Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning
In this course, you will learn
The entire toolbox you need to become a data scientist.
Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow.
Impress interviewers by showing an understanding of the data science field.
Learn how to pre-process data.
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!).
Start coding in Python and learn how to use it for statistical analysis.
Perform linear and logistic regressions in Python.
Carry out cluster and factor analysis.
Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn.
Apply your skills to real-life business cases.
Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data.
Unfold the power of deep neural networks.
Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance.
Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations.

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning
In this course, you will learn
The entire toolbox you need to become a data scientist.
Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow.
Impress interviewers by showing an understanding of the data science field.
Learn how to pre-process data.
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!).
Start coding in Python and learn how to use it for statistical analysis.
Perform linear and logistic regressions in Python.
Carry out cluster and factor analysis.
Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn.
Apply your skills to real-life business cases.
Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data.
Unfold the power of deep neural networks.
Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance.
Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations.