Machine Learning with Python

machine learning using python

Despite the reality that several of them are, in truth, unfastened and open-supply. It’s despite the fact that one of the capabilities of Machine learning with Python. That makes it stand out as a programming language. Due to this that Python for ML builders are able to download its source code. Make adjustments to it or maybe distribute it. Machine learning with Python comes with an extensive series of libraries that help you to perform your tasks.

Notable collection of built in Libraries in Machine Learning with Python

Python for ML offers a large sort of in-constructed libraries that the Machine learning using Python. Improvement agencies can use for statistics manipulation, records mining, and device getting to know, along with:-

  1. NumPy — Used for scientific calculation.
  2. Scikit— For information mining and analysis which optimizes Python’s device studying usability.
  3. Panda — gives builders with excessive-overall performance structures and information. Assessment tools that help them lessen the assignment implementation time.
  4. SciPy — Used for advanced computation.
  5. Pybrain — used for system mastering.

Many human beings declare that Python is virtually simple to understand, and given the functionality and scalability it gives. Machine learning with Python, a programming language is easy to learn and use. It specializes in code readability and is a flexible and nicely-hooked up language. How difficult Python is, is based upon on you. for example, if a novice is provided with first rate examine material and a decent trainer, Python can without problems be understood. Even brilliant Python builders can teach Machine learning Python course to a beginner.

Favored-motive programming language

What it approach is that Python may be used to assemble pretty tons a few element. it’s far extremely useful for backend net improvement, synthetic Intelligence, medical Computing, and statistics evaluation. ML in Python is primarily be used for internet development, tool operations, Server & Administrative equipment, medical modeling and also can be utilized by several builders to build productiveness equipment, pc apps, and video games.

Easy to integrate

Machine learning with Python is getting as an integration language in lots of locations, to stick the existing components together. Python is easy to combine with different lower-degree languages which incorporates C, C++, or Java. Further, it is easy to include a Python based definitely-stack with statistics scientist’s art work, which lets in it to convey overall performance into production.

Easy to create prototypes

As we already understand that ML in Python is easy to look at and has the functionality to boom web sites brief. Python requires an awful lot much less coding, this means that that that you are capable of create prototypes. And take a look at your thoughts speedy and effects in Python. In comparison to numerous distinctive programming languages. Growing prototypes saves builders’ time and decreases your organization’s generic expenditure as well.

A remarkable library environment is one of the number one reasons why Python is preferred for gadget mastering. Tool gaining knowledge of requires non-stop records processing. And an amazing way to make that powerful. Python’s libraries can allow you to get entry to, cope with and transform data.

List of important libraries in Machine learning with Python

  1. Scikit-analyze — Used for dealing with simple machine learning python course, getting to know algorithms like clustering, regression, linear and logistic regressions, category, and so forth.
  2. Pandas — Used for immoderate-diploma data systems and assessment.
  3. TensorFlow — it really works with Deep learning by installing region, schooling, and employing artificial neural networks with large datasets.
  4. Keras — Used for deep getting to know. It permits speedy calculations and prototyping, because it also makes use of the GPU apart from the CPU of the computer.
  5. Matplotlib — Used for developing second plots, histograms, charts, and so forth.
  6. Scikit-photograph — It handles image processing.
  7. NLTK — Works with language recognition, computational linguistics, and processing.
  8. PyBrain — Used for neural networks, unsupervised and reinforcement studying.
  9. Caffe — Used for deep learning and lets in switching between CPU and GPU. And procedures 60+ million snap shots a day the use of honestly the NVIDIA K40 GPU.
  10. StatsModels — It plays statistical algorithms and information exploration.

Enroll for the Online Live Instructor Led Training

Python Fundamentals and Programming

1
What is Python?
2
Why is Python essential for ML? Versions ofPython
3
How to install Python
4
Anaconda Distribution
5
How to use Jupyter Notebooks
6
Python Scripts
7
Command Line basics
8
How to use Python with Command Line
9
What is GitHub?
10
What is versioning?
11
GitHub Fundamentals
12
Python Data Types
13
Basic Programming Concepts
14
Previous Week Review
15
Python Operators
16
Conditional Statement, Loops
17
Lists, Tuples, Dictionaries, Sets
18
Methods and Functions
19
Errors and Exception Handling
20
Object Oriented Programming in Python
21
Modules and Packages

Data Handling with NumPy and Pandas

1
Numpy overview
2
Arrays & Matrices
3
Numpy basic operations, functions
4
Numpy for Data Analysis
5
Importing Pandas
6
Pandas overview
7
Pandas Series and Data Frames
8
Dealing with missing data
9
Groupby, Merging, Concatenating and Joining
10
Data Input & Output

Data Visualization with MatplotLib

1
Why visualize data?
2
Importing MatplotLib
3
Chart:LineChart, BarChartsand PieCharts
4
Plotting from Pandas object
5
Object Oriented Plotting: Setting axes limits and ticks
6
Multiple Plots
7
Plot Formatting: Custom Lines, Markers, Labels,Annotations, Colours

Advanced Data Visualisation with Seaborn

1
Importing Seaborn
2
Seaborn overview
3
Distribution and Categorical Plotting
4
Matrix plots & Grids
5
Regression Plots
6
Style & Colour

Introduction to Statistics for ML

1
Applied statistics in business
2
Descriptive Statistics
3
Inferential Statistics
4
Statistics Terms and definitions
5
Types of Data
6
Data Measurement Scales

Sampling methods in Statistics

1
Sampling Data, with and without replacement
2
Sampling Methods, Random vs Non-Random
3
Measurement on Samples
4
Random Sampling methods
5
Simple random, Stratified, Cluster, Systematic sampling
6
Biased vs unbiased sampling
7
Sampling Error
8
Data Collection methods

Exploratory Analysis and Distributions

1
Measures of Central Tendencies
2
Mean, Median and Mode
3
Data Variability: Range, Quartiles, Standard Deviation
4
Calculating Standard Deviation
5
Z-Score/Standard Score
6
Empirical Rule
7
Calculating Percentiles
8
Outliers
9
Distributions Introduction
10
Normal Distribution
11
Central Limit Theorem
12
Histogram – Normalization
13
Other Distributions: Poisson, Binomial et.,
14
Normality Testing
15
Skewness
16
Kurtosis
17
Measure of Distance
18
Euclidean, Manhattan and Murkowski Distance

Advanced Statistics for Data Statistics for Data

1
Hypothesis Testing
2
Null Hypothesis, P-Value
3
Need for Hypothesis Testing in Business
4
Two tailed, Left tailed & Right tailedtest
5
Hypothesis Testing Outcomes: Type I & II errors
6
Parametric vs Non-Parametric Testing
7
Parametric Tests, T-Tests: One sample, two sample, Paired
8
One Way ANOVA
9
Importance of Parametric Tests
10
Non-Parametric Tests: Chi-Square, Mann-Whitney, Kruskal- Wallis etc.,
11
Which Test to Choose?
12
Asserting accuracy of Data
13
Correlation & Regression

Machine Learning (ML) Fundamentals

1
Machine Learning Introduction
2
Applications of Machine Learning
3
Machine Learning vs Deep Learning vs Artificial Intelligence
4
Languages and platforms
5
Machining learning Tools
6
Linear Regression

ML Regression and Classification Algorithms

1
Logistic Regression
2
K- Nearest Neighbours
3
SVM Classifiers
4
K-Means Clustering
5
Principle Component Analysis

ML Advanced Algorithms & Techniques

1
Multiple Linear Regression, Polynomial Regression
2
Trees & Forest Classifiers
3
XG Boosting

Practice Sessions

1
Multiple Projects on Basic Machine Learning 1
2
Multiple Projects on Basic Machine Learning2
3
Multiple Projects on Advanced Machine Learning1
4
Multiple Projects on Advanced Machine Learning 2

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Duration: 40 Hours
Lectures: 107
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