Mastering Python with Machine Learning
Python, Machine Learning, R Programming, NLP, NN, Deep Learning
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Python Fundamentals / Prerequisites for AI
- What is PL?
- Python Interpreter
- How to run python code/latest version of Python
- First python program
- Python 2/ Python 3
- How does Python work?
- Data types
- Numbers and Math functions
- Operator precedence
- Exercise Variables/expressions/statements
- Augmented Assignment operator
- Strings
- String concatenation
- Type conversions
- Escape sequence
- Formatted strings
- String indexes
- Immutability
- Built in Functions and methods
- Booleans Type conversion
- Lists/slicing Matrix
- List Methods/2/3
- List unpacking
- Dictionaries-->Keys/methods/methods2
- Tuples 2 Sets
- Conditional Logic
- Indentation in python
- pgDictionaries-->Keys/methods/methods2
- Tuples 2
- Sets
- Conditional Logic
- Indentation in python
- Ternary operators
- Logical operators
- For loops Iterables
- Exercise: tricky counter
- Range()/Enumerator()
- While Loop
- Break/continue/pass
- First Gui Exercise: Find duplicates
- Functions Parameters and arguments default parameters and keywords
- Return Methods vs functions
- Docstrings clean code *args and **kwargs
- Scope/rules global/local keywords why need scope?
- Pure functions map() Filter() zip() reduce()
- List comprehensions
- Set comprehensions
- Packages in python
Machine Learning with Python
- What is ML
- AI/ML/DS YT Recommendation Engine
- Types of ML
- Types of ML problems
- Features in DATA
- Splitting data and Picking Model Tuning and Comparison
- Underfitting and Overfitting Anaconda
- Environment Setup in Mac/Windows
- Jupyter notebook
- Pandas Introduction (Data Analysis)
- NumPY
- Matplotlib: Plotting and Data Visualization
- Scikit: Creating ML models
- Fitting Model into Data Making
- Predictions with the model
- Evaluation of an ML model
- Confusion Matrix
- Project Supervised Learning classification
- Data Engineering
- What? Why? who?
- Learn SQL
Regression Models in ML through Python and R Programming
- Simple Linear Regression
- Multiple linear regression
- Polynomial regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Logistic Regression
Natural Language Processing in Python
Neural Networks and Deep Learning
- Artificial Neural Network
- Convolution Neural Networks
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