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Machine Learning

via YouTube


This course aims to provide a complete roadmap to becoming an expert in Python for Machine Learning. The learning outcomes include mastering Python basics, data manipulation with Pandas, data visualization with Matplotlib and Seaborn, exploratory data analysis, advanced Python concepts, statistical analysis, machine learning algorithms like linear regression, logistic regression, decision trees, ensemble methods, clustering techniques, dimensionality reduction, and classification metrics. The course teaches various Python libraries and tools essential for data science. The teaching method includes tutorials, practical implementations, in-depth mathematical explanations, and real-world applications. The intended audience for this course is beginners and intermediate learners interested in Python programming for data science and machine learning.


Complete Road Map To Be Expert In Python- Follow My Way.
Complete Roadmap To Follow To Prepare Machine Learning With All Videos And Materials.
Tutorial 1- Anaconda Installation and Python Basics.
Why Python is the Best Programming Language For Machine Learning?.
Tutorial 2 - Python List and Boolean Variables.
Tutorial 3- Python Sets, Dictionaries and Tuples.
Tutorial 4 - Numpy and Inbuilt Functions Tutorial.
Tutorial 5- Pandas, Data Frame and Data Series Part-1.
Tutorial 6- Pandas,Reading CSV files With Various Parameters- Part 2.
Tutorial 7- Pandas-Reading JSON,Reading HTML, Read PICKLE, Read EXCEL Files- Part 3.
Tutorial 8- Matplotlib (Simple Visualization Library).
Tutorial 9- Seaborn Tutorial- Distplot, Joinplot, Pairplot Part 1.
Tutorial 10- Seaborn- Countplot(), Violinplot(), Boxplot()- Part2.
How To Become Expertise in Exploratory Data Analysis.
Tutorial 11-Exploratory Data Analysis(EDA) of Titanic dataset.
Tutorial 12- Python Functions, Positional and Keywords Arguments.
Tutorial 15- Map Functions using Python.
Tutorial 13- Python Lambda Functions.
Tutorial 16- Filter Functions In Python.
Tutorial 17- Python List Comprehension.
Tutorial 18- Python Advanced String Formatting.
Tutorial 19- Python Iterables vs Iterators.
Tutorial 20- How To Import All Important Python Data Science Libraries Using Pyforest.
Tutorial 21- Python OOPS Tutorial- Classes, Variables, Methods and Objects.
Advanced Python- Exception Handling Detailed Explanation In Python.
Advanced Python Series- Custom Exception Handling In Python.
Advance Python Series- Public Private And Protected Access Modifiers.
Advance Python Series- Inheritance In Python.
Tutorial 22-Univariate, Bivariate and Multivariate Analysis- Part1 (EDA)-Data Science.
Tutorial 23-Univariate, Bivariate and Multivariate Analysis- Part2 (EDA)-Data Science.
Tutorial 24- Histogram in EDA- Data Science.
Tutorial 24-Z Score Statistics Data Science.
Tutorial 25- Probability Density function and CDF- EDA-Data Science.
Tutorial 26- Linear Regression Indepth Maths Intuition- Data Science.
Tutorial 27- Ridge and Lasso Regression Indepth Intuition- Data Science.
Tutorial 28- Ridge and Lasso Regression using Python and Sklearn.
Multiple Linear Regression using python and sklearn.
Tutorial 28-MultiCollinearity In Linear Regression- Part 2.
Machine Learning-Bias And Variance In Depth Intuition| Overfitting Underfitting.
Tutorial 29-R square and Adjusted R square Clearly Explained| Machine Learning.
Tutorial 31- Hypothesis Test, Type 1 Error, Type 2 Error.
What Is P Value In Statistics In Simple Language?.
Tutorial 32- All About P Value,T test,Chi Square Test, Anova Test and When to Use What?.
Tutorial 33- P Value,T test, Correlation Implementation with Python- Hypothesis Testing.
Tutorial 33- Chi Square Test Implementation with Python- Hypothesis Testing- Part 2.
Tutorial 34- Performance Metrics For Classification Problem In Machine Learning- Part1.
Tutorial 35- Logistic Regression Indepth Intuition- Part 1| Data Science.
Tutorial 36- Logistic Regression Indepth Intuition- Part 2| Data Science.
Tutorial 36- Logistic Regression Mutliclass Classification(OneVsRest)- Part 3| Data Science.
Tutorial 37: Entropy In Decision Tree Intuition.
Tutorial 38- Decision Tree Information Gain.
Tutorial 39- Gini Impurity Intuition In Depth In Decision Tree.
Tutorial 40- Decision Tree Split For Numerical Feature.
Advance House Price Prediction- Exploratory Data Analysis- Part 1.
Advance House Price Prediction- Exploratory Data Analysis- Part 2.
Advance House Price Prediction-Feature Engineering Part 1.
Advance House Price Prediction-Feature Engineering Part 2.
Advance House Price Prediction-Feature Selection.
Tutorial 41-Performance Metrics(ROC,AUC Curve) For Classification Problem In Machine Learning Part 2.
Performance Metrics On MultiClass Classification Problems.
K Nearest Neighbor classification with Intuition and practical solution.
K Nearest Neighbour Easily Explained with Implementation.
Tutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)?.
Tutorial 43-Random Forest Classifier and Regressor.
Tutorial 45-Handling imbalanced Dataset using python- Part 1.
Tutorial 46-Handling imbalanced Dataset using python- Part 2.
Hyperparameter Optimization for Xgboost.
Visibility Climate Prediction- You Can Add This In Your Resume.
Euclidean Distance and Manhattan Distance.
K Means Clustering Intuition.
Hierarchical Clustering intuition.
DBSCAN Clustering Easily Explained with Implementation.
Silhouette (clustering)- Validating Clustering Models- Unsupervised Machine Learning.
Curse of Dimensionality Easily explained| Machine Learning.
Dimensional Reduction| Principal Component Analysis.
Principle Component Analysis (PCA) using sklearn and python.
What is Cross Validation and its types?.
Tutorial 42-How To Find Optimal Threshold For Binary Classification - Data Science.
Tutorial 47- Bayes' Theorem| Conditional Probability- Machine Learning.
Tutorial 48- Naive Bayes' Classifier Indepth Intuition- Machine Learning.
Tutorial 49- How To Apply Naive Bayes' Classifier On Text Data (NLP)- Machine Learning.
Support Vector Machine (SVM) Basic Intuition- Part 1| Machine Learning.
Maths Intuition Behind Support Vector Machine Part 2 | Machine Learning Data Science.
SVM Kernels In-depth Intuition- Polynomial Kernels Part 3 | Machine Learning Data Science.
Gradient Boosting In Depth Intuition- Part 1 Machine Learning.
Gradient Boosting Complete Maths Indepth Intuiton Explained| Machine Learning- Part2.
Xgboost Classification Indepth Maths Intuition- Machine Learning Algorithms.
Xgboost Regression In-Depth Intuition Explained- Machine Learning Algorithms .
Data Science In Medical-Live Tracking Of CO--VID Cases In India using Python.
Perform EDA In Seconds With Visualization Using SweetViz Library.
4 End To End Projects Till Deployment For Beginners In Data Science| All You Have To Do Is Learn.
Deploy Machine Learning Models Using StreamLit Library- Data Science.
Perform Exploratory Data Analysis In Minutes- Data Science| Machine Learning.
Pandas Visual Analysis- Perform Exploratory Data Analysis In A Single Line Of Code.
How To Read And Process Huge Datasets in Seconds Using Vaex Library| Data Science| Machine Learning.
D-Tale The Best Library To Perform Exploratory Data Analysis Using Single Line Of Code.
Interview Prep Day3-How To Prepare Support Vector Machines Important Questions In Interviews.
Google Datasets Search Engine- Search All Datasets From One Place For Data Science,Machine Learning.
How To Run Flask In Google Colab.
Time Series Forecasting Using Facebook FbProphet.
Performance Metrics Interview Questions- Data Science.
How To Perform Post Pruning In Decision Tree? Prevent Overfitting- Data Science.
How To Train Machine Learning Model Using CPU Multi Cores.
Step By Step Process To Learn Machine Learning Algorithm Efficiently.
Data Science Is Just Not About Model Building.
How To Interpret The ML Model? Is Your Model Black Box? Lime Library.
6 Healthcare End To End Machine Learning Projects- Credits Devansh and Bedanta.
Overfitting, Underfitting And Data Leakage Explanation With Simple Example.
What Is API? Application Programming Interface And Why It Is Important-Data Science.
500+ Machine Learning And Deep Learning Projects All At One Place.
Google Colab Pro Vs Colab Free- Benefits Of Using Colab Pro- How To Access From India.
Advance Python Series-Magic Methods In Classes.
Advanced Python Series- Assert Statement In Python.
How To Speed Up Pandas By 4X Times- Modin Pandas Library.
TextBlob Library In Python For Natural Language Processing.
3000+ Research Datasets For Machine Learning Researchers By Papers With Code.
Introduction To MLflow-An Open Source Platform for the Machine Learning Lifecycle.
Amazing Data Science End To End Project From Starters In ML and Deep Learning- Agriculture Domain.
SVM Kernal- Polynomial And RBF Implementation Using Sklearn- Machine Learning.
Lux - Python Library for Intelligent Visual Discovery.
Texthero-Text Preprocessing, Representation And Visualization From Zero to Hero..
Colab Pro Now Available In India, Brazil, France, Thailand,Japan,UK- BOON FOR Data Science Aspirants.
Rainfall Prediction- Converting A Kaggle Project to End To End Machine Learning Project.
PyWebIO- Creating WebAPP Using Python Without Using HTML And JS.
Creating BMI Calculator Web APP Using Python And PyWebIO.
Deployment Of ML Models Using PyWebIO And Flask.
Shapash- Python Library To Make Machine Learning Interpretable.
Difference Between fit(), transform(), fit_transform() and predict() methods in Scikit-Learn.
EvalML AutoML Library To Automate Feature Engineering, Feature Selection,Model Creation And Tuning.
Lazy Predict Python- Understanding Which Models Works Well Without Any Tuning.
How To Automate NLP Tasks Using EvalML Library.
Gradio Library-Interfaces for your Machine Learning Models.
Comparing Transfer Learning Models Using Gradio.
Introduction To Machine Learning And Deep Learning For Starters.
Numba Library- Let's Make Python Faster.
Deployment Of ML Models Using PyWebIO And Flask In Heroku.
All Automated EDA Libraries All At One Place.
Discussing All The Types Of Feature Transformation In Machine Learning.
Automating Web Scrapping Using AutoScraper Library.
Automating WebScraping Amazon Ecommerce Website Using AutoScrapper.
AutoScraper and Flask: Create an API From Amazon Website in Less Than 10 Minutes.
Autoviz-Automatically Visualize Any Dataset With Single Line Of Code.
AutoScraper- Scrap Images From Amazon Ecommerce- End To End Web Scraping Application.
All Type Of Cross Validation With Python All In 1 Video.
DataPrep Library- Perform Faster EDA Within No Time.

Taught by

Krish Naik


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