Data Science and Machine Learning with Python and R
5.0
(2)
18 learners
What you'll learn
This course includes
- 14 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 66 lessons • 14 hours of video
Data Science and Machine Learning with Python and R
66 lessons
• 14 hours
Data Science and Machine Learning with Python and R
66 lessons
• 14 hours
- How To Become Expertise in Exploratory Data Analysis10:05
- 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
- K Means Clustering Intuition14:36
- Hierarchical Clustering intuition06:12
- DBSCAN Clustering Easily Explained with Implementation18:32
- Curse of Dimensionality Easily explained| Machine Learning07:37
- Principle Component Analysis (PCA) using sklearn and python12:30
- What is Cross Validation and its types?18:15
- What is Machine Learning in Data Science- Machine Learning Tutorial with Python and R-Part 110:00
- What is Supervised Machine Learning- Machine Learning Tutorial with Python and R-Part 211:42
- Anaconda installation with Packages- Machine Learning Tutorial with Python and R-Part 305:18
- Important libraries used in python Data Science- Machine Learning Tutorial with Python and R-Part 408:31
- PySpark Tutorial for Beginners | Apache Spark with Python -Linear Regression Algorithm18:11
- Computer Vision using Microsoft Cognitive Services for Images13:38
- How to select the best model using cross validation in python15:32
- TPR,FPR,FNR,TNR, Confusion Matrix25:12
- Precision, Recall and F1-Score09:03
- Artificial Neural Network for Customer's Exit Prediction from Bank23:05
- GridSearchCV- Select the best hyperparameter for any Classification Model18:16
- RandomizedSearchCV- Select the best hyperparameter for any Classification Model12:56
- Complete Life Cycle of a Data Science Project15:44
- How we can apply Machine Learning in Finance13:12
- Deep Learning in Medical Science16:50
- Setting up Raspberry pi 3 B+11:31
- How to switch your career to Data Science.21:59
- Linear Regression Mathematical Intuition24:17
- Handle Categorical features using Python18:37
- Feature Selection Techniques Easily Explained | Machine Learning23:01
- Cross Validation using sklearn and python | Machine Learning09:48
- Handling Missing Data Easily Explained| Machine Learning23:22
- Deploy Machine Learning Model using Flask13:20
- Deployment of Deep Learning Model using Flask07:33
- How to Visualize Multiple Linear Regression in python08:25
- Predicting Heart Disease using Machine Learning11:49
- Predicting Lungs Disease using Deep Learning13:51
- Stock Sentiment Analysis using News Headlines13:59
- Random Forest(Bootstrap Aggregation) Easily Explained09:18
- Voting Classifier(Hard Voting and Soft Voting Classifier)06:45
- Credit Card Fraud Detection using Machine Learning from Kaggle18:34
- DNA Sequencing Classifier using Machine Learning17:18
- Credit card Risk Assessment using Machine Learning12:10
- Why, How and When to Scale Features in Machine Learning?10:09
- How to choose number of hidden layers and nodes in Neural Network14:29
- Diabetes Prediction using Machine Learning from Kaggle13:33
- How to Read Dataset in Google Colab from Google Drive09:46
- Malaria Disease Detection using Deep Learning13:26
- Python Application to Track Amazon Product Prices13:08
- Train Test Split vs K Fold vs Stratified K fold Cross Validation13:43
- My Path on Becoming a Data Scientist- Motivation17:23
- Complete Life Cycle of a Data Science Project09:57
- Step By Step Transition Towards Data Science10:06
- What should be your Salary Expectation as a Data Scientist?09:14
- How to Crack Data Science Interviews- Motivations12:49
- The Role of Maths in Data Science and How to Learn?09:42
- Important Tools and Libraries Used By Data Scientist08:23
- How To Apply Data Science In Your Domain?06:13
- Skills Required To Become A Data Analyst and a Data Scientist11:02
- How to Prepare For Data Science Interviews13:43
- Why and When Should we Perform Feature Normalization?07:38
- Flask Vs Django and When Should You Use What?06:04
- Top 5 Python IDEs For Data Science08:45
- Perform Web Scraping On Wikipedia- Data Science08:34
