Requirements

An understanding of the fundamentals of Python programming

Basic knowledge of statistics
Description
Today Data Science and Machine Learning are used in almost every industry, including automobiles, banks, health, telecommunications, telecommunications, and more.
As the manager of Data Science and Machine Learning, you will have to research and look beyond common problems, you may need to do a lot of data processing. test data using advanced tools and build amazing business solutions. However, where and how will you learn these skills required in Data Science and Machine Learning?
DATA SCIENCE COURSEOVERVIEW
 Getting Started with Data Science
 Define Data
 Why Data Science?
 Who is a Data Scientist?
 What does a Data Scientist do?
 The lifecycle of Data Science with the help of a use case
 Job trends
 Data Science Components
 Data Science Job Roles
 Math Basics
 Multivariable Calculus
 Functions of several variables
 Derivatives and gradients
 Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function
 Cost function
 Plotting of functions
 Minimum and Maximum values of a function
 Linear Algebra
 Vectors
 Matrices
 Transpose of a matrix
 The inverse of a matrix
 The determinant of a matrix
 Dot product
 Eigenvalues
 Eigenvectors
 Optimization Methods
 Cost function/Objective function
 Likelihood function
 Error function
 Gradient Descent Algorithm and its variants (e.g., Stochastic Gradient Descent Algorithm)
 Programming Basics
 R Programming for Data Science
 History of R
 Why R?
 R Installation
 Installation of R Studio
 Install R Packages.
 R for business
 Features of R
 Basic R syntax
 R programming fundamentals
 Foundational R programming concepts such as data types, vectors arithmetic, indexing, and data frames
 How to perform operations in R including sorting, data wrangling using dplyr, and data visualization with ggplot2
 Understand and use the various graphics in R for data visualization.
 Gain a basic understanding of various statistical concepts.
 Understand and use hypothesis testing method to drive business
 decisions.
 Understand and use linear, nonlinear regression models, and
 classification techniques for data analysis.
 Working with data in R
 Master R programming and understand how various statements are executed in R.
 Python for Data Science
 Introduction to Python for Data Science
 Introduction to Python
 Python Installation
 Python Environment Setup
 Python Packages Installation
 Variables and Datatypes
 Operators
 Python PandasIntro
 Python NumpyIntro
 Python SciPyIntro
 Python MatplotlibIntro
 Python Basics
 Python Data Structures
 Programming Fundamentals
 Working with data in Python
 Objectoriented programming aspects of Python
 Jupyter notebooks
 Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
 Perform highlevel mathematical computing using the NumPy package and its vast library of mathematical functions
 Perform scientific and technical computing using the SciPy package and its subpackages such as Integrate, Optimize, Statistics, IO, and Weave
 Perform data analysis and manipulation using data structures and tools provided in the Pandas package
 Gain an indepth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, KNN and pipeline
 Use the matplotlib library of Python for data visualization
 Extract useful data from websites by performing web scraping using
Python
Integrate Python with MapReduce
 Data Basics
 Learn how to manipulate data in various formats, for example, CSV file, pdf file, text file, etc.
 Learn how to clean data, impute data, scale data, import and export data, and scrape data from the internet.
 Learn data transformation and dimensionality reduction techniques such as covariance matrix plot, principal component analysis (PCA), and linear discriminant analysis (LDA).
 Probability and Statistics Basics
 Important statistical concepts used in data science
 Difference between population and sample
 Types of variables
 Measures of central tendency
 Measures of variability
 Coefficient of variance
 Skewness and Kurtosis
 Inferential Statistics
 Regression and ANOVA
 Exploratory Data Analysis
 Data visualization
 Missing value analysis
 Introduction to Big Data
 Introduction to Hadoop
 Introduction to Tableau
 Introduction to Business Analytics
 Introduction to Machine Learning Basics
 Supervised vs Unsupervised
 Time Series Analysis
 Text Mining
 Data Science Capstone Project
Science and Mechanical Data require indepth knowledge on a variety of topics. Scientific data is not limited to knowing specific packages/libraries and learning how to use them. Science and Mechanical Data requires an accurate understanding of the following skills,
Understand the complete structure of Science and Mechanical Data
Different Types of Data Analytics, Data Design, Scientific Data Transfer Features and Machine Learning Projects
Python Programming Skills which is the most popular language in Science and Mechanical Data
Machine Learning Mathematics including Linear Algebra, Calculus and how to apply it to Machine Learning Algorithms and Science Data
Mathematics and Mathematical Analysis of Data Science
Data Science Data Recognition
Data processing and deception before installing Learning Machines
Machine learning
Ridge (L2), Lasso (L1), and Elasticnet Regression / Regularization for Machine Learning
Selection and Minimization Feature for Machine Learning Models
Selection of Machine Learning Model using Cross Verification and Hyperparameter Tuning
Analysis of Machine Learning Materials Groups
Indepth learning uses the most popular tools and technologies of today.
This Data Science and Machine Learning course is designed to consider all of the above, True Data Science and Machine Learning AZ Course. In most Data Science and Machine Learning courses, algorithms are taught without teaching Python or this programming language. However, it is very important to understand language structure in order to apply any discipline including Data Science and Mechanical Learning.
Also, without understanding Mathematics and Statistics it is impossible to understand how other Data Science and Machine Learning algorithms and techniques work.
Science and Mechanical Data is a set of complex linked topics. However, we strongly believe in what Einstein once said,
“If you can’t explain it easily, you didn’t understand it well enough.”
As a teacher, I constantly strive to reach my goal. This is one comprehensive course in Science and Mechanical Data that teaches you everything you need to learn Science and Mechanical Data using simple examples with great depth.
As you will see from the preview talks, some of the more complex topics are explained in simple language.
Some important skills you will learn,
Python Programming
Python is listed as the # 1 language for Data Science and Mechanical Data. It is easy to use and rich with various libraries and functions required to perform various Data Science and Machine Learning activities. In addition, it is the most widely used and automated language for the use of many Deep Learning frameworks including Tensorflow and Keras.
Advanced Mathematics Learning Machine
Mathematics is the foundation of Data Science in general and Learning Machines in particular. Without understanding the meanings of Vectors, Matrices, their operations and understanding Calculus, it is impossible to understand the basics of Data Science and Machine Learning. The Gradient Declaration of Basic Neural Network and Mechanical Learning is built on the foundations of Calculus and Derivatives.
Previous Statistics for Data Science
It is not enough to know only what you are saying, in the middle, the mode, etc. Advanced Techniques for Science and Mechanical Data such as feature selection, size reduction using PCA are all based on previous Distribution and Statistical Significance calculations. It also helps us to understand the operation of the data and use the appropriate machine learning process to get the best results from various Data Science and Mechanical Learning techniques.
Data recognition
As they say, the picture costs a thousand words. Data identification is one of the most important methods of Data Science and Mechanical Data and is used for Analytical Data Analysis. In that, we analyze the data visually to identify patterns and styles. We will learn how to create different sites and charts and how to analyze them for all practical purposes. Feature Selection plays an important role in Machine Learning and Visualization Data is its key.
Data processing
Scientific Data requires extensive data processing. Data Science and Machine Learning specialists spend more than 2/3 of their time analyzing and analyzing data. Data can be noisy and never in good condition. Data processing is one of the most important ways for Data Science and Mechanics to learn to get the best results. We will be using Pandas which is a wellknown Python data processing library and various other libraries for reading, analyzing, processing and cleaning data.
Machine learning
Heart and Soul Data Science is a guessing skill provided by algorithms from the Deep Learning and Learning Machines. Machine learning takes the complete discipline of Data Science ahead of others. We will integrate everything we have learned in previous sections and build learning models for various machines. The key features of Machine Learning are not only ingenuity but also understanding of the various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values affect the outcome in order to build the best machine learning models.
Who this course is for:
 For Complete Beginners to Data Sciecne, which will make you Hero in the Data Science Field.