This course is designed for individuals who are interested in learning the basics of machine learning with Python. The course covers various topics and makes difficult concepts very easy to understand. It includes multiple practical case studies to help you gain hands-on experience in implementing machine learning algorithms.
Session 1: Basic Statistics The first session covers the fundamental concepts of statistics, including mean, median, mode, standard deviation, and variance. These concepts are essential for understanding machine learning algorithms and their outputs.
Session 2: Data Exploration Validation Cleaning In this session, you will learn how to explore and validate your data. You will also learn how to clean your data by removing missing values, handling outliers, and dealing with imbalanced data.
Session 3: Projects Initiation The third session is dedicated to initiating projects. You will learn how to identify business problems, formulate problem statements, and develop project plans.
Session 4: Regression Analysis The fourth session covers regression analysis, including linear regression, multiple regression, and polynomial regression. You will learn how to build regression models and interpret their results.
Session 5: Logistic Regression The fifth session covers logistic regression, which is used for classification problems. You will learn how to build logistic regression models and interpret their results.
Session 6: Project Phase 2 – Simple ML Model In this session, you will implement a simple machine learning model to solve a business problem. You will learn how to preprocess the data, split it into training and testing sets, and build a machine learning model.
Session 7: Decision Trees The seventh session covers decision trees, which are used for classification and regression problems. You will learn how to build decision tree models and interpret their results.
Session 8: Model Selection Cross Validation In this session, you will learn how to select the best machine learning model for your data. You will also learn how to perform cross-validation to assess the performance of your models.
Session 9: Feature Engineering The ninth session covers feature engineering, which involves selecting and transforming the features of your data to improve the performance of your machine learning models.
Session 10: Project Phase 3 In this session, you will implement feature engineering and model selection techniques
Session 11: Random Forest The eleventh session covers random forest, which is an ensemble learning method used for classification and regression problems. You will learn how to build random forest models and interpret their results.
Session 12: Boosting The twelfth session covers boosting, which is another ensemble learning method used for classification and regression problems. You will learn how to build boosting models and interpret their results.
Session 13: Project Phase 4 In this session, you will implement advanced ML models like Random Forest and Boosting
Session 14: NLP Data Preprocessing The fourteenth session covers NLP data preprocessing, which involves cleaning and transforming text data for use in machine learning algorithms.
Session 15: Sentiment Analysis The fifteenth session covers sentiment analysis, which is a natural language processing technique used to determine the sentiment of a piece of text.
Session 16: Testing Of Hypothesis The final session covers hypothesis testing, which is used to determine whether a hypothesis about a population is true based on a sample of data.
In conclusion, this course covers a broad range of machine learning topics and provides practical case studies to reinforce your learning. The course is designed to be one of the best in the industry, and it makes difficult concepts very easy to understand.
