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 **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 real-life 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 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.