Data Science & Machine Learning Foundation Training

Data Science & Machine Learning Foundation Course:
Machine Learning is one the hottest technology trending these days. This course covers every aspect of machine learning from thinking, development & deployment. More than 10 projects of different domains are covered. It uses scikit-learn for machine learning & pandas for data wrangling. It's a very hands-on course. Code is public & have around 500 stars. The use cases are from different companies.

Data Science & Machine Learning Foundation Course Curriculum

1. Black Box Introduction to Machine Learning

What is not Machine Learning

What is Machine Learning

Types of ML - Supervised, Unsupervised

Supervised - Classification, Regression

Unsupervised - Clustering, Association

Machine Learning Pipeline

2. Essential NumPy

Introduction to NumPy

Creation

Access

Stacking and Splitting

Methods

Broadcasting

3. Pandas for Machine Learning

Introduction to Pandas

Understanding Series & DataFrames

Loading CSV,JSON

Connecting databases

Descriptive Statistics

Accessing subsets of data - Rows, Columns, Filters

Handling Missing Data

Dropping rows & columns

Handling Duplicates

Function Application - map, apply, groupby, rolling, str

Merge, Join & Concatenate

Pivot-tables

Normalizing JSON

Application - EDA on Employee data, sales data

4. Essential Maths & Statistics

Essential Linear Algebra

Probability Concepts

Calculus

Understanding distributions

Mean, Median, Mode, Quantile

Other statistics Concepts

5. Understanding Visualization

Introduction to matplotlib, plotly, bokeh, tablue

Basic Plotting

Title, Labels, Legends, Grid, colormap, xticks, yticks

Color, linewidth

Sub Plotting

Scatterplot

Histogram

Bar Graphs

Plotting distributions

Plotting 3D data

6. Linear Models for Classification & Regression

Simple Linear Regression using Ordinary Least Squares

Gradient Descent Algorithm

Regularized Regression Methods - Ridge, Lasso, ElasticNet

Logistic Regression for Classification

OnLine Learning Methods - Stochastic Gradient Descent & Passive Aggrasive

Robust Regression - Dealing with outliers & Model errors

Polynomial Regression

Bias-Variance Tradeoff

Application - House Price, Cancer Prediction, Insurance Prediction

7. Preprocessing for Machine Learning

Introduction to Preprocessing

StandardScaler

MinMaxScaler

RobustScaler

Normalization

Binarization

Encoding Categorical (Ordinal & Nominal) Features

Imputation

Polynomial Features

Custom Transformer

Text Processing

CountVectorizer

TfIdf

HashingVectorizer

Image using skimage

8. Decision Trees

Introduction to Decision Trees

The Decision Tree Algorithms

Decision Tree for Classification

Decision Tree for Regression

Advantages & Limitations of Decision Trees

Application - Clothe Prediction

9. Naive Bayes

Introduction Bayes' Theorm

Naive Bayes Classifier

Gaussian Naive Bayes

Multinomial Naive Bayes

Burnolis' Naive Bayes

Naive Bayes for out-of-core

Application - Text Classification

10. Composite Estimators using Pipelines & FeatureUnions

Introduction to Composite Estimators

Pipelines

TransformedTargetRegressor

FeatureUnions

ColumnTransformer

GridSearch on pipeline

Application - Author classification

11. Model Selection & Evaluation

Cross Validation

Hyperparameter Tuning

Model Evaluation

Model Persistance

Validation Curves

Learning Curves

12. Feature Selection & Dimensionality Reduction

Introduction to Feature Selection

VarianceThreshold

Chi-squared stats

ANOVA using f_classif

Univariate Linear Regression Tests using f_regression

F-score vs Mutual Information

Mutual Information for discrete value

Mutual Information for continues value

SelectKBest

SelectPercentile

SelectFromModel

Recursive Feature Elemination

PCA

SVD

Application - Credit Risk Prediction

13. Nearest Neighbors

Fundamentals of Nearest Neighbor Algorithm

Unspervised Nearest Neighbors

Nearest Neighbors for Classification

Nearest Neighbors for Regression

Nearest Centroid Classifier

Application -

14. Clustering Techniques

Introduction to Unsupervised Learning

Clustering

Similarity or Distance Calculation

Clustering as an Optimization Function

Types of Clustering Methods

Partitioning Clustering - KMeans & Meanshift

Hierarchial Clustering - Agglomerative

Density Based Clustering - DBSCAN

Measuring Performance of Clusters

Comparing all clustering methods

Application - Grouping similar customers

15. Anomaly Detection

What are Outliers ?

Statistical Methods for Univariate Data

Using Gaussian Mixture Models

Fitting an elliptic envelope

Isolation Forest

Local Outlier Factor

Using clustering method like DBSCAN

16. Support Vector Machines

Introduction to Support Vector Machines

Maximal Margin Classifier

Soft Margin Classifier

SVM Algorithm for Classification

SVM for Regression

Hyper-parameters in SVM

Application - Face recognition

17. Dealing with Imbalanced Classes

What are imbalanced classes & their impact ?

OverSampling

UnderSampling

Connecting Sampler to pipelines

Making classification algorithm aware of Imbalance

Anomaly Detection

Application - Fraud detection

18. Ensemble Methods

Introduction to Ensemble Methods

RandomForest

AdaBoost

GradientBoostingTree

VotingClassifier

Application - Malicious data detection

19. Recommendation Engine

Understanding distance vector calculation - cosine, euclidean, manhatten

Types of Recommendation Engines

Recommendation based on similarity

Application - Grouping videos based on description

20. Projects

Credit Risk Prediction

Insurance Amount Prediction

Face Generation

Text Classification

Network Spam detection

Horror Novel author Prediction

Cloth Type Prediction

Employee Attrition Prediction

Customer Churn Prediction

Video recommandation based on description

Frequently Asked Questions

What are the modes of training for "Data Science & Machine Learning Foundation" course?

This "Data Science & Machine Learning Foundation" course is an instructor-led training (ILT). The trainer travels to your office location and delivers the training within your office premises. If you need training space for the training we can provide a fully-equipped lab with all the required facilities. The online instructor-led training is also available if required. Online training is live and the instructor's screen will be visible and voice will be audible. Participants screen will also be visible and participants can ask queries during the live session.

Will I be provided with any study material during the "Data Science & Machine Learning Foundation" training?

Participants will be provided "Data Science & Machine Learning Foundation"-specific study material. Participants will have lifetime access to all the code and resources needed for this "Data Science & Machine Learning Foundation". Our public GitHub repository and the study material will also be shared with the participants.

What is the pedagogy of zekeLabs?

All the courses from zekeLabs are hands-on courses. The code/document used in the class will be provided to the participants. Cloud-lab and Virtual Machines are provided to every participant during the "Data Science & Machine Learning Foundation" training.

What is the duration of this course?

The "Data Science & Machine Learning Foundation" training varies several factors. Including the prior knowledge of the team on the subject, the objective of the team learning from the program, customization in the course is needed among others. Contact us to know more about "Data Science & Machine Learning Foundation" course duration.

What would be the venue for the "Data Science & Machine Learning Foundation" training?

The "Data Science & Machine Learning Foundation" training is organised at the client's premises. We have delivered and continue to deliver "Data Science & Machine Learning Foundation" training in India, USA, Singapore, Hong Kong, and Indonesia. We also have state-of-art training facilities based on client requirement.

Who is the trainer for "Data Science & Machine Learning Foundation" training?

Our Subject matter experts (SMEs) have more than ten years of industry experience. This ensures that the learning program is a 360-degree holistic knowledge and learning experience. The course program has been designed in close collaboration with the experts working in esteemed organizations such as Google, Microsoft, Amazon, and similar others.

Can we customize this course based on our requirements?

Yes, absolutely. For every training, we conduct a technical call with our Subject Matter Expert (SME) and the technical lead of the team that undergoes training. The course is tailored based on the current expertise of the participants, objectives of the team undergoing the training program and short term and long term objectives of the organisation.

How can I reach out to you if I have any other queries regarding the "Data Science & Machine Learning Foundation" course?

Drop a mail to us at [email protected] or call us at +91 8041690175 and we will get back to you at the earliest for your queries on "Data Science & Machine Learning Foundation" course.