Z-score method - https://www.youtube.com/watch?v=OnPE-Z8jtqM
3. Outlier detection and removal using IQR method - https://www.youtube.com/watch?v=Ccv1-W5ilak
4. Percentile method - https://www.youtube.com/watch?v=bcXA4CqRXvM
9. Feature Construction (Est. time - 1 Day)
1. Feature Construction - https://www.youtube.com/watch?v=ma-h30PoFms
10. Feature Selection (Est. time - 3 Days)
1. Feature selection using SelectKBest and Recursive Feature Elimination - https://www.youtube.com/watch?v=xlHk4okO8Ls&t=1s
2. Chi-squared Feature Selection - https://www.youtube.com/watch?v=fMIwIKLGke0
3. Backward Feature Elimination - https://www.youtube.com/watch?v=zW1SvA0Z-l4&t=2s
4. Dropping features using Pearson correlation coefficient - https://www.youtube.com/watch?v=FndwYNcVe0U
5. Feature Importance using Random Forest - https://www.youtube.com/watch?v=R47JAob1xBY
6. Feature Selection Advise - https://www.youtube.com/watch?v=YaKMeAlHgqQ
11. Cross Validation (Est. time - 2 Days)
1. What is cross-validation? - https://www.youtube.com/watch?v=fSytzGwwBVw
2. Holdout Method - https://www.youtube.com/watch?v=4NnI3SBuww4
3. K-Fold Cross Validation - https://www.youtube.com/watch?v=gJo0uNL-5Qw
4. Leave 1 Out Cross Validation - https://www.youtube.com/watch?v=yxqcHWQKkdA
5. Time series cross validation - https://www.youtube.com/watch?v=g9iO2AwTXyI
12. Modelling - Stacking and Blending (Est. time - 1 Week)
1. Stacking - https://www.youtube.com/watch?v=O-aDHBGMqXA
2. Blending - https://www.youtube.com/watch?v=TuIgtitqJho
3. LightGBM - https://www.youtube.com/watch?v=n_ZMQj09S6w
4. CatBoost - https://www.youtube.com/watch?v=8o0e-r0B5xQ
13. Model Tuning (Est. time - 4 Days)
1. GridSearchCV - https://www.youtube.com/watch?v=4Im0CT43QxY
2. RandomSearchCV - https://www.youtube.com/watch?v=Q5dH5mOQ_ik
3. Hyperparameter Tuning - https://www.youtube.com/watch?v=355u2bDqB7c
14. Working with imbalanced data (Est. time - 3 Days)
1. How to handle imbalanced data - https://www.youtube.com/watch?v=JnlM4yLFNuo
2. Kaggle Notebook - https://www.kaggle.com/kabure/credit-card-fraud-prediction-rf-smote
3. SMOTE on Quora Dataset - https://www.kaggle.com/theoviel/dealing-with-class-imbalance-with-smote
15. Handling Multicollinearity(Est. time - 2 Days)
1. What is multicollinearity? - https://www.youtube.com/watch?v=ekuD8JUdL6M
2. Practical Example - https://www.youtube.com/watch?v=ATH4urDitI8
3. VIF in Multicollinearity - https://www.youtube.com/watch?v=GMAp_tP1ZQ0
16. Data Leakage - (Est. time - 2 Days)
1. What is Data Leakage? - https://machinelearningmastery.com/data-leakage-machine-learning/
2. Practical - Data Leakage on Quora Question Pair Dataset - https://www.kaggle.com/sudalairajkumar/simple-leaky-exploration-notebook-quora
3. Practical - Data Leakage on Credit Card data - https://www.kaggle.com/dansbecker/data-leakage
17. Serving your model(Est. time - 1 Week)
1. Pickling your model - https://www.youtube.com/watch?v=yY1FXX_GSco
2. Flask Tutorial - https://www.youtube.com/watch?v=swHI1H7DVsQ
3. Streamlit Tutorial - https://www.youtube.com/watch?v=Klqn--Mu2pE
4. Deploy model on Heroku - https://www.youtube.com/watch?v=YncZ0WwxyzU
5. Deploy model on AWS - https://www.youtube.com/watch?v=_rwNTY5Mn40
6. Deploy model to GCP - https://www.youtube.com/watch?v=fw6NMQrYc6w
7. Deploy model to Azure - https://www.youtube.com/watch?v=qnbJcbjh-3s
8. ML model to Android App - https://www.youtube.com/watch?v=ax3WyB-_LJY
18. Working with Large Datasets
1. What is Out of core ML? - https://www.youtube.com/watch?v=9e4nUuq2Hmg
2. Practical implementation of Out of core ML - https://www.youtube.com/watch?v=sRCuvcdvuzk
3. NYC Cab Dataset Project - https://vaex.io/blog/ml-impossible-train-a-1-billion-sample-model-in-20-minutes-with-vaex-and-scikit-learn-on-your
4. Diving into different domains (Est. time 6-8 Weeks)
This is the level where you would dive into different domains of Machine Learning. Mastering these will make you a true Data Scientist.
1. SQL (Est. time - 2 Days)
1. Complete SQL Roadmap - https://www.youtube.com/watch?v=FGBme8dWR_M
2. SQL learning resources - https://docs.google.com/document/d/1wCALgWubTOvuvlXJ3Eweh7AgJj4sPq2pW92y3viPZbs/edit?usp=sharing
3. The only video you need to see - https://www.youtube.com/watch?v=nopIGY1zJE0
2. Recommendation Systems
1. Movie Recommendation System - https://www.youtube.com/watch?v=1xtrIEwY_zY
2. Book Recommender System - https://www.youtube.com/watch?v=sf93xpq8vaA
3. Fashion Recommender System - https://www.youtube.com/watch?v=xanJe6e8Xuw
3. Association Rule Learning
1. Association Rule Mining(Apriori Algorithm) - https://www.youtube.com/watch?v=guVvtZ7ZClw
2. Eclat Algorithm - https://www.youtube.com/watch?v=oBiq8cMkTCU
3. Market Basket Analysis - https://www.youtube.com/watch?v=Y7Xkqqfz1UU
4. Anamoly Detection
1. Anamoly Detection Lecture from Microsoft Research - https://www.youtube.com/watch?v=12Xq9OLdQwQ
2. Novelty Detection Lecture - https://www.youtube.com/watch?v=vIDcjbpwY3k
5. NLP
1. Complete NLP Roadmap - https://www.youtube.com/watch?v=PKv_okm1H-k
2. Complete NLP Playlist - https://www.youtube.com/watch?v=zlUpTlaxAKI&list=PLKnIA16_RmvZo7fp5kkIth6nRTeQQsjfX
3. NLP Project Ideas - https://www.youtube.com/watch?v=oWJe2T29kAo
4. Email Spam Classifier Project - https://www.youtube.com/watch?v=YncZ0WwxyzU
5. Building a Chatbot - https://www.youtube.com/watch?v=Nb21OhaW8GY
6. Fundamentals of Neural Network
5. Pushing it with Projects (Est. time 6-8 Weeks)
The objective of this level is to sharpen your knowledge that you have accumulated in the previous 4 levels
1. 8 types of Projects for your portfolio - https://www.youtube.com/watch?v=SQHfry4xmdM
2. How to select a project - https://www.youtube.com/watch?v=kH--k1VKFt4
3. Car Price Predictor - https://www.youtube.com/watch?v=iRCaMnR_bpA
4. Banglore House Price Predictor - https://www.youtube.com/watch?v=DVxkI1VmpCk
5. Posture Detection using ML5.js - https://www.youtube.com/watch?v=kRvIcdLhDtU
6. Laptop Price Predictor - https://www.youtube.com/watch?v=BgpM2IiCH6k
7. Which bollywood celebrity are you? - https://www.youtube.com/watch?v=X67rclJcIL0
8. Finding similar GOT characters - https://www.youtube.com/watch?v=ygGknomFEWY
9. IPL win probability predictor - https://www.youtube.com/watch?v=ygGknomFEWY
10. T20 score predictor - https://www.youtube.com/watch?v=ygGknomFEWY
11. Titanic Survivor Prediction - https://www.youtube.com/watch?v=Bnp94fpxZjY
12. Diabetes Prediction using ML - https://www.youtube.com/watch?v=xUE7SjVx9bQ
13. Fake news prediction - https://www.youtube.com/watch?v=nacLBdyG6jE
14. Loan Status Prediction - https://www.youtube.com/watch?v=XckM1pFgZmg
15. Gold Price Prediction - https://www.youtube.com/watch?v=9ffkBvh8PTQ
16.Handwriting Classifier - https://www.youtube.com/watch?v=1B3YIkyPNk0
17. Flight Fare Prediction - https://www.youtube.com/watch?v=y4EMEpEnElQ
18. Link for 500+ ML+DL projects - https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
Electronics Engineering Student | Software Development | Embedded Systems | IoT | Seeking Opportunities to Learn & Gain Real-World Experience
2 周Thank you for providing this wonderful roadmap. so we need to follow the roadmap as shown above?
UiUx Designer | Html | Css | Angularjs |
4 个月Thank you so much for providing this road map Can you please provide a pdf for me