Introduction -- Data Science and Machine Learning using Python - A Bootcamp (Skillshare)

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 these 10 classes:

• 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.
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, transections 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.

Dear learners, There are 10 classes (Class 1 to Class 10) in this bootcamp. You will learn range of python libraries that are essential to learn the Data Science and Machine Learning. During this journey, you will do lots and lots of hands-on training and implement several end-to-end machine learning models using real datasets. We will cover all these libraries in this bootcamp along with end-to-end Machine Learning projects using state of the art algorithms and real datasets. You will learn theory and get lots of hands-on training using real data project.