Data Science With Python
- Introduction to Data Science
- Business Intelligence Vs Data Analysis
- Data Analysis Vs Data Scientist
- Data Scientist Roles.
- Different Disciplines of Data Science
- Machine Learning
- Natural Language Processing
- Deep Learning
- Artificial Intelligence
- When to use Machine Learning Models and
- Deep Learning Models.
- Applications of Machine Learning
- Why Machine Learning is the Future
- what are prerequisites for Data Science.
- Statistics
- Python essentials for Data Science
- Different Python modules used for Data Science
- Installing Python and Anaconda (Mac, Linux & Windows)
- Why need to preprocess data
- Importing the Libraries
- Importing the Dataset
- Python: overview of Object-oriented
- programming: classes & objects
- Missing Data
- Categorical Data
- Splitting the Dataset into the Training set and Test set
- Feature Scaling
- Data Preprocessing Template!
- Linear Vs Non-Linear Regression
- Types of Linear Regressions
- what is slope and intercept.
- How to Derive Simple Linear Regression coefficients.
- Simple Linear Regression in python
- Business Problem Description
- What is the P-Value?
- Multiple Linear Regression in Python –
- Backward Elimination – Preparation
- Multiple Linear Regression in Python –
- Automatic Backward Elimination
- Polynomial Regression in Python – Step 1
- Polynomial Regression in Python – Step 2
- Polynomial Regression in Python – Step 3
- Polynomial Regression in Python – Step 4
- How to get the dataset
- SVR in Python
- Decision Tree Regression Intuition
- How to get the dataset
- Decision Tree Regression in Python
- Random Forest Regression Intuition
- How to get the dataset
- Random Forest Regression in Python
- R-Squared Intuition
- Adjusted R-Squared Intuition
- Evaluating Regression Models Performance –
- Homework’s Final Part
- Interpreting Linear Regression Coefficients
- Developing Accuracy testing functions.
- What is a classification model?
- different types of classification models
- when to use what type of model
- how to measure accuracy of a classification model
- Logistic Regression in Python
- K-Nearest Neighbor Intuition
- How to get the dataset
- K-NN in Python
- SVM Intuition
- How to get the dataset
- SVM in Python
- Kernel SVM Intuition
- Mapping to a higher dimension
- The Kernel Trick
- Types of Kernel Functions
- How to get the dataset
- Kernel SVM in Python
- Bayes Theorem
- Naive Bayes Intuition
- Naive Bayes Intuition (Challenge)
- Naive Bayes Intuition (Extras)
- How to get the dataset
- Naive Bayes in Python
- Decision Tree Classification Intuition
- Entropy of Target variable
- Entropy of Input Variable on Target variable
- Information Gain
- How to get the dataset
- Decision Tree Classification in Python
- Random Forest Classification Intuition
- how random forest works
- How to get the dataset
- Random Forest Classification in Python
- Performance
- False Positives & False Negatives
- Confusion Matrix
- Accuracy Paradox
- CAP Curve
- CAP Curve Analysis
- What is a unsupervised learning.
- how to use unsupervised for business problems
- different Clustering models.
- K-Means Clustering Intuition
- K-Means Random Initialization Trap
- K-Means Selecting the number of clusters
- How to get the dataset
- K-Means Clustering in Python
- Hierarchical Clustering Intuition
- Hierarchical Clustering How Dendrograms Work
- Hierarchical Clustering Using Dendrograms
- How to get the dataset
- HC in Python
- what are recommendation systems
- types of recommendation systems
- different ARM algorithms.
- how ARM used for recommendations.
- Apriori Intuition
- How to get the dataset
- Apriori in Python
- FPGrowth Intuition
- problems with other ARM model.
- How to construct Growing tree.
- How to get the dataset
- FPGrowth in Python
- What is Reinforcement Learning
- Models used in Reinforcement Learning
- The Multi-Armed Bandit Problem
- Upper Confidence Bound (UCB) Intuition
- How to get the dataset
- Upper Confidence Bound in Python
- Thompson Sampling Intuition
- Algorithm Comparison: UCB vs Thompson Sampling
- How to get the dataset
- Thompson Sampling in Python
- what is nlp and its importance.
- what we can do with nlp
- Introduction to spam engines .
- Introduction to sentiment analyzers.
- word tokenization
- sentence tokenization
- parts of speech tagging
- lemmatization
- removing stop words
- building word clouds
- feature extraction techniques and importance
- Word Existence feature
- Word proportion feature.
- TFIDF feature.
- NLP vs Machine Learning
- How to get the dataset
- Natural Language Processing in Python
- Homework Challenge
- problems of Machine Learning Models.
- Neural Networks
- How gradient descent algorithm works.
- what are Deep Neural Networks.
- The Neuron
- The Activation Function
- How do Neural Networks work?
- How do Neural Networks learn?
- Gradient Descent
- Stochastic Gradient Descent
- Back propagation
- How to get the dataset
- Business Problem Description
- ANN in Python
- What are convolutional neural networks?
- Softmax & Cross-Entropy
- How to get the dataset
- CNN in Python
- what is a dimension.
- why we should reduce dimensionality.
- different techniques to reduce
- PCA in Python
- How to get the dataset
- LDA in Python
- How to get the dataset
- Kernel PCA in Python
- How to select a model
- deferent techniques to select a model
- How to get the dataset
- k-Fold Cross Validation in Python
- Grid Search in Python
- how to get the dataset
- XGBoost in Pythoni
