ML for Beginners: Build Your First Smart Model Today

About Course

In today’s data-driven world, the ability to analyze, interpret, and leverage data is a crucial skill across numerous industries. This course is meticulously designed to provide beginners with a comprehensive introduction to the essential concepts, tools, and techniques of data science. This course serves as a gateway to the exciting and rapidly growing field of data science, equipping you with the foundational knowledge and practical skills needed to start your journey in this domain.

Who Should Take This Course?

Beginners with No Prior Experience: Individuals new to data science and programming who want to understand the basics and build a solid foundation.
Career Changers: Professionals from non-technical fields looking to transition into data science or analytics roles.
Students and Recent Graduates: Undergraduates and graduates from any discipline seeking to gain valuable data science skills.
Business Professionals: Analysts, managers, and decision-makers aiming to harness data for strategic planning and operational efficiency.
Educators and Researchers: Academics and researchers needing to analyze and visualize data for their studies and teaching.
Tech Enthusiasts: Hobbyists eager to learn about data science and its applications.

Whether you are looking to start a new career, advance in your current role, or simply gain a deeper understanding of data science, the “Foundations of Data Science” course is tailored to meet your needs and help you achieve your goals. Join us and take the first step towards mastering data science and unlocking the potential of data-driven decision-making.

Understand Machine Learning Fundamentals
Identify Data Science Roles and Responsibilities
Acquire data from multiple sources and perform data cleaning to ensure accuracy and consistency.
Conduct exploratory data analysis (EDA) and create visualizations to uncover patterns and insights using tools like Matplotlib and Seaborn.
Implement strategies for detecting, handling, and imputing missing data.
Identify and work with various data types, including numerical, categorical
Develop, train, and evaluate machine learning models using libraries like Scikit-Learn.
Conduct feature engineering to enhance model performance by creating, transforming, and selecting features.
Interpret the results of machine learning models, including understanding metrics like accuracy, precision, recall, and F1 score.
Execute data preprocessing steps, including normalization, standardization, and encoding categorical variables.

Course Content

Introduction

What is Machine Learning?
Machine Learning Workflow
Applications of Machine Learning
Machine Learning Algorithm
What is a Dataset

Installation and Setup

Python Installation on Windows
What are Virtual Environments
Creating and activating a virtual environment on Windows
Python Installation on macOS
Creating and activating a virtual environment on macOS
What is Jupyter Notebook
Installing Pandas and Jupyter Notebook in the Virtual Environment
Starting Jupyter Notebook
Creating a new Notebook
Exploring Jupyter Notebook Source and Folder Files

Build and train a machine learning model

Installing and importing libraries
What is Data Preprocessing
Download and Load Dataset
Exploring the Dataset
Handle missing values and drop unnecessary columns.
Encode categorical variables.
What is Feature Engineering
Create new features.
Dropping unnecessary columns
Visualize numerical features
Visualize number of passengers that survived
What is a Model
Define features and target variable.
Split data into training and testing sets.
Standardize features.
What is a logistic regression model.
Train logistic regression model.
Making Predictions
What is accuracy in machine learning
What is is classification report.
What is confusion matrix
Evaluate the model using accuracy, confusion matrix, and classification report.
Saving the Model
Loading the model

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