Data Science & Machine Learning using Python - A Bootcamp

What you'll learn
You will learn the skill set and power of Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making.
Python for Data Science and Machine Learning
NumPy for Numerical Data
Pandas for Data Analysis
Plotting with Matplotlib
Statistical Plots with Seaborn
Interactive dynamic visualizations of data using Plotly
SciKit-Learn for Machine Learning
K-Mean Clustering, Logistic Regression, Linear Regression
Random Forest and Decision Trees
Principal Component Analysis (PCA)
Support Vector Machines
Recommender Systems
Natural Language Processing and Spam Filters
and much more...................!

Requirements
A PC and passion to be successful!
Some experience in programming could be helpful but not required!

Description
Greetings,

I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the impact of your work around your, is not is amazing?

This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making.

Data Science bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is "Learn by Doing"!

For your satisfaction, I would like to mention few topics that we will be learning in this course:

Basis Python programming for Data Science

Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter

NumPy

Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions

Pandas

Pandas Data Structures - Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling - Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data Visualization

Matplotlib

Basic Plotting & Object Oriented Approach

Seaborn

Distribution & Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics

Plotly and Cufflinks

Interactive & Geographical plotting

SciKit-Learn (one of the world's best machine learning Python library) including:

Liner Regression

Over fitting , Under fitting Bias Variance Tradeoff

Logistic Regression

Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision

K Nearest Neighbour

Curse of Dimensionality, Model Performance

Decision Trees

Tree Depth, Splitting at Nodes, Entropy, Information Gain

Random Forest

Bootstrap, Bagging (Bootstrap Aggregation)

K Mean Clustering

Elbow Method

Principle Component Analysis (PCA)

Support Vector Machine

Recommender Systems

Natural Language Processing (NLP)

Tokenization, Text Normalization, Vectorization, BoW, TF-IDF, Pipeline feature........and MUCH MORE..........!

Not only the hands-on practice using tens of real data project, theory lectures are also provided to make you understand the working principle behind the Machine Learning models.

Who this course is for?
For you, if you:
want to learn Data Science with Python
want to learn Machine Learning with Python
are tired of complicated courses and "Learn by Doing"

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