Course overview
- Provider
- Udemy
- Course type
- Paid course
- Level
- All Levels
- Duration
- 12 hours
- Lessons
- 88 lessons
- Certificate
- Available on completion
- Course author
- Piyush Dave
-
- The course provides path to become a data scientist
- Problem Solving Approach
- Impress interviewers by showing an understanding of the data science concept with Machine Learning
- Python Basic to Advance Concept
- Python Libraries for Data Analysis such Numpy, Scipy, Pandas
- Python Libraries for Data Visualization such Matplotlib, Seaborn, Plotlypy
- Case Studies of Data Science with Coding
- Machine Learning With Linear Regression, Logistic Regression, SVM, NLP
Description
This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, Numpy Library , Scipy Library , Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, Introduction to Data Science and steps to start Project in Data Science, Case Studies of Data Science and Machine Learning Algorithms such as Linear, Logistic, SVM, NLP
This is best course for any one who wants to start career in data science. with machine Learning.
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.
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.
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
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