Data Science and Machine Learning using Python - A Bootcamp

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) 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 Trade-off, saving and loading your trained Machine Learning Models

Logistic Regression

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

K Nearest Neighbour (KNN)

Curse of Dimensionality, Model Performance

Decision Trees

Tree Depth, Splitting at Nodes, Entropy, Information Gain

Random Forests

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, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (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.

So, what are you waiting for, this is your opportunity to learn the real Data Science with a fraction of the cost of any of your undergraduate course.....!

Brief overview of Data around us:

According to IBM, we create 2.5 Quintillion bytes of data daily and 90% of the existing data in the world today, has been created in the last two years alone. Social media, transactions records, cell phones, GPS, emails, research, medical records and much more…., the data comes from everywhere which has created a big talent gap and the industry, across the globe, is experiencing shortage of experts who can answer and resolve the challenges associated with the data. Professionals are needed in the field of Data Science who are capable of handling and presenting the insights of the data to facilitate decision making. This is the time to get into this field with the knowledge and in-depth skills of data analysis and presentation.

Have Fun and Good Luck!