h264, yuv420p, 1280x720 | ENGLISH, aac, 48000 Hz, 2 channels, s16 | 24h 57 mn | 12.11 GB
Instructor: Dr. Junaid Qazi, PhD

Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decision making.

Numpy Pandas Matplotlib Seaborn Ploty Machine Learning Scikit-Learn Data Science Recommender system NLP Theory Hands-on


What you'll learn



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 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!

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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