Machine learning Training Course

What is Machine learning ?

Machine Learning is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed. So instead of you writing the code, what you do is you feed data to the generic algorithm, and the algorithm/ machine builds the logic based on the given data.

What does it do?

It enables the computers or the machines to make data-driven decisions rather than being explicitly programmed for carrying out a certain task. These programs or algorithms are designed in a way that they learn and improve over time when are exposed to new data.

How does Machine Learning Work?

Machine Learning algorithm is trained using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction on the basis of the model.
The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again and again with an augmented training data set.

Machine Learning Syllabus Curriculum

Introduction to ML Model.
Data Handling
Data Pre-processing
Types of ML Model.
Supervised and Unsupervised.
How to test your Data?
Cross validation techniques

What is Linear Regression?
Gradient Descent overview.
Gradient Descent Calculations.
R and Python Overview.
How to improve your model?

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?

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

Distance based model with kNN.
Value of k - overview.

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

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.

Clustering overview.
k-means Clustering.
Hierarchical clustering.

Principal Component Analysis(PCA).
Maths behind PCA.
Engine Recommendation.
Content and Collaborative Filtering.
Market Basket Analysis
What is Apriori Rule?

Image Detection, Image Classification, Localization.
Convolutional Neural Networks(CNN) overview.
Strides, Padding methods
Convolutional, Padding and Fully Connected layers
Sliding Window
Edge Detection

YOLO ALgorithm - You Only Look Once
Introduction to classical networks like LeNet5
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