Python Beyond Basics for Machine Learning, Data Science, AI

Data Science, Machine Learning and Artificial Intelligence with Python

Learn the Most demanding language of industry with concept applied to Data Science, Machine Learning and AI

Important topics  are covered such as Python Basic Concepts, Advance Concept, Python Crash Course, Python Libraries such as numpy, pandas, matplotlib, seaborn, Data Science Concept with Case Studies , Machine Learning and it's types, Artificial Intelligence with Case Studies

This Course will design to understand Data Visualization and Data Analysis with  Machine Learning Algorithms with case Studies. 

Data Analysis with Machine Learning Algorithms  such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered.

The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered.

Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered.

Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

Data Visualization and Analysis with ML using Python, Numpy Pandas, Matplotlib, Seaborn, Plotly & Scikit Learn library

This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms  such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.