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Machine Learning using Python

Teacher
Coursack
Category:
Data Science, Machine Learning using Python
machine learning using python

Machine Learning Using Python

Coursack’s Machine Learning with Python certification course dives into the fundamental concepts of machine learning using Python. A well-known and approachable programming language. You’ll study supervised vs unsupervised learning. Check out how statistical modelling relates to machine learning, and make a comparison of each.

Why Learn Machine Learning Using Python?

Data Science may be a set of techniques that permits the computers to find out the specified behaviour from data without explicitly being programmed. Along the way, you’ll check out real-life samples of machine learning and see how it affects society in ways you’ll not have guessed!

1 to 1 Instructor led Training

Group Classes

Corporate Training

Student Discount

  • 35+ Hours of Training.

  • Class for Individual Students

  • Labs and Assignments

  • Class Recording is provided

  • 35+ Hours of Training.

  • Training is provided to group of students

  • Labs and Assignments

  • Class Recording is provided

Looking for corporate training ? Contact us for group discount 

  • 35+ Hours of Training.

  • Training is provided to group of students

  • Labs and Assignments

  • Class Recording is provided

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₹21,750

$375

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Most importantly, by Coursack’s ML using Python certification course you will transform your theoretical knowledge into practical skills using hands-on experience labs. Enroll right now and start to prepare more learning than your machine!

Who Are The Targeted Audience?

  • Developers aspiring to be a Machine Learning Engineer
  • Python professionals who wanted to design automatic predictive models
  • Business Analysts who want to know Machine Learning (ML) Techniques
  • Analytics Managers who wanted to lead a team of analysts
  • Information Architects who want to realize expertise in Predictive Analytics

1. Predictive Modeling Basics

1
– Introduction to Machine Learning & Predictive Modeling
2
– Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
3
– Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
4
– Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
5
– Overfitting (Bias-Variance Trade off) & Performance Metrics
6
– Feature engineering & dimension reduction
7
– Concept of optimization & cost function
8
– Overview of gradient descent algorithm
9
– Overview of Cross validation (Bootstrapping, K-Fold validation)
10
– Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)

2. Unsupervised Learning: Segmentation

1
– What is segmentation & Role of ML in Segmentation?
2
– Concept of Distance and related math background
3
– K-Means Clustering
4
– Expectation Maximization
5
– Hierarchical Clustering
6
– Spectral Clustering (DBSCAN)
7
– Principle component Analysis (PCA)

3. Supervised learning: Decision Tree

1
– Decision Trees – Introduction – Applications
2
– Types of Decision Tree Algorithms
3
– Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
4
– Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
5
– Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
6
– Decision Trees – Validation
7
– Overfitting – Best Practices to avoid
8
– Case Study on Decision Tree

4. Supervised Learning: Ensemble Learning

1
– Concept of Ensemble
2
– Manual Ensemble Vs. Automated Ensemble
3
– Methods of Ensemble (Stacking, Mixture of Experts)
4
– Bagging (Logic, Practical Applications)
5
– Random forest (Logic, Practical Applications)
6
– Boosting (Logic, Practical Applications)
7
– Ada Boost
8
– Gradient Boosting Machines (GBM)
9
– XGBoost
10
– Case Study on Random Forest

5. Supervised Learning: Artificial Neural Networks (ANN)

1
– Motivation for Neural Networks and Its Applications
2
– Perceptron and Single Layer Neural Network, and Hand Calculations
3
– Learning in a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
4
– Neural Networks for Regression
5
– Neural Networks for Classification
6
– Interpretation of Outputs and Fine tune the models with hyper parameters
7
– Validating ANN models

6. Supervised Learning: Support Vector Machines

1
– Motivation for Support Vector Machine & Applications
2
– Support Vector Regression
3
– Support vector classifier (Linear & Non-Linear)
4
– Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
5
– Interpretation of Outputs and Fine tune the models with hyper parameters
6
– Validating SVM models

7. Supervised Learning: KNN

1
– What is KNN & Applications?
2
– KNN for missing treatment
3
– KNN for solving regression problems
4
– KNN for solving classification problems
5
– Validating KNN model
6
– Model fine tuning with hyper parameters

8. Supervised Learning: Naïve Bayes

1
– Concept of Conditional Probability
2
– Bayes Theorem and Its Applications
3
– Naïve Bayes for classification
4
– Applications of Naïve Bayes in Classifications

9. Text Mining and Analytics

1
– Taming big text
2
– Unstructured vs. Semi-structured Data
3
– Fundamentals of information retrieval
4
– Properties of words
5
– Creating Term-Document (TxD)
6
– Matrices
7
– Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
8
– Finding patterns in text: text mining, text as a graph
9
– Natural Language processing (NLP)
10
– Text Analytics – Sentiment Analysis using Python
11
– Text Analytics – Word cloud analysis using Python
12
– Text Analytics – Segmentation using K-Means/Hierarchical Clustering
13
– Text Analytics – Classification (Spam/Not spam)
14
– Applications of Social Media Analytics
15
– Metrics (Measures Actions) in social media analytics
16
– Examples & Actionable Insights using Social Media Analytics
17
– Important python modules for Machine Learning (SciKit Learn, stats models, SciPy, nltk)
18
– Fine tuning the models using Hyper parameters, grid search, piping etc.
19
– Case Study on Text Analytics

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Duration: 30 Hours
Lectures: 77
Level: Beginner

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Machine Learning using Python
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