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Best Machine Learning Training Institute

Introduction

Join the Best Machine Learning Training Institute

Every now and then we are learning about how technology is trying hard to make the life simpler and easier for you. Do you really think so, earlier it was that easy? Well, undoubtedly the answer is a big no. Then what is it that is helping artificial intelligence to become part of human lives.

There is one thing that is the core of artificial intelligence, which is machine learning. There are only few students who are confident enough to make their career in this, while the rest look out for best machine learning training institute. Sofcon is one of the leading and premier institutes known for offering great services at affordable rates.

So, are you one of those students who are looking for machine learning training institute? Then your wait is over. Enroll yourself today, in Sofcon, where the main aim is to give world class training. We are having experienced trainers not only for this best machine learning course but also for other courses as well. The key benefits of joining Sofcon includes, you get the opportunity to choose your classes as per your needs.

We have different time slots for the batches, apart from this there are also fast track courses available. By taking admission in the fast track courses, you can reduce your time for completing the course and give more time towards the placement activities. We offer 100% job assistance to the students, the benefits of which can be availed till they are placed with a reputed organisation and drawing handsome salary package. We have world class facility with latest equipment and devices on which you can gain sufficient experience. Getting complete practical exposure is not possible in the college, but don’t worry we can help you out in the same.

Course Content

What we learn…..

MODULE 1: BASICS OF PYTHON

• Introduction of python
• Data types and operations
• Statements and syntax in python
• Introduction to python programming
• Memory management and garbage collections
• Modules and packages
• Functions in python
• File handling
• Exception handling in python programming
• Classes in python
• Regular expressions
• Data structures
• GUI programming in python
• Basics of thread in python
• Network & socket programming
• Data base access with python

MODULE 2: BASICS OF MACHINE LEARNING

• Introduction of Machine Learning
• Types of Machine Learning
• ML algorithms
• ML package :scikit-learn
• Anaconda

MODULE 3: FUNDAMENTAL OF MACHINE LEARNING

• Converting business problems to data problems
• Understanding supervised and unsupervised learning with examples
• Understanding biases associated with any machine learning algorithm
• Ways of reducing bias and increasing generalisation capabilites
• Drivers of machine learning algorithms
• Cost functions
• Brief introduction to gradient descent
• Importance of model validation
• Methods of model validation
• Cross validation & average error

MODULE 4: BASIC INTRODUCTION NUMPY

• Introduction to NumPy
• Creating an array
• Class and Attributes of array
• Basic Operations
• Activity-Slice
• Stack operations
• Mathematical Functions of NumPy
• Introduction to Pandas
• Understanding Data Frame
• Series
• Concatenating and appending Data Frames
• loc and iloc
• Drop columns or rows
• Group by
• Map and apply
• Mathematical computing with Python

MODULE 5: DATA MANIPULATION WITH PANDAS

• Introduction of Pandas
• Data Types in Pandas
• Understanding Series
• Understanding Data Frame
• View and Select Data Demo
• Missing Values
• Data Operations
• File Read and Write Support
• Pandas Sql Operation

MODULE 6: DATA PREPROCESSING

• Introduction of data preprocessing
• Dealing with missing data
• Handling categorical data
• Encoding class labels
• One-hot-encoding
• Split data into training and testing sets
• Bringing Features onto same scale

MODULE 7: LINEAR REGRESSION

• Introduction of Linear Regression
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Evaluate Performance of a linear regression model
• Overfitting and underfitting

MODULE 8: LOGISTIC REGRESSION

• Concept of Logistic Regression
• Implementing Logistic regression with scikit-learn
• Logistic Regression Parameters
• Multi-class classification
• MNIST digit dataset with Logistic Regression
• Predictive modeling on adult income dataset

MODULE 9: K-NEAREST NEIGHBORS (KNN)

• KNN theory
• Implementing KNN with scikit-learn
• KNN Parameters
• n_neighbors
• Metric
• finding techniques of Nearest Neighbors
• Writing Own KNN classifier from scratch

MODULE 10: K MEANS CLUSTERING

• K Means Algorithm Theory
• K Means with Python
• K Means Project Overview
• K Means Project Solutions

MODULE 11: CLUSTERING AND DIMENSION REDUCTION

• K-means Clustering
• Elbow method
• Principal components analysis(PCA)
• PCA step by step
• Implementing PCA with scikit-learn
• LDA with scikit-learn

MODULE 12: SUPPORT VECTOR MACHINE (SVM)

• SVM theory
• Implementing SVM with scikit-learn
• SVM Parameters:
• C and gamma
• Plot hyperplane for linear classification
• Decision function

MODULE 13: DECISION TREE AND RANDOM FOREST

• Basics of decision tree
• Implementing decision tree with scikit-learn
• Decision tree parameters
• Combining multiple decision trees via Random forest
• How random forest works

MODULE 14: TREE MODELS USING PYTHON

• Introduction to decision trees
• Tuning tree size with cross validation
• Introduction to bagging algorithm
• Random Forests
• Grid search and randomized grid search
• ExtraTrees (Extremely Randomised Trees)
• Partial dependence plots

MODULE 15: GENERALIZED LINEAR MODELS IN PYTHON

• Linear Regression
• Regularisation of Generalised Linear Models
• Ridge and Lasso Regression
• Logistic Regression
• Methods of threshold determination and performance measures for classification score models

MODULE 16: MODEL EVALUATION AND PARAMETER TUNING

• Cross validation via K-Fold
• Tuning hyper parameters via grid search
• Confusion matrix
• Recall and Precision
• ROC and AUC

MODULE 17: NAIVE BAYES CLASSIFICATION

• Theory Naive Bayes Algorithm
• Features extraction
• Countvectorizer
• TF-IDF
• Text Classification

MODULE 18: BASICS OF OPEN CV

• Introduction of Computer Vision & Open CV
• Images Manipulations
• Image Segmentation
• Object Detection
• Face, People and Car Detection
• Face Analysis and Fulters
• Machine Learning in Computer Vision
• Motion Analysis & Object Tracking

MODULE 19: VERSION CONTROL USING GIT AND INTERACTIVE DATA PRODUCTS

• Need and Importance of Version Control
• Setting up git and github accounts on local machine
• Creating and uploading GitHub Repos
• Push and pull requests with GitHub App
• Merging and forking projects

MODULE 20: PYTHON FOR DATA VISUALIZATION-MATPLOTLIB

• Introduction to Matplotlib
• Matplotlib Part 1 Set up
• Matplotlib Part 2 Plot
• Matplotlib Part 3 Next steps
• Matplotlib Exercises Overview
• Matplotlib Exercises – Solutions

MODULE 21: TEXT MINING IN PYTHON

• Gathering text data using web scraping with urllib
• Processing raw web data with BeautifulSoup
• Interacting with Google search using urllib with custom user agent
• Collecting twitter data with Twitter API
• Naive Bayes Algorithm
• Feature Engineering with text data
• Sentiment analysis

MODULE 22: UNSUPERVISED LEARNING IN PYTHON

• Need for dimensionality reduction
• Principal Component Analysis (PCA)
• Difference between PCAs and Latent Factors
• Factor Analysis
• Hierarchical, K-means & DBSCAN Clustering

MODULE 23: BOOSTING ALGORITHMS USING PYTHON

• Concept of weak learners
• Introduction to boosting algorithms
• Adaptive Boosting
• Extreme Gradient Boosting (XGBoost)

Course Details

Course Duration: 80 Hrs

Eligibility: Diploma/BCA/BSC/B.Tech/MCA/M.Tech/MSC

Scope:

Mode:
• 3 Months Job Oriented Training
• 2/4/6 Weeks Summer/Winter Training
• 6 Months Internship Training
• Fast Track Training
• Customize Training
• College/Corporate Training
• Week Days/Regular Training
• Weekend Training

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