- Course type
- Paid course
- All Levels
- 25 hours
- 111 lessons
- Available on completion
- Course author
- Dr. Junaid Qazi, PhD
- 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...................!
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
Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions
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
Basic Plotting & Object Oriented Approach
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:
Over fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning Models
Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision
K Nearest Neighbour (KNN)
Curse of Dimensionality, Model Performance
Tree Depth, Splitting at Nodes, Entropy, Information Gain
Bootstrap, Bagging (Bootstrap Aggregation)
K Mean Clustering
Principle Component Analysis (PCA)
Support Vector Machine
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!