Data Science 2020 : Complete Data Science & Machine Learning
4.6 (888 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,563 students enrolled

Data Science 2020 : Complete Data Science & Machine Learning

Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science
4.6 (888 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,563 students enrolled
Last updated 6/2020
English
English
Current price: $119.99 Original price: $199.99 Discount: 40% off
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This course includes
  • 26 hours on-demand video
  • 7 articles
  • 49 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn Complete Data Science skillset required to be a Data Scientist with all the advance concepts
  • Master Python Programming from Basics to advance as required for Data Science and Machine Learning
  • Learn complete Mathematics of Linear Algebra, Calculus, Vectors, Matrices for Data Science and Machine Learning.
  • Become an expert in Statistics including Descriptive and Inferential Statistics.
  • Learn how to analyse the data using data visualization with all the necessary charts and plots
  • Perform data Processing using Pandas and ScikitLearn
  • Master Regression with all its parameters and assumptions
  • Solve a Kaggle project and see how to achieve top 1 percentile
  • Learn various classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machines
  • Get complete understanding of deep learning using Keras and Tensorflow
  • Become the Pro by learning Feature Selection and Dimensionality Reduction
Course content
Expand all 281 lectures 26:15:37
+ Introduction
4 lectures 14:20
Download Course Material
02:41
Udemy Reviews - Important Message
03:56
+ -- Part 1: Essential Python Programming --
21 lectures 01:42:41
Install Anaconda, Spyder
04:09
Hands On - Hello Python and Know the environment
05:24
Hands On - Variable Types and Operators
08:55
Hands On - Decision Making - If-Else
05:41
Python Loops explained
02:25
Hands On - While Loops
05:18
Hands On - For Loops
04:30
Python Lists Explained
01:45
Hands On - Lists Basic Operations
03:45
Hands On - Lists Operations Part 2
02:49
Multidimensional Lists Explained
04:10
Hands On - Slicing Multidimensional lists
05:48
Hands On - Python Tuples
03:35
Python Dictionary Explained
03:22
Hands On - Access the Dictionary Data
04:46
Hands On - Dictionary Methods and functions
03:56
File processing - Open and Read files
07:03
File Processing - Process Data and Write to Files
05:18
File Processing - Process Data using Loops
04:31
Project 1 - Calculate the average temperature per city
08:30
Solution - Project 1 calculate the average temperature per city
07:01

Let's test what you have learnt so far from the python programming lectures.

Essential Python Programming
5 questions
+ -- Part 2: Essential Mathematics --
28 lectures 02:30:24
What you will learn in this Part?
00:26
Algebraic Equations
08:23
Exponents and Logs
04:22
Polynomial Equations
04:01
Factoring
03:38
Quadratic Equations
02:40
Functions
04:05

Let's refresh and test our knowledge of the Algebra.

Algebra Foundations
4 questions
Calculus Foundation
05:15
Rate of Change
02:24
Limits
03:05
Derivative Rules and Operations
06:33
Double Derivatives and finding Maxima
05:39
Double Derivatives example
09:59
Partial Derivatives and Gradient Descent
04:02
Integration and Area Under the Curve
04:23

Let's test what we have learnt in the section of Calculus.

Calculus
4 questions
Vector Basics - What is a Vector and vector operations
05:09
Vector Arithmetic
03:55
Matrix Foundation
03:26
Matrix Arithmetic
08:46
Identity, Inverse, Determinant and Transpose Matrix
03:54
Matrix Transformation
04:26
Change of Basis and Axis using Matrix Transformation
09:44
Eigenvalues and Eigenvectors
06:11

Let's test what we learnt about the vectors, matrices and their transformation.

Linear Algebra
5 questions
Understanding probability in simple terms
06:02
Probability Terms
01:49
Conditional Probability
06:45
Random Processes and Random Variables
08:34
Probability Foundation
4 questions
+ What is Data Science and Machine Learning?
8 lectures 42:00
Need for Data Science and Machine Learning
07:02
Decoding Data Science and Machine Learning
09:22
Data Science Project Lifecycle Part 1
02:54
Data Science Project Lifecycle Part 2
03:51
Data Science Project Lifecycle Part 3
03:37
Data Science Project Lifecycle Part 4
03:44
What does a Data Scientist do and the skills required?
06:24

Let's test what we have learnt so far.

Data Science Basics
4 questions
+ -- Part 3: Essential Statistics --
1 lecture 00:24
What you will learn in this part?
00:24
+ Descriptive Statistics
5 lectures 27:28
What is Data? Understanding the Data and its elements.
04:37
Measure of Central Tendency using Mean, Median, mode
06:19
Measure of Dispersion using Standard Deviation and variance
07:10
Hands on - Get Statistical Summary
03:04
Measure of Dispersion using Percentile, Range and IQR
06:18
+ Data Visualization
20 lectures 01:29:55
Importance of Data Visualization
02:45
Data Visualization - Frequency Table, Histogram and Bar Chart
03:05
Understanding Boxplot for Numerical Data
04:37
What is a Plot?
02:30
Hands On - Create Line Plots
08:51
Hands On - Understand Plot Figure Menu
02:18
Hands On - Create your first Bar Chart
03:35
Hands On - Create Histogram of Data
07:05
Hands On - Plotting Boxplot
04:11
Data Visualization for Categorical Data
06:49
Hands On - Pie Charts Part 1
08:16
Hands On - Pie Charts Part 2
02:40
Hands On - Scatter Plots
06:24
Hands On - MatplotLib Figures for creating multiple plots
06:53
Hands On - Subplots for plotting multiple plots in one figure
07:57
Hands On - Customization of Plot elements Part 1
01:48
Hands On - Customization of Plot elements Part 2
03:22
Hands On - Customization of Plot elements Part 3
02:46
Hands On - Customization of Plot elements Part 4
03:47
Claim your reward now.
00:16
+ Inferential Statistics, Distributions and Hypothesis
14 lectures 02:19:19
Understand Population Vs Samples
09:24
What is a Sample Bias?
11:33
What is Correlation and Causality?
10:04
What is Covariance and Covariance Matrix?
09:09
Probability Density Function and Distributions
09:51
Normal Distributions
08:58
Standard Normal Distributions
15:28
Sampling Distributions
05:50
Central Limit Theorem
07:45
Confidence Interval - Part 1
08:03
Confidence Interval - Part 2
13:42
What is Hypothesis and Null Vs Alternate Hypothesis?
09:09
What is Statistical Significance
09:17
Hypothesis Testing Examples
11:06
+ -- Part 4: Data Pre-Processing --
12 lectures 01:18:31
Hands On - Import Library to Read and Slice the data
12:34
Hands On - Understand the data you are dealing with
05:53
Hands On - Handling Missing Values
13:07
Label-Encoding for Categorical Data
02:45
Hands On Label Encoding
04:24
Hot-Encoding for Categorical Data Explained
03:15
Hands On - Hot-Encoding for Categorical Data
06:18
Data normalization - Understand the reasons.
06:43
Hands On - Data Normalization using Standard Scaler
06:51
Hands On - Data Normalization using minmax
03:15
Train and Test Data Split explained
03:42
Hands On - Train and Test Data Split
09:44
+ -- Part 5: Regression --------
1 lecture 00:08
What you will learn in this section?
00:08
Requirements
  • No prerequisites. I will teach right from basics in Python to Advanced Deep Learning
  • Passion to deal with data analysis
Description

Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more?

Well, you have come to the right place. This Data Science and Machine Learning course has 250+ lectures, more than 25+ hours of content, 11 projects including one Kaggle competition with top 1 percentile score, code templates and various quizzes.

Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.

As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning?

Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,

  • Understanding of the overall landscape of Data Science and Machine Learning

  • Different types of Data Analytics, Data Architecture, Deployment characteristics of Data Science and Machine Learning projects

  • Python Programming skills which is the most popular language for Data Science and Machine Learning

  • Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data Science

  • Statistics and Statistical Analysis for Data Science

  • Data Visualization for Data Science

  • Data processing and manipulation before applying Machine Learning

  • Machine Learning

  • Ridge (L2), Lasso (L1) and Elasticnet Regression/ Regularization for Machine Learning

  • Feature Selection and Dimensionality Reduction for Machine Learning models

  • Machine Learning Model Selection using Cross Validation and Hyperparameter Tuning

  • Cluster Analysis for unsupervised Machine Learning

  • Deep Learning using most popular tools and technologies of today.

This Data Science and Machine Learning course has been designed considering all of the above aspects, the true Data Science and Machine Learning A-Z Course. In many Data Science and Machine Learning courses, algorithms are taught without teaching Python or such programming language. However, it is very important to understand the construct of the language in order to implement any discipline including Data Science and Machine Learning.

Also, without understanding the Mathematics and Statistics it's impossible to understand how some of the Data Science and Machine Learning algorithms and techniques work.

Data Science and Machine Learning is a complex set of topics which are interlinked. However, we firmly believe in what Einstein once said,

"If you can not explain it simply, you have not understood it enough."

As an instructor, I always try my level best to live up to this principle. This is one comprehensive course on Data Science and Machine Learning that teaches you everything required to learn Data Science and Machine Learning using the simplest examples with great depth.

As you will see from the preview lectures, some of the most complex topics are explained in a simple language.

Some of the key skills you will learn,

  • Python Programming

    Python has been ranked as the #1 language for Data Science and Machine Learning. It is easy to use and is rich with various libraries and functions required for performing various tasks for Data Science and Machine Learning. Moreover, it is the most preferred and default language of use for many Deep Learning frameworks including Tensorflow and Keras.


  • Advance Mathematics for Machine Learning

    Mathematics is the very basis for Data Science in general and Machine Learning in particular. Without understanding the meanings of Vectors, Matrices, their operations as well as understanding Calculus, it is not possible to understand the foundation of the Data Science and Machine Learning. Gradient Descent which forms the very basis of Neural Network and Machine Learning is built upon the basics of Calculus and Derivatives.


  • Advance Statistics for Data Science

    It is not enough to know only mean, median, mode etc. The advance techniques of Data Science and Machine Learning such as Feature Selection, Dimensionality Reduction using PCA are all based on advance inferential statistics of Distributions and Statistical Significance. It also helps us understanding the data behavior and then apply an appropriate machine learning technique to get the best result from various techniques of Data Science and Machine Learning.


  • Data Visualization

    As they say, picture is worth a thousand words. Data Visualization is one of the key techniques of Data Science and Machine Learning and is used for Exploratory Data Analysis. In that, we visually analyse the data to identify the patterns and trends. We are going to learn how to create various plots and charts as well as how to analyse them for all the practical purposes. Feature Selection plays a key role in Machine Learning and Data Visualisation is key for it.


  • Data Processing

    Data Science require extensive data processing. Data Science and Machine Learning practitioners spend more than 2/3rd of the time processing and analysing the data. Data can be noisy and is never in the best shape and form. Data Processing is one of the key disciplines of Data Science and Machine Learning to get the best results. We will be using Pandas which is the most popular library for data processing in Python and various other libraries to read, analyse, process and clean the data.


  • Machine Learning

    The heart and soul of Data Science is the predictive ability provided by the algorithms from Machine Learning and Deep Learning. Machine Learning takes the overall discipline of Data Science ahead of others. We will combine everything we would learn from the previous sections and build various machine learning models. The key aspects of the Machine Learning is not just about the algorithms but also understanding various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values impact the outcome so that you can build the best machine learning models.


  • Feature Selection and Dimensionality Reduction

    In case you wonder, what makes a good data scientists, then this section is the answer. A good Data Science and Machine Learning practitioner does not just use libraries and code few lines. She will analyse every feature of the data objectively and choose the most relevant ones based on statistical analysis. We will learn how to reduce the number of features as well as how we can retain the value in the data when we practice and build various machine learning models after applying the principles of Feature Selection and Dimensionality Reduction using PCA.


  • Deep Learning

    You can not become a good Data Science and Machine Learning practitioner, if you do not know how to build powerful neural network. Deep Learning can be said to be another kind of Machine Learning with great power and flexibility. After Learning Machine Learning, we are going to learn some key fundamentals of Deep Learning and build a solid foundation first. We will then use Keras and Tensorflow which are the most popular Deep Learning frameworks in the world.


  • Kaggle Project

    As an aspiring Data Scientists, we always wish to work on Kaggle project for Machine Learning and achieve good results. I have spent huge effort and time in making sure you understand the overall process of performing a real Data Science and Machine Learning project. This is going to be a good Machine Learning challenge for you.


Your takeaway from this course,

  1. Complete hands-on experience with huge number of Data Science and Machine Learning projects and exercises

  2. Learn the advance techniques used in the Data Science and Machine Learning

  3. Certificate of Completion for the most in demand skill of Data Science and Machine Learning

  4. All the queries answered in shortest possible time.

  5. All future updates based on updates to libraries, packages

  6. Continuous enhancements and addition of future Machine Learning course material

  7. All the knowledge of Data Science and Machine Learning at fraction of cost

This Data Science and Machine Learning course comes with the Udemy's 30-Day-Money-Back Guarantee with no questions asked.

So what you are waiting for? Hit the "Buy Now" button and get started on your Data Science and Machine Learning journey without spending much time.

I am so eager to see you inside the course.


Disclaimer: All the images used in this course are either created or purchased/downloaded under the license from the provider, mostly from Shutterstock or Pixabay.

Who this course is for:
  • Beginners as well as advance programmers who want to make a career in Data Science and Machine Learning