Best Machine Learning Roadmap With Resources

Levels Of Learning

• Test Your Water Level
• Jump Into Conceptual Depths
• Learn Practical Concepts
• Pushing Yourself With Project

Test Your Water Level (Estimated Time: 6–8 weeks)

Learn Python
→ OOPS in Python
→ File Handling
→ Exception Handling
→ Regular Expression
→ Functional Programming
→ Basics Of Flask And Django
→ Practice Problems

Learn Numpy
→ Numpy Playlist
→ Practice Problems

Learn Pandas
→ Pandas Playlist
→ Practice Problems

Learn Data Visualization
→ Matplotlib
→ Seaborn

Learn Descriptive Statistics
→ Statistics

Learn Data Analysis Process
→ Data Analysis

Learn EDA (Exploratory Data Analysis)
→ Univariate Analysis
→ Multivariate Analysis
→ Pandas Profiling
→ EDA on House Price Dataset : Click Here
→ EDA on Titanic Dataset: Click Here
→ EDA on Heart Disease Dataset: Click Here
→ EDA on Olympics Dataset: Click Here
→ EDA on PIMA Diabetes Dataset : Click Here
→ EDA on Haberman’s Survival Dataset: Click Here
→ EDA on Breast Cancer Dataset: Click Here
→ EDA on IPL Dataset : Click Here

Learn Machine Learning Basics
→ What is Machine Learning?
→ ML vs DL vs AI
→ Types Of Machine Learning
→ Applications Of Machine Learning
→ Jobs In Datascience
→ How to work with CSV, JSON and SQL Data ?
→ Tools Used In ML

Jump Into Conceptual Depths (Estimated Time: 9–18 weeks)

Learn About Tensors
→ What are Tensors?

Advanced Statistics
→ Covariance
→ Pearson Correlation Coefficient
→ QQ Plot
→ Confidence Interval
→ Hypothesis Testing
→ Chisquare Test
→ Anova Test
→ Playlist

Probability Basics
→ Condition Probability
→ Independent Events
→ Bayes Theorem
→ Uniform Distribution
→ Binomial Distribution
→ Bernaulli Distribution
→ Poission Distribution
→ Playlist

Linear Algebra Basics
→ Representing Tabular Data
→ Vectors
→ Matrices
→ Matrix Multiplication
→ Dot Product
→ Equation of line in N-dm
→ Eigen Vector and Eigen Values
→ Playlist

Basics Of Calculus
→ Big Picture of Derivatives
→ Maxima and Minima
→ Playlist

Machine Learning Algorithms
→ Linear Regression
→ Gradient Descent
→ Logistic Regression
→ Support Vector Machines
→ Naive Bayes
→ K Nearest Neighbors
→ Decision Trees
→ Random Forest
→ Bagging
→ Adaboost
→ Gradient Boosting
→ Xgboost
→ PCA (Principle Component Analysis)
→ KMeans Clustering
→ Heirarchical Clustering
→ DBSCAN
→ T-sne

Machine Learning Metrics

Bias Variance Tradeoff

Regularization

Cross-Validation

Learn Practical Concepts (Estimated Time: 18–26 weeks)

Data Acquisition
→ Web Scraping
→ Fetch Data from API

Working With Missing Values
→ Handling Missing Numerical Data
→ Handling Missing Categorical Data
→ Missing Indicator
→ KNN Imputer
→ MICE
→ Kaggle Notebooks and Practice Datasets : Click Here

Feature Scaling / Normalization
→ Standardization
→ Normalization

Feature Encoding Techniques
→ Ordinal Encoding
→ Label Encoding
→ OHC
→ Feature Hashing

Feature Transformation
→ Log Transform
→ Box Cox Transform
→ Yeo Johnson Transform
→ Discretization

Working With Pipelines
→ Column Transformer
→ Sklearn Pipelines

Handling Date and Time Data
→ Working with time and date data

Working With Outliers
→ What are Outliers ?
→ Outlier detection
→ Outlier Removal using Z-score method
→ Removal using IQR method
→ Percentile method

Feature Construction
→ Feature Construction

Feature Selection
→ Feature Selection using SelectKBest and Recursive Feature Elimination
→ Chi-squared Feature Selection
→ Backward Feature Elimination
→ Dropping features using Pearson correlation coefficient
→ Feature importance using Random Forest
→ Feature Selection Advise

Cross-Validation
→ What is cross-validation ?
→ Holdout Method
→ K-Fold cross-validation
→ Leave one out cross-validation
→ Time Series cross-validation

Modelling-Stacking And Blending
→ Stacking
→ Blending
→ LightGBM
→ CatBoost

Model Tuning
→ GridSearchCV
→ RandomSearchCV
→ Hyperparameter tuning

Working with imbalanced data
→ Kaggle Notebook : Click Here
→ SMOTE on quora dataset : Click Here

Handling Multicollinearity
→ What is Multicollinearity ?
→ Practicle Example
→ VIF in Multicollinearity

Data Leakage
→ What is Data Leakage ?
→ Practical :> Data Leakage on Quora Question Pair Dataset : Click Here
→ Practical :> Data Leakage on Credit Card Data : Click Here

Serving Your Model
→ Deploy Model On Heroku
→ Deploy Model On AWS
→ Deploy Model On GCP
→ Deploy Model On Azure

Pushing Yourself With Project

500 (AI ,Machine Learning ,Deep Learning,Computer Vision,NLP):
→ Click Here

Note : Still working on resources part because as time changes technology changes.