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 COURSE-OVERVIEW
- 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
- Transpose of a matrix
- The inverse of a matrix
- The determinant of a matrix
- Dot product
- 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
- Understand and use linear, non-linear 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
- Python Pandas-Intro
- Python Numpy-Intro
- Python SciPy-Intro
- Python Matplotlib-Intro
- Python Basics
- Python Data Structures
- Programming Fundamentals
- Working with data in Python
- Object-oriented 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 high-level mathematical computing using the NumPy package and its vast library of mathematical functions
- Perform scientific and technical computing using the SciPy package and its sub-packages 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 in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
- Use the matplotlib library of Python for data visualization
- Extract useful data from websites by performing web scraping using
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 in-depth 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
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
In-depth 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 A-Z 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 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.
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.
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 well-known Python data processing library and various other libraries for reading, analyzing, processing and cleaning data.
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.
What Will You Learn?
- Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package
- Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions.
- Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing
- Apply knowledge and actionable insights from data across a broad range of application domains.
Basic Maths Required for Data Science
Types of Statistics00:00
Measures of Shapes00:00
Normal Probability Distribution00:00
Central Limit Theorem00:00
Hypothesis Testing for Decision Making00:00
python for Data Science
Identifiers in Python00:00
Comments in Python00:00
Variables in Python00:00
Data Types & Related Stuffs in Python00:00
Conversion of Data Types in Python00:00
Python I/O functions00:00
User Input in Python00:00
Operators in Python00:00
Control Flow in Python00:00
Functions in Python00:00
Types of Functions in Python00:00
Recursive Functions in Python00:00
Argument in a Function00:00
Lambda or Anonymous Functions in Python00:00
Advance Programming in Python00:00
Advance Programming in Python: Part 200:00
Let’s Dig Deeper
EDA on Mc’donalds Data Set00:00
Exploratory Data Analysis00:00
Let’s explore Machine Learning
Introduction: Machine Learning00:00
How to use Linear Regression00:00
Logistic Regression on Titanic Data Set00:00
Algorithms used in Decision Treee00:00
Issues with Decision Tree00:00
Applications of Decision Tree00:00
Working on Titanic Data Set00:00
Types of Random Forest00:00
Why Random Forest00:00
Application of Random Forest00:00
Random Forest Implementation on Titanic Data Set00:00
Model Evaluation Technique00:00
Concept of R-Squared00:00
Recall / Sensitivity / True Rate of Positive00:00
AUC/ ROC curve00:00
Model Evaluation recall Curve00:00
Data Analysis using R00:00
Data Analysis using R: part 200:00
All about R Language00:00
Intro to Hadoop00:00
Intro to Tableu00:00
Intro to Business Analytics00:00
Project: Telecom Churn Production
Project: Part 1: Let’s get our system ready00:00
Project: part 200:00
Project: Part 300:00
Project: part 400:00
Project: Let’s Finalise it00:00
Additional Resources : Downloadable