Data Science 2022 : Complete Data Science & Machine Learning
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
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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 AZ, Deep learning and more?
Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.
We are going to execute following reallife projects,
 Kaggle Bike Demand Prediction from Kaggle competition
 Automation of the Loan Approval process
 The famous IRIS Classification
 Adult Income Predictions from US Census Dataset
 Bank Telemarketing Predictions
 Breast Cancer Predictions
 Predict Diabetes using Prima Indians Diabetes Dataset
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 indepth 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 AZ 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 ProgrammingPython 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 LearningMathematics 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 ScienceIt 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 VisualizationAs 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 ProcessingData 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 LearningThe 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 ReductionIn 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 LearningYou 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 ProjectAs 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,
 Complete handson experience with huge number of Data Science and Machine Learning projects and exercises
 Learn the advance techniques used in the Data Science and Machine Learning
 Certificate of Completion for the most in demand skill of Data Science and Machine Learning
 All the queries answered in shortest possible time.
 All future updates based on updates to libraries, packages
 Continuous enhancements and addition of future Machine Learning course material
 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 30DayMoneyBack 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
Jose Juan Molina Toledo –
Excelente profesor. Explica todos los conceptos de forma excepcional. Usa palabras sencillas fácilmente entendibles para un no nativo en inglés
Maryam Bello –
This is the best course I’ve seen on data science. So comprehensive and explanatory. kudos to the instructor
Daragh O’Duffy –
So far it has been very good! I like the way the lecturer speaks, slowly and clearly. I have done courses in Universities that are less exhausative and took far longer!
Shubham Jaiswal –
IT IS REALLY A GREAT EXPERIENCE TO LEARN THE DATA SCIENCE BY FUNDAMENTAL CONBCEPT AS WELL AS HANDSON PRACTICE ALONG WITH IT. FEELING LUCKY AND THANKFUL FOR THIS OPPROTUNITY.
Hari Prasanth S –
Overall Best Course & @Jithesh sir explained everything in a easy manner by taking Realtime examples which makes us understand more Better. Now I have basic Idea of all the concepts & strong in Mathematical Concept. Thank You for this Amazing Course for Beginners
Reza Khanbabaie –
This course looks a complete package for a person to become a data scientist. He explains all steps very clearly.
Baijnath Kumar –
Very good explanation of statistics and algorithm
Severino Corsini –
overall good course, just some topics are “obscure” and it’s difficult to understand the logic behind them (for example, the kernels in SVM)
Hammad Ahmed –
A great course to understand all the models in depth. This really helps in building data science projects as you must know all the assumptions for a certain model that you would be using for prediction.
Omer Zaheer –
I have completed almost 25% and so far it is very good course.
AddixData –
Sometimes explanations are very detailled while some others (as cross validation) are only seen associated to other notions
Subodh –
i really enjoyed the course and Jitesh has been a wonderful instructor.the best part about the course is every concept has been explained with its mathematical intuition and there is hands on to apply the concept we have understood.