What you will learn
- Python for Data Science (Data Analysis & PowerBI)
- Mathematics & Statistics for Data Science
- SQL Fundamentals
- Building APIs with Flask.
- Machine Learning Specialist Course
- Deep Learning
- Natural Language Processing (NLP)
- Deployment
This course includes
- 🎫 15 Capstone Project
- 📊7 modules
- ⏰ 150 Hours
- 📋Assignments and 2 Capstone Project
Curriculum of this diploma
A. Python for beginners
1. Syntax
2. Variables
3. Data types
- Numbers
- Strings
- Lists
- Set, tuples and Boolean
- Dictionary
4. Flowchart
5. Input & output
6. Comparison operators
7. Conditional statements
8. Loops (while/for loops)
9. Python built-in functions (enumerate, zip, …)
10. List|Dict Comprehensions
11. Mini-project #1 (Calculator)
12. Mini-project #2 (Guess the Number Game)
13. Functions
14. Scope of variables (Local/Global)
15. File Handling
16. Lambda Expressions
17. Map, Reduce, Filter using Lambda Expression
18. Error Handling
19. Introduction to classes
20. OOP Concepts (Encapsulation, inheritance, abstraction, polymorphism)
21. Converting Jupiter notebook to .py
22. Importing .py & packages
23. OS package handling
24. Git & GitHub
25. Mini-project #3 (TicTacToe)
26. Mini-project #4 (Rock Paper Scissors)
27. Python Final Project (Classes using 4 Mini-projects)
C. Math & Statistics for Data Science
1. Getting started with statistics
2. Data Classification
3. Standard Deviation
4. Correlation and Covariation
5. Introduction to Probability
6. Hypothesis Testing
7. Marginal and Conditional Probability
8. Normal Distribution
9. Linear Algebra (Matrix Calculations)
D. Python Packages for Data Analysis
1. Numpy
2. Pandas
3. Matplotlib
4. Seaborn
E. Data Importing & Cleansing
1. Importing from different sources of data using Pandas
2. Handling Missing and Invalid Data
3. Cleansing Missing Data Using Pandas Python Package
4. Cleansing the outlying data using Pandas Python Package
5. Missing Data Imputation
6. Categorical Variable Encoding
7. Introductory Web Scraping
F. Exploratory Data Analysis using Python
1. Visualization Options
2. Data Modelling
3. Introduction to PowerBI
G. Exploratory Data Analysis Projects
1. Data Visualization using Python Packages
2. Project #1: TMDB Movies Dataset Analysis
3. EDA Using PowerBI
4. Project #2: Udemy Courses Dataset Analysis
B. Databases (SQL--MYSQL)
1. Introduction to Database
2. Relational Database
- Primary key
- Foreign Key
- Entity Relationships
3. SQL Syntax
4. DDL (Database handling)
7. Database Project (Company Usecase)
- Project Modelling
- Mapping database tables to relations
- Querying Tasks
8. Connect database to python
9. Bonus Video (Python TKinter for GUI)
10. Bonus Task (Create a GUI interface for Company Database)
- Create Database & Tables
- Alter
- Drop
- Truncate
5. Data Manipulation (Data Handling)
- Select
- Insert
- Update
- Delete
6. Query Language
- Select Clause
- Where Clause (filter records)
- Order By Clause
- Group By Clause
- Having Clause
- Joining Records (Inner|Outer|Cross)
- Aggregate functions (AVG|COUNT|SUM|MIN|MAX)
- Nested Queries
I. Machine Learning
1. Introduction to Machine Learning Fundamentals
2. Classification Models
- K-Nearest Neighbor (KNN)
- Logistic Regression
- Precision, Recall & F-score
- AUC & ROC
- Support Vector Machine (SVM)
- Naive Bayes
- Cross Validation
- Grid Search
- Under Sampling and Oversampling (Overfitting & Underfitting)
- Balanced and In Balanced Data
- Decision Tree Classification
- Random Forest Classification (Ensemble Method, Bagging and Boosting)
- Confusion Matrix
- Project #3: Breast Cancer Wisconsin Classification
- Project #4: UCI Heart Disease Classification
3. Regression Models
- Linear Regression (Feature Selection, Gradient Descent, Ordinary Least Squares)
- Multiple Linear Regression
- Bias and Variance
- Overfitting and Underfitting
- MSE, MAE, R-Square
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Project #5: Diamonds Dataset Regression
4. Unsupervised Learning (Clustering)
- K-Means & Hierarchical Clustering (Mall problem)
- DBSCAN (Density based Clustering)
- Project #6: Breast Cancer Cluster Analysis
5. Model Selection & Boosting
- K-fold Cross Validation
- Parameter Tuning
- Grid Search
- XGBoost
6. Dimensionality Reduction
- PCA
J. Deep Learning
1. Introduction to Deep Learning
2. Introduction keras & tensorflow
3. Artificial Neural Networks
4. Convolutional Neural Networks (Computer Vision)
5. Project #7: Fashion Mnist Image Classification
6. Recurrent Neural Networks
7. LSTM
8. Project #8: Apple Stock Price Prediction
K. Introduction to NLP
1- Project #9: Building Word Embeddings Using Word2Vec
L. Introduction to Deployment
1. APIs using Flask
2. Introduction to Amazon Web Services
3. Introduction to Big Data
what will you learn for Machine Learning on Python Track?
- Machine Learning Foundations: explore the core concepts of Machine Learning which involve understanding your dataset.
- Supervised Learning & Unsupervised Learning Models: get a background in model building, a common class of methods for model construction.
- Have a great intuition of many Machine Learning models
- Learning models, Make powerful analysis & accurate predictions
- Create strong added value to your business & Use Machine Learning for personal purpose (Complete a capstone project in your chosen domain)
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction (PCA)
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem (Create your GitHub account)
To succeed in this course, you need:
Basic knowledge of linear algebra and calculus. The ability to apply basic probability and statistics. Programming experience in Python. Some experience implementing computer science algorithms and object-oriented programming. The ability to run programs and interpret output from a command line terminal or shell. Access to a Windows, macOS, or Linux computer with Python 3.4 or later installed, and admin permissions to install new programs
Who this course is for:
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful ML tools.
Complete Data Science Diploma Track:
- Mathematics.
- Statistics.
- Python.
- Advanced Statistics in Python.
- Machine & Deep Learning.
What you’ll learn:
- Our course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in-demand data science skills:
(Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow)
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data Understand the mathematics behind Machine Learning (an absolute must)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Carry out cluster and factor analysis
- Be able to create Machine Learning algorithms in Python, using NumPy, stats models, and scikit-learn- Apply your skills to real-life business cases.
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlow
- Develop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks.
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross-validation, testing, and how hyperparameters could improve performance.
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations