Complete Machine Learning playlist
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What you'll learn
This course includes
- 36.5 hours of video
- Certificate of completion
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Course content
1 modules • 153 lessons • 36.5 hours of video
Complete Machine Learning playlist
153 lessons
• 36.5 hours
Complete Machine Learning playlist
153 lessons
• 36.5 hours
- Complete Road Map To Be Expert In Python- Follow My Way29:11
- Complete Roadmap To Follow To Prepare Machine Learning With All Videos And Materials18:56
- Tutorial 1- Anaconda Installation and Python Basics19:05
- Why Python is the Best Programming Language For Machine Learning?05:15
- Tutorial 2 - Python List and Boolean Variables21:49
- Tutorial 3- Python Sets, Dictionaries and Tuples16:05
- Tutorial 4 - Numpy and Inbuilt Functions Tutorial26:42
- Tutorial 5- Pandas, Data Frame and Data Series Part-116:49
- Tutorial 6- Pandas,Reading CSV files With Various Parameters- Part 229:32
- Tutorial 7- Pandas-Reading JSON,Reading HTML, Read PICKLE, Read EXCEL Files- Part 319:31
- Tutorial 8- Matplotlib (Simple Visualization Library)25:55
- Tutorial 9- Seaborn Tutorial- Distplot, Joinplot, Pairplot Part 121:43
- Tutorial 10- Seaborn- Countplot(), Violinplot(), Boxplot()- Part210:54
- How To Become Expertise in Exploratory Data Analysis10:05
- Tutorial 11-Exploratory Data Analysis(EDA) of Titanic dataset31:45
- Tutorial 12- Python Functions, Positional and Keywords Arguments13:39
- Tutorial 15- Map Functions using Python04:46
- Tutorial 13- Python Lambda Functions06:03
- Tutorial 16- Filter Functions In Python03:58
- Tutorial 17- Python List Comprehension08:07
- Tutorial 18- Python Advanced String Formatting10:25
- Tutorial 19- Python Iterables vs Iterators12:02
- Tutorial 20- How To Import All Important Python Data Science Libraries Using Pyforest05:01
- Tutorial 21- Python OOPS Tutorial- Classes, Variables, Methods and Objects14:28
- Advanced Python- Exception Handling Detailed Explanation In Python20:29
- Advanced Python Series- Custom Exception Handling In Python08:58
- Advance Python Series- Public Private And Protected Access Modifiers14:56
- Advance Python Series- Inheritance In Python11:39
- Tutorial 22-Univariate, Bivariate and Multivariate Analysis- Part1 (EDA)-Data Science13:11
- Tutorial 23-Univariate, Bivariate and Multivariate Analysis- Part2 (EDA)-Data Science15:53
- Tutorial 24- Histogram in EDA- Data Science04:42
- Tutorial 24-Z Score Statistics Data Science11:59
- Tutorial 25- Probability Density function and CDF- EDA-Data Science07:52
- Tutorial 26- Linear Regression Indepth Maths Intuition- Data Science24:15
- Tutorial 27- Ridge and Lasso Regression Indepth Intuition- Data Science20:17
- Tutorial 28- Ridge and Lasso Regression using Python and Sklearn09:51
- Multiple Linear Regression using python and sklearn19:51
- Tutorial 28-MultiCollinearity In Linear Regression- Part 216:00
- Machine Learning-Bias And Variance In Depth Intuition| Overfitting Underfitting16:53
- Tutorial 29-R square and Adjusted R square Clearly Explained| Machine Learning17:16
- Tutorial 31- Hypothesis Test, Type 1 Error, Type 2 Error11:45
- What Is P Value In Statistics In Simple Language?11:18
- Tutorial 32- All About P Value,T test,Chi Square Test, Anova Test and When to Use What?12:01
- Tutorial 33- P Value,T test, Correlation Implementation with Python- Hypothesis Testing20:02
- Tutorial 33- Chi Square Test Implementation with Python- Hypothesis Testing- Part 214:09
- Tutorial 34- Performance Metrics For Classification Problem In Machine Learning- Part124:12
- Tutorial 35- Logistic Regression Indepth Intuition- Part 1| Data Science12:40
- Tutorial 36- Logistic Regression Indepth Intuition- Part 2| Data Science28:17
- Tutorial 36- Logistic Regression Mutliclass Classification(OneVsRest)- Part 3| Data Science06:39
- Tutorial 37: Entropy In Decision Tree Intuition08:58
- Tutorial 38- Decision Tree Information Gain12:40
- Tutorial 39- Gini Impurity Intuition In Depth In Decision Tree11:13
- Tutorial 40- Decision Tree Split For Numerical Feature06:11
- Advance House Price Prediction- Exploratory Data Analysis- Part 123:29
- Advance House Price Prediction- Exploratory Data Analysis- Part 219:48
- Advance House Price Prediction-Feature Engineering Part 114:12
- Advance House Price Prediction-Feature Engineering Part 213:54
- Advance House Price Prediction-Feature Selection08:03
- Tutorial 41-Performance Metrics(ROC,AUC Curve) For Classification Problem In Machine Learning Part 209:49
- Performance Metrics On MultiClass Classification Problems06:02
- K Nearest Neighbor classification with Intuition and practical solution20:06
- K Nearest Neighbour Easily Explained with Implementation18:02
- Tutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)?06:27
- Tutorial 43-Random Forest Classifier and Regressor10:18
- Tutorial 45-Handling imbalanced Dataset using python- Part 113:01
- Tutorial 46-Handling imbalanced Dataset using python- Part 210:59
- Hyperparameter Optimization for Xgboost14:55
- What is AdaBoost (BOOSTING TECHNIQUES)14:06
- Visibility Climate Prediction- You Can Add This In Your Resume17:23
- Euclidean Distance and Manhattan Distance08:39
- K Means Clustering Intuition14:36
- Hierarchical Clustering intuition06:12
- DBSCAN Clustering Easily Explained with Implementation18:32
- Silhouette (clustering)- Validating Clustering Models- Unsupervised Machine Learning20:07
- Curse of Dimensionality Easily explained| Machine Learning07:37
- Dimensional Reduction| Principal Component Analysis19:06
- Principle Component Analysis (PCA) using sklearn and python12:30
- What is Cross Validation and its types?18:15
- Tutorial 42-How To Find Optimal Threshold For Binary Classification - Data Science15:10
- Tutorial 47- Bayes' Theorem| Conditional Probability- Machine Learning11:02
- Tutorial 48- Naive Bayes' Classifier Indepth Intuition- Machine Learning15:55
- Tutorial 49- How To Apply Naive Bayes' Classifier On Text Data (NLP)- Machine Learning13:10
- Support Vector Machine (SVM) Basic Intuition- Part 1| Machine Learning12:50
- Maths Intuition Behind Support Vector Machine Part 2 | Machine Learning Data Science23:27
- SVM Kernels In-depth Intuition- Polynomial Kernels Part 3 | Machine Learning Data Science20:41
- SVM Kernal- Polynomial And RBF Implementation Using Sklearn- Machine Learning14:40
- Gradient Boosting In Depth Intuition- Part 1 Machine Learning11:20
- Gradient Boosting Complete Maths Indepth Intuiton Explained| Machine Learning- Part217:47
- Xgboost Classification Indepth Maths Intuition- Machine Learning Algorithms🔥🔥🔥🔥23:59
- Xgboost Regression In-Depth Intuition Explained- Machine Learning Algorithms 🔥🔥🔥🔥19:30
- Data Science In Medical-Live Tracking Of CO--VID Cases In India using Python15:39
- Perform EDA In Seconds With Visualization Using SweetViz Library17:20
- 4 End To End Projects Till Deployment For Beginners In Data Science| All You Have To Do Is Learn10:49
- Deploy Machine Learning Models Using StreamLit Library- Data Science12:56
- Perform Exploratory Data Analysis In Minutes- Data Science| Machine Learning18:34
- Pandas Visual Analysis- Perform Exploratory Data Analysis In A Single Line Of Code🔥🔥🔥🔥13:12
- How To Read And Process Huge Datasets in Seconds Using Vaex Library| Data Science| Machine Learning19:31
- D-Tale The Best Library To Perform Exploratory Data Analysis Using Single Line Of Code🔥🔥🔥🔥12:54
- Interview Prep Day3-How To Prepare Support Vector Machines Important Questions In Interviews🔥🔥13:45
- Google Datasets Search Engine- Search All Datasets From One Place For Data Science,Machine Learning11:51
- How To Run Flask In Google Colab07:39
- Time Series Forecasting Using Facebook FbProphet16:57
- Performance Metrics Interview Questions- Data Science04:34
- How To Perform Post Pruning In Decision Tree? Prevent Overfitting- Data Science16:24
- How To Train Machine Learning Model Using CPU Multi Cores14:41
- Step By Step Process To Learn Machine Learning Algorithm Efficiently14:08
- Data Science Is Just Not About Model Building07:59
- How To Interpret The ML Model? Is Your Model Black Box? Lime Library11:27
- 6 Healthcare End To End Machine Learning Projects- Credits Devansh and Bedanta07:48
- Overfitting, Underfitting And Data Leakage Explanation With Simple Example14:12
- What Is API? Application Programming Interface And Why It Is Important-Data Science08:25
- 500+ Machine Learning And Deep Learning Projects All At One Place07:49
- Google Colab Pro Vs Colab Free- Benefits Of Using Colab Pro- How To Access From India13:08
- Advance Python Series-Magic Methods In Classes10:07
- Advanced Python Series- Assert Statement In Python06:05
- How To Speed Up Pandas By 4X Times- Modin Pandas Library12:08
- TextBlob Library In Python For Natural Language Processing08:53
- 3000+ Research Datasets For Machine Learning Researchers By Papers With Code06:38
- Introduction To MLflow-An Open Source Platform for the Machine Learning Lifecycle12:13
- Amazing Data Science End To End Project From Starters In ML and Deep Learning- Agriculture Domain08:16
- Lux - Python Library for Intelligent Visual Discovery10:11
- Texthero-Text Preprocessing, Representation And Visualization From Zero to Hero.15:58
- Colab Pro Now Available In India, Brazil, France, Thailand,Japan,UK- BOON FOR Data Science Aspirants05:30
- Rainfall Prediction- Converting A Kaggle Project to End To End Machine Learning Project06:50
- PyWebIO- Creating WebAPP Using Python Without Using HTML And JS17:18
- Creating BMI Calculator Web APP Using Python And PyWebIO12:11
- Deployment Of ML Models Using PyWebIO And Flask12:28
- Shapash- Python Library To Make Machine Learning Interpretable16:03
- Difference Between fit(), transform(), fit_transform() and predict() methods in Scikit-Learn26:03
- EvalML AutoML Library To Automate Feature Engineering, Feature Selection,Model Creation And Tuning23:28
- Lazy Predict Python- Understanding Which Models Works Well Without Any Tuning09:05
- How To Automate NLP Tasks Using EvalML Library15:28
- Gradio Library-Interfaces for your Machine Learning Models08:48
- Comparing Transfer Learning Models Using Gradio08:24
- Introduction To Machine Learning And Deep Learning For Starters50:28
- Numba Library- Let's Make Python Faster06:45
- Deployment Of ML Models Using PyWebIO And Flask In Heroku07:33
- All Automated EDA Libraries All At One Place14:39
- Discussing All The Types Of Feature Transformation In Machine Learning22:24
- Automating Web Scrapping Using AutoScraper Library15:33
- Automating WebScraping Amazon Ecommerce Website Using AutoScrapper14:14
- AutoScraper and Flask: Create an API From Amazon Website in Less Than 10 Minutes14:13
- Autoviz-Automatically Visualize Any Dataset With Single Line Of Code06:10
- AutoScraper- Scrap Images From Amazon Ecommerce- End To End Web Scraping Application06:55
- All Type Of Cross Validation With Python All In 1 Video15:23
- DataPrep Library- Perform Faster EDA Within No Time08:42
- Time Series Forecasting Made Easy Using Dart Library - Perform Multivariate Forecasting In No Time11:37
- FLAML - Fast and Lightweight AutoML Library By Microsoft07:32
- Tutorial on Automated Machine Learning using MLBox11:03
- Definition Of Bias And Variance In Machine Learning- Interview Question08:18
- Elasticnet Regression Machine Learning Algorithm Explained In Depth11:04
- Out Of Bag Evaluation(OOB) And OOB Score Or Error In Random Forest07:11
- PCA Indepth Geometric And Mathematical InDepth Intuition ML Algorithms01:28:31
