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 • 65 lessons • 14 hours of video
Data Science and Machine Learning with Python and R
65 lessons
• 14 hours
Data Science and Machine Learning with Python and R
65 lessons
• 14 hours
- Tutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)? 06:27
- Tutorial 43-Random Forest Classifier and Regressor 10:18
- Tutorial 45-Handling imbalanced Dataset using python- Part 1 13:01
- Tutorial 46-Handling imbalanced Dataset using python- Part 2 10:59
- Hyperparameter Optimization for Xgboost 14:55
- K Means Clustering Intuition 14:36
- Hierarchical Clustering intuition 06:12
- DBSCAN Clustering Easily Explained with Implementation 18:32
- Curse of Dimensionality Easily explained| Machine Learning 07:37
- Principle Component Analysis (PCA) using sklearn and python 12:30
- What is Cross Validation and its types? 18:15
- What is Machine Learning in Data Science- Machine Learning Tutorial with Python and R-Part 1 10:00
- What is Supervised Machine Learning- Machine Learning Tutorial with Python and R-Part 2 11:42
- Anaconda installation with Packages- Machine Learning Tutorial with Python and R-Part 3 05:18
- Important libraries used in python Data Science- Machine Learning Tutorial with Python and R-Part 4 08:31
- PySpark Tutorial for Beginners | Apache Spark with Python -Linear Regression Algorithm 18:11
- Computer Vision using Microsoft Cognitive Services for Images 13:38
- How to select the best model using cross validation in python 15:32
- TPR,FPR,FNR,TNR, Confusion Matrix 25:12
- Precision, Recall and F1-Score 09:03
- Artificial Neural Network for Customer's Exit Prediction from Bank 23:05
- GridSearchCV- Select the best hyperparameter for any Classification Model 18:16
- RandomizedSearchCV- Select the best hyperparameter for any Classification Model 12:56
- Complete Life Cycle of a Data Science Project 15:44
- How we can apply Machine Learning in Finance 13:12
- Deep Learning in Medical Science 16:50
- Setting up Raspberry pi 3 B+ 11:31
- How to switch your career to Data Science. 21:59
- Linear Regression Mathematical Intuition 24:17
- Handle Categorical features using Python 18:37
- Feature Selection Techniques Easily Explained | Machine Learning 23:01
- Cross Validation using sklearn and python | Machine Learning 09:48
- Handling Missing Data Easily Explained| Machine Learning 23:22
- Deploy Machine Learning Model using Flask 13:20
- Deployment of Deep Learning Model using Flask 07:33
- How to Visualize Multiple Linear Regression in python 08:25
- Predicting Heart Disease using Machine Learning 11:49
- Predicting Lungs Disease using Deep Learning 13:51
- Stock Sentiment Analysis using News Headlines 13:59
- Random Forest(Bootstrap Aggregation) Easily Explained 09:18
- Voting Classifier(Hard Voting and Soft Voting Classifier) 06:45
- Credit Card Fraud Detection using Machine Learning from Kaggle 18:34
- DNA Sequencing Classifier using Machine Learning 17:18
- Credit card Risk Assessment using Machine Learning 12:10
- Why, How and When to Scale Features in Machine Learning? 10:09
- How to choose number of hidden layers and nodes in Neural Network 14:29
- Diabetes Prediction using Machine Learning from Kaggle 13:33
- How to Read Dataset in Google Colab from Google Drive 09:46
- Malaria Disease Detection using Deep Learning 13:26
- Python Application to Track Amazon Product Prices 13:08
- Train Test Split vs K Fold vs Stratified K fold Cross Validation 13:43
- My Path on Becoming a Data Scientist- Motivation 17:23
- Complete Life Cycle of a Data Science Project 09:57
- Step By Step Transition Towards Data Science 10:06
- What should be your Salary Expectation as a Data Scientist? 09:14
- How to Crack Data Science Interviews- Motivations 12:49
- The Role of Maths in Data Science and How to Learn? 09:42
- Important Tools and Libraries Used By Data Scientist 08:23
- How To Apply Data Science In Your Domain? 06:13
- Skills Required To Become A Data Analyst and a Data Scientist 11:02
- How to Prepare For Data Science Interviews 13:43
- 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 Science 08:45
- Perform Web Scraping On Wikipedia- Data Science 08:34
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