Complete Data Science Boot Camp Using Python

Data science & Machine learning - Pandas, Numpy, Matplotlib, Scikit learn, Supervised&Deep learning and Neural networks

What you'll learn

Basics of Data science and Machine learning

Create their own Data model and prediction modelling

Data gathering and Data manipulation

Requirements

Basic Python knowledge

Willing to learn new tools

Description

End to end Implementation of Data science and Machine Learning model.From Data analysis and gathering to creating your own modelling will be covered as part of this course.Pandas:Creation of Data representationData filteringData frameworkSelection and viewingData ManipulationNumpy:Datatypes in NumpyCreating arrays and Matrix.Manipulation of data.Standard deviation and variance.Reshaping of Matrix.Dot functionMini-project using Numpy and Pandas packageMatplotlib:Creation Plots - Line, Scatter, bar and Histogram.Creating plots from Pandas and Numpy dataCreation of subplotsCustomization and saving plotsScikit Learn: Scikit-learn is a free, open-source Python library for machine learning. It offers simple, efficient tools for data analysis and modeling, including classification, regression, clustering, preprocessing, and model selection. Built on NumPy and SciPy, it features a consistent API and supports various popular algorithmsSupervised Learning: A machine learning method where models are trained using labeled data, meaning each input is paired with the correct output or label. The algorithm learns the relationship between inputs and outputs, enabling it to predict or classify new, unseen data accuratelySkills & ApplicationsImport, preprocess, and visualize real-world datasetsPerform statistical analyses efficientlyCreate reproducible analyses and effective visual storytellingThis course is ideal for beginners and intermediate learners aiming to build analytical and visualization skills necessary for data-driven decision making in science, business, and engineering.

Overview

Section 1: Machine Learning Introduction

Lecture 1 Introduction to Machine learning

Lecture 2 Areas in AI and Data Science

Lecture 3 Example of Machine learning

Lecture 4 Real time application of Machine learning

Lecture 5 Types of Machine learning

Section 2: Machine learning and Data science Framework

Lecture 6 Introduction to Machine learning and data science framework

Lecture 7 Overview of the Framework

Lecture 8 Types of Machine learning

Lecture 9 Types of Data

Lecture 10 Evaluation in Machine learning

Lecture 11 Modelling in Machine learning

Lecture 12 Experiment and tools used in Machine learning

Section 3: Pandas in Data science

Lecture 13 Introduction to Pandas

Lecture 14 Installation of Python

Lecture 15 How to use Jupiter notebook

Lecture 16 Series and Dataframe in Pandas

Lecture 17 Describe data in Pandas

Lecture 18 Selecting and viewing data- Part 1

Lecture 19 Selecting and viewing data- Part 2

Lecture 20 Manipulation of Data - Part1

Lecture 21 Manipulation of Data - Part2

Lecture 22 Manipulation of Data - Part3

Lecture 23 Error in reset_Index explained

Section 4: Numpy in Data science and machine learning

Lecture 24 Overview Numpy in Machine learning

Lecture 25 Introduction of numpy

Lecture 26 Numpy Datatype and Attribute

Lecture 27 Creating array in Numpy

Lecture 28 Random seed in Numpy

Lecture 29 Viewing matrix in Numpy

Lecture 30 Manipulation in Numpy - Part1

Lecture 31 Manipulation in Numpy - Part2

Lecture 32 Standard deviation and Variance

Lecture 33 Reshape and Transpose

Lecture 34 Dot function in numpy

Lecture 35 Miin-project using Numpy

Lecture 36 Comparison in Numpy

Lecture 37 Sorting in Numpy

Lecture 38 Converting Image to data

Section 5: Matplotlib

Lecture 39 Introduction to Matplotlib

Lecture 40 Overview of MatplotLib

Lecture 41 Create your first plot using Matplotlib

Lecture 42 Types of Plot creation using Matplotlib

Lecture 43 Workflow of MatplotLib

Lecture 44 Creating Line and Scatter plot

Lecture 45 Bar Plot and Histogram

Lecture 46 Subplots in MatplotLib

Lecture 47 Plotting with Pandas data - Part 1

Lecture 48 Plotting with Pandas data - Part 2

Lecture 49 Plotting with Pandas data - Part 3

Lecture 50 Histogram of Heart dataset

Lecture 51 Pyplot vs Object Oriented method

Lecture 52 Advanced Matplotlib - Part 1

Lecture 53 Advanced Matplotlib - Part 2

Lecture 54 Customization of Plot - Part 1

Lecture 55 Customization of Plot - Part 2

Lecture 56 Customization of Plot - Part 3

Lecture 57 Saving of Plot

Section 6: Sci-kit Learn (SKLearn)- Modelling

Lecture 58 Introduction to Scikit Learn

Lecture 59 Scikit Learn Overview

Lecture 60 Workflow of Scikit Learn

Lecture 61 Workflow of Scikit Learn- Implementation Part 1

Lecture 62 Workflow of Scikit Learn- Implementation Part 2

Lecture 63 Workflow of Scikit Learn- Implementation Part 3

Beginners of programming,Willingness in learning to create their own modelling