Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more?
Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.
We are going to execute following real-life projects,
- Kaggle Bike Demand Prediction from Kaggle competition
- Automation of the Loan Approval process
- The famous IRIS Classification
- Adult Income Predictions from US Census Dataset
- Bank Telemarketing Predictions
- Breast Cancer Predictions
- Predict Diabetes using Prima Indians Diabetes Dataset
Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.
As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning?
Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,
- Understanding of the overall landscape of Data Science and Machine Learning
- Different types of Data Analytics, Data Architecture, Deployment characteristics of Data Science and Machine Learning projects
- Python Programming skills which is the most popular language for Data Science and Machine Learning
- Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data Science
- Statistics and Statistical Analysis for Data Science
- Data Visualization for Data Science
- Data processing and manipulation before applying Machine Learning
- Machine Learning
- Ridge (L2), Lasso (L1) and Elasticnet Regression/ Regularization for Machine Learning
- Feature Selection and Dimensionality Reduction for Machine Learning models
- Machine Learning Model Selection using Cross Validation and Hyperparameter Tuning
- Cluster Analysis for unsupervised Machine Learning
- Deep Learning using most popular tools and technologies of today.
This Data Science and Machine Learning course has been designed considering all of the above aspects, the true Data Science and Machine Learning A-Z Course. In many Data Science and Machine Learning courses, algorithms are taught without teaching Python or such programming language. However, it is very important to understand the construct of the language in order to implement any discipline including Data Science and Machine Learning.
Also, without understanding the Mathematics and Statistics it’s impossible to understand how some of the Data Science and Machine Learning algorithms and techniques work.
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