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Machine Learning Using Python

Teacher
Coursack
Category:
Data Science, 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

₹26,500

$480

₹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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