AI Rank
中文
← Open Source
campusx-official

ML-Roadmap-for-2022

A curated list of Machine learning videos, links, projects and datasets to help you conquer the ML landscape in 6 months.

TutorialsMLE roadmap
Open on GitHub
Momentum
+0stars in 24 hours0.0%
1.24k
Stars
579
Forks
+3
This week
2
Contributors
Created 2022-01-10 · Updated 2026-10-13 · #11030 today
Top developers
campusx-officialnitishksingh24

campusx-official/ML-Roadmap-for-2022 is ranked 11,030 with 1,235 stars.

Same-category comparison

campusx-official/ML-Roadmap-for-2022 is ranked 11,030 among these 8 peers; dsgiitr/graph_nets is ranked 11,025.

Same-category comparison
NameRankMetrics
wangyuGithub01/Machine_Learning_Resources11,001
Stars:
1,242
Forks:
179
Hyperparticle/one-pixel-attack-keras11,008
Language:
Jupyter Notebook
Stars:
1,240
Forks:
214
Description:
Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet.
mlbvn/ml-yearning-vi11,012
Language:
Python
Stars:
1,239
Forks:
396
Description:
Một cuốn sách tập trung vào hướng dẫn cách cấu trúc các dự án Học Máy và phân tích cách làm cho các thuật toán Học Máy hoạt động.
dsgiitr/graph_nets11,025
Language:
Jupyter Notebook
Stars:
1,236
Forks:
226
Description:
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
MorvanZhou/Evolutionary-Algorithm11,035
Language:
Python
Stars:
1,234
Forks:
618
the-full-stack/fsdl-text-recognizer-project11,057
Language:
Jupyter Notebook
Stars:
1,229
Forks:
421
Description:
Lab materials for the Full Stack Deep Learning Course.
hanbt/learn_dl11,075
Language:
TeX
Stars:
1,225
Forks:
971
Description:
Deep learning algorithms source code for beginners.
ml4a/ml4a-ofx11,080
Language:
C++
Stars:
1,224
Forks:
272
Description:
A collection of openFrameworks apps for working with machine learning.
Key parameters and specifications
Key parameters and specifications
ParameterValue
CategoryTutorials
SubcategoryMLE roadmap
Stars1,235
Forks579
Contributors2
Created date2022-01-10
Updated date2026-10-13
Stars in 1 day0
Stars in 7 days3
Rank11,030
Previous rank10,987
Rank change-43
7-day stars rank7,006
Previous 7-day stars rank6,205
7-day stars rank change-801
Total stars rank9,472
Previous total stars rank9,466
Total stars rank change-6
CountryIndia
README summary

ML-Roadmap-for-2022 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. time 6-8 Weeks) The goal of this level is to get you familiar with the ML universe. #### Learn Exploratory Data Analysis (EDA) (Est. #### Learn Machine Learning Basics (Est. time 6-8 Weeks) The goal of this level is to learn the core machine learning concepts and algorithms 1. 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. #### Feature Scaling/Normalization (Est. #### Modelling - Stacking and Blending (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. #### Fundamentals of Neural Network - 5. 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 - 2.

Frequently asked questions

How many stars and forks are recorded?

campusx-official/ML-Roadmap-for-2022 has 1,235 stars and 579 forks.

What is the repository rank?

campusx-official/ML-Roadmap-for-2022 is ranked 11,030.

Which repository is closest in rank?

campusx-official/ML-Roadmap-for-2022 is compared with dsgiitr/graph_nets, ranked 11,025 with 1,236 stars.

README

ML-Roadmap-for-2022

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

  2. Gaining Conceptual depth

  3. Learning Practical Concepts

  4. Diving into different domains

  5. Pushing it with Projects

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)

     1. Basics of Python - https://www.youtube.com/playlist?list=PLKnIA16_Rmvb1RYR-iTA_hzckhdONtSW4
 2. OOP in Python
    - Lecture 1 - https://www.youtube.com/watch?v=1s869EfxoDo
    - Lecture 2 - https://www.youtube.com/watch?v=8To-A6VPL90
 3. Advance Topics
    - File Handling - https://www.youtube.com/watch?v=ixEeeNjjOJ0
    - Exception Handling - https://www.youtube.com/watch?v=NIWwJbo-9_8
    - Regular Expressions - https://www.youtube.com/watch?v=K8L6KVGG-7o
    - Functional Programming - https://www.youtube.com/watch?v=SvK_GErE2nM
    - Basics of Flask - https://www.youtube.com/watch?v=swHI1H7DVsQ
 4. Practice Problems - https://docs.google.com/document/d/1E_xCNijOWZ4Bm7r7DVj-1OA-oUopEFmv4tRm0YNuFWQ/edit?usp=sharing

  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)

     1. Matplotlib - https://www.youtube.com/playlist?list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_
 2. Seaborn - https://www.youtube.com/playlist?list=PLKnIA16_RmvbB1bFGjvS6a8T0mnqawejo

  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)

     1. Playlist - https://www.youtube.com/watch?v=ZhacwtUR0SU&list=PLKnIA16_RmvZAqJzKstVHywcRNMn6pcGD

  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

  4. 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

  5. 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

  6. 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
 9. Bagging - https://www.youtube.com/watch?v=LUiBOAy7x6Y&list=PLKnIA16_RmvZ7iKIcJrLjUoFDEeSejRpn
 10. Adaboost - https://www.youtube.com/watch?v=sFKnP0iP0K0&list=PLKnIA16_RmvZxriy68dPZhorB8LXP1PY6
 11. Gradient Boosting - https://www.youtube.com/watch?v=fbKz7N92mhQ&list=PLKnIA16_RmvaMPgWfHnN4MXl3qQ1597Jw
 12. Xgboost - https://www.youtube.com/watch?v=BTLB-ppqBZc&list=PLKnIA16_RmvbXJbBW4zCy4Xbr81GRyaC4
 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 https://distill.pub/2016/misread-tsne/

  7. Machine Learning Metrics - https://www.youtube.com/watch?v=Ti7c-Hz7GSM&list=PLKnIA16_RmvZJGOqRjqhOhTEmQW3vDdbQ

  8. Bias Variance Tradeoff - https://www.youtube.com/watch?v=74DU02Fyrhk

  9. Regularization - https://www.youtube.com/watch?v=aEow1QoTLo0&list=PLKnIA16_RmvZuSEZ24Wlm13QpsfLlJBM4

  10. Cross-Validation - https://www.youtube.com/watch?v=S5NkE-xgx98

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.

  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
 6. MICE - https://www.youtube.com/watch?v=a38ehxv3kyk
 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. Time Series(Coming Soon)

  7. Computer Vision(Coming Soon)

  8. Fundamentals of Neural Network - https://www.youtube.com/playlist?list=PLKnIA16_RmvYuZauWaPlRTC54KxSNLtNn

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
Nearby in the ranking
Similar items