What you'll learn:
- Master in creating Machine Learning Models on Python
- Visualizing various ML Models wherever possible to develop a better understanding about it.
- How to Analyse the Data, Clean it and Prepare (Data Preprocessing Techniques) it to feed into Machine Learning Models.
- Learn the most Basic Mathematics behind Simple Linear Regression and its Best fit line.
- What is Gradient Descent, how it works Internally with full Mathematical explanation.
- Make predictions using Simple Linear Regression, Multiple Linear Regression.
- Deploy your own model on AWS using Flask so that anyone can access it and get the prediction.
- Make predictions using Logistic Regression, K-Nearest Neighbours and Naive Bayes.
- Fundamental Concept of Deep Learning and Natural Language Processing. Python Code is include at some place for explanation.
- Regularisation and idea behind it. See it in action using Lasso and Ridge Regression.
This course is designed to cover maximum concepts of machine learning a-z. Anyone can opt for this course. No prior understanding of machine learning is required.
Bonus introductions include Natural Language Processing and Deep Learning.
Below Topics are covered
Chapter - Introduction to Machine Learning
- Machine Learning?
- Types of Machine Learning
Chapter - Setup Environment
- Installing Anaconda, how to use Spyder and Jupiter Notebook
- Installing Libraries
Chapter - Creating Environment on cloud (AWS)
- Creating EC2, connecting to EC2
- Installing libraries, transferring files to EC2 instance, executing python scripts
Chapter - Data Preprocessing
- Null Values
- Correlated Feature check
- Data Molding
- Imputing
- Scaling
- Label Encoder
- On-Hot Encoder
Chapter - Supervised Learning: Regression
- Simple Linear Regression
- Minimizing Cost Function - Ordinary Least Square(OLS), Gradient Descent
- Assumptions of Linear Regression, Dummy Variable
- Multiple Linear Regression
- Regression Model Performance - R-Square
- Polynomial Linear Regression
Chapter - Supervised Learning: Classification
- Logistic Regression
- K-Nearest Neighbours
- Naive Bayes
- Saving and Loading ML Models
- Classification Model Performance - Confusion Matrix
Chapter: UnSupervised Learning: Clustering
- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method
- Hierarchical Clustering: Agglomerative, Dendogram
- Density Based Clustering: DBSCAN
- Measuring UnSupervised Clusters Performace - Silhouette Index
Chapter: UnSupervised Learning: Association Rule
- Apriori Algorthm
- Association Rule Mining
Chapter: Deploy Machine Learning Model using Flask
- Understanding the flow
- Serverside and Clientside coding, Setup Flask on AWS, sending request and getting response back from flask server
Chapter: Non-Linear Supervised Algorithm: Decision Tree and Support Vector Machines
- Decision Tree Regression
- Decision Tree Classification
- Support Vector Machines(SVM) - Classification
- Kernel SVM, Soft Margin, Kernel Trick
Chapter - Natural Language Processing
Below Text Preprocessing Techniques with python Code
- Tokenization, Stop Words Removal, N-Grams, Stemming, Word Sense Disambiguation
- Count Vectorizer, Tfidf Vectorizer. Hashing Vector
- Case Study - Spam Filter
Chapter - Deep Learning
- Artificial Neural Networks, Hidden Layer, Activation function
- Forward and Backward Propagation
- Implementing Gate in python using perceptron
Chapter: Regularization, Lasso Regression, Ridge Regression
- Overfitting, Underfitting
- Bias, Variance
- Regularization
- L1 & L2 Loss Function
- Lasso and Ridge Regression
Chapter: Dimensionality Reduction
- Feature Selection - Forward and Backward
- Feature Extraction - PCA, LDA
Chapter: Ensemble Methods: Bagging and Boosting
- Bagging - Random Forest (Regression and Classification)
- Boosting - Gradient Boosting (Regression and Classification)
