SECTION 1: INTRODUCTION TO ML
What is ML?
Why ML?
Opportunities in ML
What is ML models?
Why R and Python is popular?
SECTION 2: ML MODEL OVERVIEW
Introduction to ML Model.
Data Handling
Data Pre-processing
Types of ML Model.
Supervised and Unsupervised
How to test your Data?
Cross validation techniques
SECTION 3: LINEAR REGRESSION
What is Linear Regression?
Gradient Descent overview.
Gradient Descent Calculations
R and Python Overview.
How to improve your model?
SECTION 4: OVERFITTING
Overfitting Overview
How to use Linear Regression for Overfitting?
How to avoid Overfitting?
Bias-Variance Tradeoff.
Regularization - Ridge, LASSO
ANOVA, F tests overview.
What is Logistic Regression?
Classification with Logistic Regression.
Maximum Likelihood Estimation.
Build an end to end model with Logistic Regression using scikit Learn.
How to build a model in the Industry?
SECTION 5: DECISION TREES
Why Decision Tree?
Entropy, Gini Impurity overview
Implement Overfitting.
How to improve the Decision Tree model without Overfitting?
Bagging, Boosting
Random Forest
AdaBoost, Gradient Boost
SECTION 6: K-NN
Distance based model with kNN.
Value of k - overview.
SECTION 7: SUPPORT VECTOR MACHINES(SVM)
Power of SVM overview.
Why SVM?
What is Kernel Functions?
What are the Kernel Functions available?
How to Build an OCR(Optical Character Reader) with the help of SVM and Kernel functions?
Neural Networks overview.
Why Neural Networks?
What is Neural Network Architecture?
How to build AND, OR, NOT, XOR, XNOR Logic Gates with Neural Network?
What is Forward & Backward Propagation?
List of Activation Functions.
Vanishing Gradient problem
SECTION 8: DEEP NEURAL NETWORKS
Optimization methods overview.
Gradient Descent with Momentum, RMSProp, ADAM.
Learning Rate Decay.
Xavier Initialization.
Introduction to Keras and Tensorflow(TF)
Deep Learning in Keras with TensorFlow as the backend
SECTION 9: UNSUPERVISED LEARNING
Clustering overview
k-means Clustering.
Hierarchical clustering.
SECTION 10: PCA
Principal Component Analysis(PCA).
Maths behind PCA.
Engine Recommendation.
Content and Collaborative Filtering
Market Basket Analysis
What is Apriori Rule?
SECTION 11: COMPUTER VISION
Image Detection, Image Classification, Localization.
Convolutional Neural Networks(CNN) overview.
Strides, Padding methods
Convolutional, Padding and Fully Connected layers
Sliding Window
Edge Detection
SECTION 12: ADVANCED COMPUTER VISION
YOLO ALgorithm - You Only Look Once
Introduction to classical networks like LeNet5
IoU
Introduction to Natural Language Processing(NLP)
Text Preprocessing
Lemmatization, Stemming
Syntactical Parsing, Entity Parsing
Develop a chatbot with the above concepts of NLP and Neural Networks