Complete Data Science & Machine Learning Course

Learn Complete Data Science & Machine Learning Course

What you'll learn

Master the essential concepts, techniques, and tools of data science and machine learning.

Acquire hands-on experience with Python programming and its libraries for data manipulation, analysis, and visualization.

Build and evaluate predictive models using a variety of machine learning algorithms and techniques.

Complete Data Science & Machine Learning Course

Requirements

python installed

Description

Course Title: Complete Data Science and Machine Learning CourseCourse Description:Welcome to the "Complete Data Science and Machine Learning Course"! In this comprehensive course, you will embark on a journey to master the fundamentals of data science and machine learning, from data preprocessing and exploratory data analysis to building predictive models and deploying them into production. Whether you're a beginner or an experienced professional, this course will provide you with the knowledge and skills needed to succeed in the dynamic field of data science and machine learning.Class Overview:Introduction to Data Science and Machine Learning:Understand the principles and concepts of data science and machine learning.Explore real-world applications and use cases of data science across various industries.Python Fundamentals for Data Science:Learn the basics of Python programming language and its libraries for data science, including NumPy, Pandas, and Matplotlib.Master data manipulation, analysis, and visualization techniques using Python.Data Preprocessing and Cleaning:Understand the importance of data preprocessing and cleaning in the data science workflow.Learn techniques for handling missing data, outliers, and inconsistencies in datasets.Exploratory Data Analysis (EDA):Perform exploratory data analysis to gain insights into the underlying patterns and relationships in the data.Visualize data distributions, correlations, and trends using statistical methods and visualization tools.Feature Engineering and Selection:Engineer new features and transform existing ones to improve model performance.Select relevant features using techniques such as feature importance ranking and dimensionality reduction.Model Building and Evaluation:Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, and gradient boosting.Evaluate model performance using appropriate metrics and techniques, including cross-validation and hyperparameter tuning.Advanced Machine Learning Techniques:Dive into advanced machine learning techniques such as support vector machines (SVM), neural networks, and ensemble methods.Model Deployment and Productionization:Deploy trained machine learning models into production environments using containerization and cloud services.Monitor model performance, scalability, and reliability in production and make necessary adjustments.Enroll now and unlock the full potential of data science and machine learning with the Complete Data Science and Machine Learning Course!

Overview

Section 1: Introduction To Complete Data Science & Machine Learning Course

Lecture 1 Introduction To Course

Section 2: Complete Python Programming Course

Lecture 2 Python Complete Course Introduction

Lecture 3 Python Class 1 : Introduction To Python

Lecture 4 Python Class 2 : Setting Python Environment

Lecture 5 Python Class 3 : Introduction To Variables

Lecture 6 Python Class 4 : Introduction To Keywords

Lecture 7 Python Class 5 : Introduction To Datatypes

Lecture 8 Python Class 6 : ID Function

Lecture 9 Python Class 7 : Arithmetic Operator

Lecture 10 Python Class 8 : Logical Operator

Lecture 11 Python Class 9 : Comparison Operator

Lecture 12 Python Class 10 : Bitwise Operator

Lecture 13 Python Class 11 : Membership Operator

Lecture 14 Python Class 12 : Identity Operator

Lecture 15 Python Class 13 : Conditional Statements

Lecture 16 Python Class 14 : For Loop and Range Function

Lecture 17 Python Class 15 : While Loops

Lecture 18 Python Class 16 : Break and Continue

Lecture 19 Python Class 17 : Function

Lecture 20 Python Class 18 : Try Except Finally Blocks

Lecture 21 Python Class 19 : String and Functions

Lecture 22 Python Class 20 : List and Functions

Lecture 23 Python Class 21 : Tuple and Functions

Lecture 24 Python Class 22 : Dictionary and Functions

Lecture 25 Python Class 23 : Class and Object

Lecture 26 Python Class 24 : Class Methods

Lecture 27 Python Class 25 : Inheritance and its types

Lecture 28 Python Class 26 : Polymorphism and its types

Lecture 29 Python Class 27 : Encapsulation and Access Modifiers

Lecture 30 Python Class 28 : Abstraction

Lecture 31 Python Class 29 : Mini Project

Section 3: Complete Data Science Course

Lecture 32 Complete Data Science Course

Lecture 33 Numpy Complete Course

Lecture 34 Numpy Class 1 : Import and Install

Lecture 35 Numpy Class 2 : Array and its Types

Lecture 36 Numpy Class 3 : Datatypes

Lecture 37 Numpy Class 4 : NDIM Function

Lecture 38 Numpy Class 5 : ARANGE Function

Lecture 39 Numpy Class 6 : CONCATENATE Function

Lecture 40 Numpy Class 7 : NDMIN Function

Lecture 41 Numpy Class 8 : NDITER Function

Lecture 42 Numpy Class 9 : All Functions

Lecture 43 Pandas Class 1 : Import Dataset

Lecture 44 Pandas Class 2 : Head & Tail Function

Lecture 45 Pandas Class 3 : Info Function

Lecture 46 Pandas Class 4 : Drop na Function

Lecture 47 Pandas Class 5 : Fill na Function

Lecture 48 Pandas Class 6 : Drop Duplicates Function

Lecture 49 Pandas Class 7 : Replace Values Function

Lecture 50 Matplotlib Class 1 : Import Dataset

Lecture 51 Matplotlib Class 2 : Show Function

Lecture 52 Matplotlib Class 3 : Marker Function

Lecture 53 Matplotlib Class 4 : Xlabel Ylabel Function

Lecture 54 Matplotlib Class 5 : Title Function

Lecture 55 Matplotlib Class 6 : Linestyle Linewidth Function

Lecture 56 Matplotlib Class 7 : Barplot

Section 4: Complete Machine Learning Course

Lecture 57 Complete Machine Learning Introduction

Lecture 58 Machine Learning Class 1 : Linear Regression

Lecture 59 Machine Learning Class 2 : Logistics Regression

Lecture 60 Machine Learning Class 3 : Support Vector Machine

Lecture 61 Machine Learning Class 4 : KNN

Lecture 62 Machine Learning Class 5 : K Means Clustering

Lecture 63 Machine Learning Class 6 : Naive Bayes

Lecture 64 Machine Learning Class 7 : Decision Tree Classifier

Lecture 65 Machine Learning Class 8 : Random Forest

Students and professionals interested in pursuing a career in data science, machine learning, or artificial intelligence.,Professionals seeking to enhance their skills and stay competitive in the rapidly evolving field of data science and machine learning.