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MACHINE LEARNING WITH PYTHON ONLINE TRAINING

(5.0/5) 431+ Students Enrolled

Machine Learning training is designed to help you develop necessary skills that are vital to gain in-depth knowledge in supervised learning, unsupervised learning, machine learning algorithms, vector machines, and much more through real-time use cases and project-based learning. If you are an aspirant who wants to explore how a device can recognize patterns and messages from data, then machine learning with Python is an ideal start for you! We’ll prepare you to play with the army of powerful machine learning models to solve any problem.

Machine Learning with Python Certification Overview

The Machine Learning training by Codegnan has over 60 hours to ensure that you have the proper understanding of every concept before going to the next module. You will get acquainted with the requirement of product-based companies. The detailed overview on algorithms and techniques, such as regression, classification, time series modeling, supervised and unsupervised learning, SKLearn usage, statistical thinking, etc. Working on real-time case studies will make you understand the job aspect of a Machine Learning engineer. We assure you the great command in practicals along with theoretical concepts. So, get leveled up in Machine Learning in less investment! Your dream job is waiting for you!

Key Features

Course Feature 1

45 Hours Instructor Led Training

Course Feature 2

Self-Paced Videos

Course Feature 3

Exercises & Projects

Course Feature 4

Certification & Job Assistance

Course Feature 5

Flexible Schedule

Course Feature 6

Lifetime Access & Upgrade

Course Feature 7

24/7 Lifetime Support

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Machine Learning with Python Certification Curriculum

1. Importance of Data in 21st Century

2. Types of Data and its usage

3. Python Crash course (using IDLE)

● Data Types

● Conditional Statements

● Control Statements

● Functions

4. What is Machine Learning and types of Learning

5. Anaconda Environment Setup and its usage

1. Numpy

● Creating arrays, Difference between List and Array

● Accessing Elements, Slicing, Concatenation

● Universal Functions, Shape Manipulation

● Automatic Reshaping, Vector Stacking.

2. Pandas

● Pandas DataStructures

● Indexing, Selecting Data, Slicing functions

● Some Useful DataFrame Functions

● Handling Missing Values in DataFrame

● Time Series Analysis

3. Data Visualization Libraries

● Matplotlib

● Plotly

● Basic Plotting with Seaborn

● Projects on Data Analysis – Google Analysis, Market Analysis

4. Sklearn usage

● Playing with Scikit-learn, Understanding classes in Scikit-learn

Machine Learning Fundamentals

• What is machine learning?
• How Machine Learning works?
• Applications of machine learning
• Different types of machine learning
• How do we know machines are learning right?
• Different stages of machine learning projects.

Data Transformation and Preprocessing
• Handling Numeric Features
• Feature Scaling
• Standardization and Normalization
• Handling Categorical Features
• One Hot Encoding, pandas get_dummies
• Label Encoding
• More on different encoding techniques

Train,Test and Validation Split
• Simple Train and Test Split
• Drawbacks of train and test split
• K-fold cross validation
• Time based splitting

Overfitting And Underfitting
• What is overfitting ?
• What causes overfitting?
• What is Underfitting ?
• What causes underfitting ?
• What are bias and Variance ?
• How to overcome overfitting and underfitting problems ?

Regression
• Introduction to Linear Regression
• Understanding How Linear Regression Works
• Maths behind Linear Regression
• Ordinary Least Square
• Gradient Descent
• R - square
• Adjusted R-square
• Polynomial Regression
• Multiple Regression
• Performance Measures - MSE, RMSE, MAE
• Assumption of Linear Regression
• Ridge and Lasso regression
• RFE (Recursive Feature elimination)

Hands On - Problem formulation and Case Study on Hotstar, Netflix, And housing prices Dataset

Classification

Logistic regression

• Introduction to classification problems
• Introduction to logistic regression
• Why the name regression ?
• The sigmoid function
• Log odds
• Cost function
• Feature importance and model interpretability
• Collinearity of features
• Feature engineering for non-linearly separable data

Performance Metrics for Classification Algorithms

• Accuracy Score
• Confusion Matrix
• TPR, FPR, FNR, TNR
• Precision - Recall
• F1-Score
• ROC Curve and AUC
• Log Loss

Hands On - Real World Case Study on IBM HR Employee Attrition dataset

K Nearest Neighbors

• Introduction to KNN
• Effectiveness of KNN
• Distance Metrics
• Accuracy of KNN
• Effect of outlier on KNN
• Finding the k Value
• KNN on regression
• Where not to use KNN
Hands On - Different case study on KNN

Natural Language Processing

• Introduction to NLP
• Converting Text to vector
• Data Cleaning
• Preprocessing Text Data - Stop word removal, Stemming , Tokenization, Lemmatization
• Collecting Data from the web
• Developing a Classifier
• Building Pipelines for NLP projects
• Uni-grams,bi-grams and n-grams
• tf-idf
• Word2Vec
Hands On - Text Summarization, WebScraping for data, Sentiment Analysis, Topic Modelling, Text Summarization and Text Generation

Naive Bayes

• Refresher on conditional Probability
• Bayes Theorem
• Examples on Bayes theorem
• Exercise problems on Naive Bayes
• Naive Bayes Algorithm
• Assumptions of Naive Bayes Algorithm
• Laplace Smoothing
• Naive Bayes for Multiclass classification
• Handling numeric features using Naive Bayes
• Measuring performance of Naive Bayes
Hands On - Working on spam detection and Amazon Food Review dataset

Support Vector Machines

• Introduction to SVM
• What are hyperplanes ?
• Geometric intuition
• Maths behind svm’
• Loss Function
• Kernel trick
• Polynomial kernel, rbf and linear kernels
• SVM Regression
• Tuning the parameter
• GridSearch and RandomizedSearch
• SVM Regression
Hands On - Case Study SVM on Social network ADs and Gender recognition from voice dataset

Decision Tree
• Introduction to Decision Tree
• Homogeneity and Entropy
• Gini Index
• Information Gain
• Advantages of Decision Tree
• Preventing Overfitting
• Advantages And Disadvantages
• Plotting Decision Trees
• Plotting feature importance
• Regression using Decision Trees

Hands-On - Decision Tree on US Adult income dataset

Ensemble Learning
• Introduction to Ensemble Learning
• Bagging (Bootstrap Aggregation)
• Constructing random forests
• Runtime
• Case study on Bagging
• Tuning hyperparameters of random forest(GridSearch, RandomizedSearch)
• Measuring model performance
• Boosting
• Gradient Boosting
• Adaboost and XGBoost
• Case study on boosting trees
• Hyperparameter tuning
• Evaluating performance
• Stacking Models
Hands-On - Talking Data Ad Tracking Fraud Detection case study

Clustering

• Introduction to unsupervised learning
• Applications of Unsupervised Learning
• Kmeans Geometric intuition
• Maths Behind Kmeans
• Kmeans in presence of outliers
• Kmeans random initialization problem
• Kmeans++
• Determining the right k
• Evaluation metrics for Kmeans
• Case study on Kmeans
• Hierarchical Clustering
• Agglomerative and Divisive
• Denodgrams
• Case study on hierarchical clustering
• Segmentation
• Case Study on Segmentation
• DBSCAN - Density based clustering
• MinPts and Eps
• Core Border and Noise Points
• Advantages and Limitation of DBSCAN
• Case Study on DBSCAN clustering

Hands On - Applying Unsupervised models on Retail data and mall customer dataset

Dimensionality Reduction Techniques

• What are dimensions?
• Why is high dimensionality a problem ?
• Introduction to MNIST dataset with (784 Dimensions)
• Into to Dimensionality reduction techniques
• PCA (Principal Component Analysis) for dimensionality reduction
• t-sne (t-distributed stochastic neighbor embedding

Hands-on: Applying Dimensionality Reduction on MNIST data

• Introduction
• Markov Decision Process
• Expected Return
• Policy and Value Function
• Q-Learning
• Exploration vs Exploitation
• OpenAI Gym and python for Q-learning
• Training Q-Learning Agent
• Watching Q-Learning Play Games

Hands On - Working with OpenAI Gym and Q-Learning

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Machine Learning with Python Certification Projects

Project 1

1. Project Medical Treatment

• Introduction to problem statement playing with data
• Translating the problem to machine learning
• Playing and dealing with data
• Cleaning the data
• Train/Test Validation split
• Evaluation metric-log loss
• Building a worst model
• Evaluating a Worst Model
• Going back to data
• Preprocessing the data
• Feature engineering and Exploratory Data Analysis
• Building the model again
• Evaluating the model

Project 2

Taxi Demand Prediction in NewYork City

• Problem overview
• Setting up objectives
• Mapping to ML problem
• Working with Dask
• Getting familiar with data
• Choosing the evaluation metric
• Data Cleaning and Preprocessing
• Exploratory Data Analysis
• Treating the outliers
• Feature Engineering
• Applying different algorithms and evaluating the model
• Choosing the best model

Project 3

Quora Problem

• Problem formulation
• Setting objectives
• Preparing Data Description
• Playing with Data
• EDA - Basic Exploration
• Extracting Features
• Preprocessing the text
• Feature Analysis
• Visualizing using t-sne
• Applying Models
• Evaluating and Comparing the models

Project 4

AD click prediction

Ad CLICK PREDICTION

Project 5

NETFLIX MOVIE RECOMMENDATION

NETFLIX MOVIE RECOMMENDATION

Project 1

1. Project Medical Treatment

• Introduction to problem statement playing with data
• Translating the problem to machine learning
• Playing and dealing with data
• Cleaning the data
• Train/Test Validation split
• Evaluation metric-log loss
• Building a worst model
• Evaluating a Worst Model
• Going back to data
• Preprocessing the data
• Feature engineering and Exploratory Data Analysis
• Building the model again
• Evaluating the model

Project 2

Taxi Demand Prediction in NewYork City

• Problem overview
• Setting up objectives
• Mapping to ML problem
• Working with Dask
• Getting familiar with data
• Choosing the evaluation metric
• Data Cleaning and Preprocessing
• Exploratory Data Analysis
• Treating the outliers
• Feature Engineering
• Applying different algorithms and evaluating the model
• Choosing the best model

Project 3

Quora Problem

• Problem formulation
• Setting objectives
• Preparing Data Description
• Playing with Data
• EDA - Basic Exploration
• Extracting Features
• Preprocessing the text
• Feature Analysis
• Visualizing using t-sne
• Applying Models
• Evaluating and Comparing the models

Project 4

AD click prediction

Ad CLICK PREDICTION

Project 5

NETFLIX MOVIE RECOMMENDATION

NETFLIX MOVIE RECOMMENDATION

Certification In This Program

Codegnan Certification for Machine Learning

Machine Learning Certification

Training on different technologies provided by Codegnan is a set of blended learning models that brings classroom learning experience with its world-class LMS. We understand the effort of students; thus, as a token of motivation, our training is honored by top leading industries like Microsoft and HP. After the successful completion of your Machine Learning course, you will be awarded Codegnan’s certification.

Can I get a job with Machine Learning certification?

We can't even imagine what will happen with the technology we'll see in the coming years, but we know we already have a lack of skilled AI and machine-learning professionals. Only when we get people educated and put in the millions of AI jobs can the gap increase. If you want to be one of those experts, get certified the sooner you start your training you will be working in this exciting and rapidly changing field.

Codegnan Certification for Machine Learning

Machine Learning with Python Certification Trainer/s

SAIRAM UPPUGUNGLA
SAIRAM UPPUGUNGLA

7 YEARS+ EXPERIENCE IN DATA SCIENCE, DATA ANALYSIS - CEO AND FOUNDER OF CODEGNAN IT SOLUTIONS

He is a tech-expert with 7 years of industrial experience in Python, Data Analysis, Big Data, Machine Learning and NLP. He has 360 degrees of expertise in all these subjects. He is known for his practical approach to different real-time industrial problems. He is known for his great interest in helping students reach their true potential and scale greater heights. Believing in Problem-based teaching pedagogies, he left his job in Malaysia as a data engineer and came back to the newly born state to fill the void between students and the industry.

SAKETH REDDY KALLEPU
SAKETH REDDY KALLEPU

OPERATIONS HEAD AND DATA SCIENCE TRAINER CODEGNAN IT SOLUTIONS (OPC) PVT LTD

A Master in Computational Intelligence and also Data Science Consultant for Andhra Pradesh State Skill Development Corporation(APSSDC) blended with a passion for nurturing the meaning of education with technology. With expertise in Machine Learning, Data Analytics, Natural Language Processing, and Cloud Computing, he believes in teaching all functionalities to the core making his life's motto to train students to be a Data Scientist rather than a Data Engineer. With a vision of building a better society using technological innovation, he joined Codegnan to bring out and enrich the essence of Technical Education with industrial excellence. His ambition is to make Codegnan the Centre of Excellence towards LeadIndia 2020.

Machine Learning with Python Certification FAQs

The following aspirants can opt for Machine Learning training certification course: Programming Enthusiasts, Project Managers, Web Developers, Full Stack Developers, Software Developers, Software Engineers, Technical Lead Architects, Technology Enthusiasts, Machine Learning Enthusiasts, etc.

To enroll yourself in Machine Learning, one should have a strong foundation of analytics and logical reasoning.

Machine Learning is the application of artificial intelligence. Robots use machine learning algorithms to perform tasks and robots are the future. Since robots perform tasks more efficiently, companies around the world are implementing robotic techniques to improve productivity. As per the tech giant Google, machine learning is the future and this future is going to be bright.

After the successful completion of your Machine Learning Certification Course, you will be able to make your career advancement in artificial intelligence and natural language processing related tasks. You can be a machine learning engineer in a real manner and a quick job is what you need the most.

Features of Machine Learning like Modularity, Middle-level language, General-purpose language, Portable language, Profitability, Speed of compilation, Easy to learn, Structured programming language, Make smarter products, etc. make machine learning the most considerable and advantageous career in artificial intelligence. Education at Codegnan will assure you the ability to code AI models by the end of the Machine Learning training.

You will get 24*7 support and lifetime access to the LMS, where course material like presentations, installation guides & class recordings are available. Email support will always be there to clear your doubts.

We will be helping you get your dream job in Machine Learning by sharing your resume with various recruiters and assisting you with building your resume and preparing for the interviews.

In offline or classroom training, you will be given dedicated time and tools when you can implement the programs you have learned during the training.

If you want to enroll yourself in the Machine Learning training program, then payments can be made using any of the following options, and a receipt of the same will be issued to you right after the payment confirmation.
1. UPI
2. Visa Debit/Credit Card
3. American Express and Diners Club Card
4. Master Card
5. PayPal

The future of Machine Learning is exceptionally bright. The scope of artificial intelligence has led to successful ventures and developments in fields like software development, science, arts, business, education, and government administration.

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