About Machine Learning Training

ScimoX provides best industry relevant content with systematic delivery on the most popular computer algorithms Machine Learning. Machine Learning is simply making a computer perform a task without explicitly programming it.
Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. If launching a career in Machine Learning sounds right up your alley, Scimox Machine Learning Certification Training will definitely help you comprehend and master concepts like regression, clustering, classification, and prediction. Take Scimox Machine Learning course and become an expert!

Course content:

The Machine Learning Advanced Certification Training will help you master Machine Learning using Python language by identifying basic theoretical principles, algorithms, and applications of Machine Learning.

This Machine Learning course will also help you understand the connections between theory and practice in Machine Learning and help you master topics like Reinforcement Learning, Deep Learning, advanced techniques like Dimensionality Reduction, Support Vector Machines, etc to prepare you for the role of Machine Learning Engineer.

Course Objective of Machine Learning with Python Training

The learning objectives of python course in ScimoX :
1.Internalize the concepts & constructs of machine learning techniques
2.Learn to create your own Modeling on Machine Learning
3.Master In Python Basic & Advanced Project
4.Master In Conceptualization, summarization and Representation of data
3. Work on Big data computing environment, Modern data analytics technologies like Hadoop and MapReduce.
4. Work on Statistics, Workflow of R tool, Data mining, reporting/visualization, fundamental of SQL, classified algorithms, supervised, unsupervised machine learning algorithms and lot more
5.100% Interview Guarantee
6.After Placement Career Mentoring

Machine Learning Syllabus

Basic
Python language introduction, Python 3 basics, Keywords in Python, Namespaces and Scope in Python, Structuring Python Programs, Decision making

Input/Output
Taking input in Python, Vulnerability in input() function, Python | end parameter in print(), Python | sep parameter in print()

Data Types
Introduction to DataTypes, Strings, List, Tuples, Sets, Dictionary,

Variables
Variables, expression condition and functionMaximum possible value of an integer in python, Global and local variables in python Packing and unpacking arguments in python, End parameter in Python

Control Flow
Loops, Loops and Control Statements (continue, break and pass) in Python, Looping technique in python, range vs xrange on python, Programs for printing pyramid technique in python Chaining comparison in python, else with for switch function, Using iteration in python effectively Iterators in Python, Iterators function in python Iterators function Python __iter__() and __next__() | Converting an object into an iterator

Modules and Packages:
Modules , Importing module, Standard Module – sys, Standard Module – OS, The dir Function, Package Exercise

Exception Handling:
Errors, Run Time Errors, Handling IO Exceptions, Try.... except statement, Raise, Assert, Exercise

Files and Directories: Introduction, Writing Data to a File, Reading Data From a File, Additional File Methods, Working with files, Working with Directories, The pickle Module, Exercise

Classes Objects:
Introduction classes and objects, Creating Classes, Instance Methods, Special class method, Inheritance, Method overriding, Data handling Exercise

Getting started with pandas
Data Frame Basics, Key Operations on Data Frames. function, Matplotlib

Plotting for exploratory data analysis (EDA)
Data-point, vector, observation, Input variables/features/dimensions/independent variable , Output Variable/Class Label/ Response Label/ , Scatter-plot: 2D, 3D.

Advance Library:
Multi threading , Thread, Starting a thread , Threading module, , array, numpy, , Tkinter programming Tkinter widget

Probability and Statistics
Introduction to Probability and Stats, Gaussian/Normal Distribution, Uniform Distribution and random number generators , Bernoulli and Binomial distribution , Hypothesis testing

Linear Algebra
Point/Vector (2-D, 3-D, n-D) , Dot product and angle between 2 vectors, Projection, unit vector , Equation of a line (2-D), plane(3-D) and hyperplane (n-D) , Square, Rectangle, Hyper-cube and Hyper-cuboid.

Dimensionality reduction and Visualization
What is dimensionality reduction?, . Data representation and pre-processing , MNIST dataset (784 dimensional) , Principal Component Analysis, T-distributed stochastic neighborhood embedding (t-SNE) .
Real world problem: Predict sentiment polarity given product reviews on Amazon
Dataset overview: Amazon Fine Food reviews , Data Cleaning: Deduplication, Featurizations: convert text to numeric vectors, Code samples, Exercise: t-SNE visualization of Amazon reviews with polarity based color-coding .

Real world problem: Predict sentiment polarity given product reviews on Amazon
Foundations, K-Nearest Neighbors

Classification algorithms in various situations
Imbalanced vs balanced dataset, Multi-class classification, k-NN, given a distance or similarity matrix, Train and test set differences, Impact of Outliers, Local Outlier Factor, Impact of Scale & Column standardization

Performance measurement of models
Accuracy, Confusion matrix, TPR, FPR, FNR, TNR , Precision & recall, F1-score, Receiver Operating Characteristic Curve (ROC) curve and AUC, Log-loss
Naive Bayes
Conditional probability, Independent vs Mutually exclusive events, Bayes Theorem with examples, Exercise problems on Bayes Theorem, Naive Bayes algorithm
Logistic Regression
Geometric intuition, Sigmoid function & Squashing , Optimization problem, Weight vector, L2 Regularization: Overfitting and Underfitting, L1 regularization and sparsity, Probabilistic Interpretation: Gaussian NaiveBayes, Loss minimization interpretation

Linear Regression and Optimization
Geometric intuition, Mathematical formulation, Cases

Support Vector Machines (SVM)
Geometric intuition, Mathematical derivation, Loss minimization: Hinge Loss, Dual form of SVM formulation, Kernel trick, Polynomial kernel, Train and run time complexities

Decision Trees
Geometric Intuition: Axis parallel hyperplanes, Sample Decision tree, Building a decision Tree:, Exercise: Decision Trees on Amazon reviews dataset

Ensemble Models
What are ensembles?, Bootstrapped Aggregation (Bagging) , Boosting, Stacking models, Cascading classifiers, Kaggle competitions vs Real world

Featurizations and Feature engineering
Time-series data, mage data, Relational data, Graph data, Feature Engineering, Model specific featurizations, Feature orthogonality

Hierarchical clustering
Agglomerative & Divisive, Dendrograms , Proximity methods: Advantages and Limitations, Time and Space Complexity, Code sample, Exercise: Amazon food reviews

DBSCAN (Density based clustering)
Density based clustering , MinPts and Eps: Density, Core, Border and Noise points, Density edge and Density connected points, DBSCAN Algorithm

Recommender Systems and Matrix Factorization
Problem formulation: Movie reviews, Content based vs Collaborative Filtering, Similarity based Algorithms, Matrix Factorization, Hyperparameter tuning, Matrix Factorization for recommender systems: Netflix Prize Solution, Cold Start problem, Word Vectors using MF, Eigen-Faces.

Deep Learning: Neural Networks
History of Neural networks and Deep Learning., How Biological Neurons work?, Diagrammatic representation: Logistic Regression and Perceptron, Multi-Layered Perceptron , Training a single-neuron model, Training an MLP: Chain rule

Deep Learning: Deep Multi-layer perceptrons
1980s to 2010s , Dropout layers & Regularization, Rectified Linear Units (ReLU), Weight initialization, Batch Normalization, Gradient monitoring and Clipping, Softmax for multi-class classification, How to train a Deep MLP? , Auto Encoders

Deep Learning: Tensorflow and Keras.
Overview, GPU vs CPU for Deep Learning, Google Colaboratory, TensorFlow, MNIST classification in Keras, Hyperparameter tuning in Keras

Deep Learning: Convolutional Neural Nets
Biological inspiration: Visual Cortex , Convolution, Convolutional layer, Max-pooling, CNN Training: Optimization, ImageNet dataset , Data Augmentation, Convolution Layers in Keras , AlexNet , VGGNet , Residual Network

Deep Learning:Recurrent Neural Networks
Why RNNs, Recurrent Neural Network, Training RNNs: Backprop, Types of RNNs, Need for LSTM/GRU, GRUs, LSTM, Deep RNN, Bidirectional RNN

Fee Structure & Course Duration

• Course :- Machine Learning
• Level: Master Certification
• Batch: Weekends, Weekdays
• Format: Class Room Training
• Course Fee :-(Machine Learning With Python ): Rs.23000 /-Machine Learning: Rs .19000 /
• Course Duration : 3 Month
• Training Location:Hazaratnagar Branch, Lucknow

Contact Person

HR Counsellor

Mr. Deepak Kanuajia
Email :- Deepak@scimox.com
Contact No. :- (+91) - 7011907181