Course Details

Debuggers Machine Learning development course content is based upon the industry recommendations and is regularly updated to keep pace with the ongoing technological advancements. It is one of the best & reliable Machine Learning institutes that offers basic as well as an advanced level of Machine Learning development training in Lucknow. What you'll Learn

• Module1:- Introduction to Machine Learning- Need of Machine Learning
• Module2:- Introduction to Python- Core Python Concept
• Module3:- Basics of Python(Variables, Data Types, Operators)
• Module4:- Working on conditional statement.
• Module5:- Working on if, if-else, Nested if-else.
• Module6:- Implementing looping- For, While, Nested Loop
• Module7:- Working on control statement- Break, Continue
• Module8:- Sting Manipulation, Accessing, Basic Operations, String Slices.
• Module9:- Implementing Functions.
• Module10:- Working on List-Accessing, operational, Function and Methods.
• Module11:- Working on Tuple- Accessing, operational, Function and Methods
• Module12:-Working on Dictionaries- Accessing, operational, Properties, Function and Methods.
• Module13:- Implementation of Function- Definition, Calling a Function, Type of Functions.
• Module14:- Function Arguments, Anonymous Functions, Global and Local Variables.
• Module15:- Working on Modules- importing Modules, Math Module, Random Module, Packages and Compositions.
• Module16:- Working on Input/output Operations- Printing on Screen, Reading data from Keyboard.
• Module17:-Implementation of input/output Operations- Opening and closing Files, Reading and Writing Files, Functions
• Module18:- Implementations on Exception Handling
• Module19:- Implementation on OOPs Concepts- Object, class, attributes, inheritance, overloading, overriding, Data Hiding.
• Module20:- Introduction to Framework- Numpy, Scipy, Pandas, Matplotlip, Tensorflow, Keras
• Module21:- Understating Data Processing- importing libraries and data sets
• Module22:- Working on Missing data, categorial Data, Splitting data, Feature Scaling.
• Module23:- Creating data processing Template
• Module24:- Understating Regression- Simple Linear Regression, Multiple Linear Regression
• Module25:- Understating Polynomial regression, support vector regression, decision tree regression
• Module26:- Understating Classification of Logistic regression, K-nearest Neighbors, Support vector Machine
• Module27:- Understating Kernel SVM, Naive Bays, decision tree Classifier, Evaluating Classification Model Performance.
• Module28:- Understating Perception, feature expansion, method and Kernel
• Module29:- Working on Clustering (K-means , Hierarchical, Recommender)
• Module30:- Understating Association Rule Learning (Apriori) 
• Module31:- Understating Reinforcement learning(Upper Confidence Bound, Thompson Sampling, Markov Decision Process)
• Module32:- Understating Natural Language Processing- tokenization, Word Embedding, Sequence Model
• Module33:- Understating Deep Learning- Artificial Neural Network, Convolutional Neural Network, Transfer learning, multiclass classification, deep Convolution Model
• Module34:- Understating Dimensionality reduction- principle component analysis, linear discrimination analysis, kernel PCA
•  Module35:- Understating Model Selection and boosting- XGBoost Real Time Latest Project