Data Science & Machine Learning Foundation Training in Bangalore - ZekeLabs Best Data Science & Machine Learning Foundation Training Institute in Bangalore India
Data Science & Machine Learning Foundation-training-in-bangalore-by-zekelabs

Data Science & Machine Learning Foundation Training

Data Science & Machine Learning Foundation Course: This course covers the concepts and tools you'll need throughout the entire data science pipeline and goes on to introduce to concepts of Machine Learning. It starts with getting to know about NumPy, pandas, and other python libraries for data-crunching, and matPlotLib for data visualization. After understanding it you will be ready to start Machine Learning. Machine Learning foundation will include the scikit-Learn library and various approaches to solve real-life problems using Machine Learning. The concepts and techniques that you learn here will help you right from asking the right kinds of questions to making inferences and publishing results. In the final capstone project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
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Assignments
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Industry Level Projects
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Certification

Data Science & Machine Learning Foundation Course Curriculum



NumPy Standard Data Types
NumPy Array Attributes
Array Slicing: Accessing Subarrays
Array Concatenation and Splitting
The Slowness of Loops
Exploring NumPy's UFuncs
Ufuncs: Learning More
Summing the Values in an Array
Computation on Arrays: Broadcasting
Rules of Broadcasting
Comparisons, Masks, and Boolean Logic
Working with Boolean Arrays
Fancy Indexing
Combined Indexing
Installing and Using Pandas
The Pandas Series Object
The Pandas Index Object
Data Selection in Series
Operating on Data in Pandas
UFuncs: Index Alignment
Handling Missing Data
Missing Data in Pandas
Hierarchical Indexing
Methods of MultiIndex Creation
Rearranging Multi-Indices
Combining Datasets: Concat and Append
Simple Concatenation with pd.concat
Relational Algebra
Specification of the Merge Key
Overlapping Column Names: The suffixes Keyword
Simple Aggregation in Pandas
Pivot Tables
Pivot Tables by Hand
Example: Birthrate Data
Introducing Pandas String Operations
Working with Time Series
Pandas Time Series: Indexing by Time
Frequencies and Offsets
General Matplotlib Tips
Setting Styles
Saving Figures to File
Simple Line Plots
Adjusting the Plot: Axes Limits
Simple Scatter Plots
Scatter Plots with plt.scatter
Visualizing Errors
Continuous Errors
Visualizing a Three-Dimensional Function
Two-Dimensional Histograms and Binnings
Choosing Elements for the Legend
Multiple Legends
Customizing Colorbars
Multiple Subplots
plt.subplot: Simple Grids of Subplots
plt.GridSpec: More Complicated Arrangements
Example: Effect of Holidays on US Births
Arrows and Annotation
Major and Minor Ticks
Reducing or Increasing the Number of Ticks
What Is Machine Learning?
Supervised Machine Learning
Examples of products using machine learning
Data Representation in Scikit-Learn
Feature data, Target data, Dependent variable
Thinking About Model Validation
Selecting the Best Model
Grid Search
Categorical Features
Image Features
Imputation of Missing Data
Using House Price Prediction
Polynomial Linear Regression
Understaning linear regression using matrix
Using Iris datset to understand logistic regression
Cost Function & Mathematical Foundation
Bayesian Classification
Multinomial Naive Bayes
Application : Identify category from text
Introduction
Elbow rule to decide number of clusters
Application : Detection of number of characters in arabic
Understaning Decision trees
Motivating Random Forests: Decision Trees
Random Forest Regression
Other Boosting techniques - AdaBoost, Gradient Tree Boosting
Nearest Neighbours
Elbow rule to decide number of clusters
Application : Detection of number of characters in arabic
Understanding BeautifulSoup
Extracting the nearest neighbors
Computing similarity scores
Building a movie recommendation system
Handwriting Detection
Image Identification

Frequently Asked Questions


We have options for classroom-based as well as instructor led live online training. The online training is live and the instructors screen will be visible and voice will be audible. Your screen will also be visible and you can ask queries during the live session.

The training on "Data Science & Machine Learning Foundation" course is a hands-on training. All the code and exercises will be done in the live sessions. Our batch sizes are generally small so that personalized attention can be given to each and every learner.

We will provide course-specific study material as the course progresses. You will have lifetime access to all the code and basic settings needed for this "Data Science & Machine Learning Foundation" through our GitHub account and the study material that we share with you. You can use that for quick reference

Feel free to drop a mail to us at info@zekelabs.com and we will get back to you at the earliest for your queries on "Data Science & Machine Learning Foundation" course.

We have tie-ups with a number of hiring partners and and placement assistance companies to whom we connect our learners. Each "Data Science & Machine Learning Foundation" course ends with career consulting and guidance on interview preparation.

Minimum 2-3 projects of industry standards on "Data Science & Machine Learning Foundation" will be provided.

Yes, we provide course completion certificate to all students. Each "Data Science & Machine Learning Foundation" training ends with training and project completion certificate.

You can pay by card (debit/credit), cash, cheque and net-banking. You can also pay in easy installments. You can reach out to us for more information.

We take pride in providing post-training career consulting for "Data Science & Machine Learning Foundation".



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