Machine Learning with Python and Statistics
About this course
This course is specifically designed for students to learn the concepts in Python, Statistics and Maching Learning. We have tailored this curriculum so that even non-technical students can opt this course and understand the complex concepts. This course includes concepts in Python such as: Variables, functions,Pandas, Numpy, exception handling, web scraping, multithreading,connecting to database, matplotlib, modules, packages,files, flask,grammer correction and speech to text conversion. Projects in Python such as Hangman, Snake Game, Phonebook and Password Generator. For Statistics it includes concepts such as Inferential statistics, Descriptive statistics,data types, population, Central Tendencies, Measures of Dispersion,Z-score, Min-max scaling, Co-variance, Correlation, Multi-collinearity, Anova, Kurtosis,Normal Distribution, Poisson Distribution,Bionominal Distribution,Hypothesis Testing, Central Limit Theorem, Degrees Of Freedom, Confidence Interval, P-value.It also covers important Machine Learning algorithms such as Linear Regression, Logistic Regression,Confusion Matrix, Cost Matrix, Naive Bayes, K-Nearest Neighbors, Decision Tree Algorithm, Random Forest Algorithm,Support Vector Machine, Polynomial Regression, Unsupervised Learning, K-Means Clustering, Principal Component Analysis, DBSCAN, Linear Discriminant Analysis, Linear regression, Logistic Regression, Naive Bayes, KNN, Decision Tree, Support Vector Machine, K means Clustering, Principal Component Analysis, Hierarchical Clustering and Docker for Machine Learning. We have also included 'Deployment of Machine Learning' as one of the section so that user can learn to built the model from scratch and deploy it on its own.
69/100
CourseAsk score
- What the provider tells you
- 45/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- understand Python programming fundamentals
- apply statistical methods to analyze data
- implement machine learning algorithms
- deploy machine learning models
- create projects like games and data applications
Course objectives
- provide a foundational understanding of Python programming
- teach statistical analysis techniques
- introduce machine learning algorithms and their applications
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