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.

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