Python for Data Scientist Training Course – Machine Learning

What the specialist says

From scripting to data, this Python track is a straight bridge to real work opportunities. This Python for Data Scientist Training Course – Machine Learning helps you do that faster.

Ajay Narang Python & Automation Trainer

Master Python for Data Science and Machine Learning: Launch Your Career! DevLustro Academy is a leader in the Data Science domain, offering a comprehensive program to help you harness, interpret, and utilize data in unprecedented ways. Course was selected for our collection of top-rated courses trusted by businesses worldwide.

Our Core Highlights

World Class Instructor
World Class Instructor Mentorship from global experts
1:1 with Industry Expert
1:1 with Industry Expert Personalised coaching tailored to you
Global Hiring Network
Global Hiring Network 400+ hiring partners around the world
Average Salary Hike
Average Salary Hike 55% average hike for our alumni

Course Description

Master Python for Data Science and Machine Learning: Launch Your Career! Elysium Academy is a leader in the Data Science domain, offering a comprehensive program to help you harness, interpret, and utilize data in unprecedented ways. Course was selected for our collection of top-rated courses trusted by businesses worldwide.

Python for Data Scientist Training Course – Machine Learning

Data Analyst Course

Course Details

Advance in object-oriented programming (OOP) and error handling.

Utilize powerful libraries like NumPy and Pandas for data manipulation.

Visualize data effectively using Matplotlib and Seaborn.

Implement machine learning algorithms with Scikit-learn.

Explore deep learning techniques using TensorFlow.

High demand for data scientists with Python and machine learning skills.

Python's versatility and extensive libraries are crucial for data science.

Machine learning's integration in various industries is expanding.

Advancements in AI and machine learning offer continuous learning opportunities.

Graduates can become Data Scientists, Machine Learning Engineers, and AI Specialists.

Course Goals

  • Master Python fundamentals: variables, data types, loops, and functions.

Future Scope of this Course

  • Industry Based Projects
  • Recognized worldwide

Our Career Service

  • Personalized coordinator.
  • Trainer feedback.
  • Trainer availability post sessions.
  • Get your staff certified.
  • Certificate from governing bodies.

Our Training Program Benefits

  • Hands on assignment
  • Curriculum that focuses on the learner.
  • Live, interactive training by DevLustro experts.
  • Curriculum aligned with current industry practices.
  • Portfolio / project support for real-world use.
01Chapter-1 Getting Started
  • 01.01History and Features of Python
  • 01.02Basic Syntax
  • 01.03Variables and Data Types
  • 01.04Operators
  • 01.05Conditional, Loop & Control Statements
  • 01.06Functions
  • 01.07Random Modules
  • 01.08Data Science
  • 01.09Data Science vs. Data Scientist
  • 01.10Future scope of data scientist
  • 01.11How to link python with DS
  • 01.12Data Mining
  • 01.13Data Sets
  • 01.14Python packages
  • 01.15Data Collections
  • 01.16Data Preprocessing
  • 01.17Data Visualization
  • 01.18Data Modelling
  • 01.19Pandas
  • 01.20Numpy
  • 01.21Scipy
  • 01.22Sckit
  • 01.23NLTK
  • 01.24Pandas
  • 01.25Numpy
  • 01.26Scipy
  • 01.27Sckit-Learn
  • 01.28NLTK
  • 01.29Matplot
  • 01.30Keras
  • 01.31Tensorflow
  • 01.32PyTorch
02Chapter-2 Data Collection and Preprocess
  • 02.01Numpy
  • 02.02Create Numpy arrays
  • 02.03Numpy operations
  • 02.04Numpy for statistical operations
  • 02.05Handling Missing/Fill-na/Replace Values
  • 02.06Drop Column/row
  • 02.07Label Encoding
  • 02.08One-Hot Encoding
  • 02.09Reshaping
  • 02.10Data operations
  • 02.11Data frame creations
  • 02.12Statistical functions in data operations
  • 02.13Merging and joining data frame
  • 02.14Concatenate
  • 02.15What is Data Normalization?
  • 02.16Standard Scalar
  • 02.17Min-Max Scalar
  • 02.18Hands on standard scalar
  • 02.19Hands on min-max scalar
  • 02.20How to collect input data
  • 02.21Read CSV Data
  • 02.22Read JSON Data
  • 02.23Read XLS Data
  • 02.24Read HTML Contents
  • 02.25View Data
03Chapter-3 Visualizations
  • 03.01Plotting with matplotlib
  • 03.02Bar charts
  • 03.03Pie charts
  • 03.04Scatter plots
  • 03.05Box plots
  • 03.06Histogram
  • 03.07Bubble Chart
  • 03.08Heat maps
  • 03.09Graph/line graph
  • 03.10Geographical data
  • 03.11Plotting with seaborn
  • 03.12Histogram with grid
  • 03.13Distplot
  • 03.14Pairplot
  • 03.15Scatter plots
  • 03.16Box plots
  • 03.17Lmplots
  • 03.18Histogram
  • 03.19Challenge: Visualize titanic dataset
  • 03.20Feature Extraction
  • 03.21Feature Selection
  • 03.22Pairplot
  • 03.23Scatter plots
  • 03.24Box plots
  • 03.25Lmplots
  • 03.26Histogram
  • 03.27Challenge: Visualize titanic dataset
  • 03.28open() mode
  • 03.29read() mode
  • 03.30write() mode
  • 03.31append() mode
04Chapter-4 Machine Learning
  • 04.01Al vs ML vs DL
  • 04.02Application of machine learning
  • 04.03How do machine learns
  • 04.04Types of Machine learning
  • 04.05Supervised Learning
  • 04.06Un-supervised Learning
  • 04.07What is Native Bayes?
  • 04.08Types of Naive Bayes
  • 04.09IRIS Classification
  • 04.10Data Processing
  • 04.11Train and create model
  • 04.12Performance Estimation
  • 04.13Analyse and create confusion matrix
  • 04.14Ordinary Least Square and Regression Errors
  • 04.15Data Processing
  • 04.16Train and Test Model
  • 04.17Test the model and Predict Y Values
  • 04.18R-Squared and its Importance
  • 04.19Score and Get coefficients
  • 04.20Calculate RMSE (Root Mean Squared Error)
  • 04.21Plot the predictions
  • 04.22What is SVM?
  • 04.23SVM kernel types
  • 04.24Problem: IRIS Classification
  • 04.25Data Processing
  • 04.26Train and create model
  • 04.27Performance Estimation
  • 04.28Analyse and create confusion matrix
05Chapter-5 Algorithms
  • 05.01What is Logistic Regression?
  • 05.02Problem: Heart Disease Prediction
  • 05.03Build Model
  • 05.04Performance Estimation
  • 05.05Analyse and create confusion matrix
  • 05.06What is decision tree?
  • 05.07Decision Tree Parameters
  • 05.08Problem: IRIS Classification
  • 05.09Data Processing
  • 05.10Train and create model
  • 05.11Evaluate Model
  • 05.12What is random forest?
  • 05.13Ensemble Learning
  • 05.14Bagging and Boosting Classifiers
  • 05.15Problem: Cardio Vascular Disease
  • 05.16lmplementation
  • 05.17Evaluate Model
  • 05.18What is KNN?
  • 05.19KNN parameters
  • 05.20Problem: Cardio Vascular Disease
  • 05.21Data collection and preprocessing
  • 05.22lmplementation
  • 05.23Evaluate Model
06Chapter-6 Evaluation Metrics
  • 06.01Evaluate Accuracy
  • 06.02Classification metrics
  • 06.03What is Threshold and PHAPTER Adjusting Thresholds
  • 06.04AUC ROC Curve
  • 06.05Why to reduce dimensions and Importance of PCA?
  • 06.06Steps to calculate PCA
  • 06.07Implementation of PCA
  • 06.08Visualization
  • 06.09What is Ridge regression?
  • 06.10Implement Ridge Regression
  • 06.11Plot Ridge Regression Line
  • 06.12Lasso Regression or L1 Penalty
  • 06.13lmplement lasso Regression
  • 06.14Plot lasso Regression Line
  • 06.15What is over fitting?
  • 06.16How to avoid over fitting?
  • 06.17What is under fitting?
  • 06.18How to avoid under fitting?
  • 06.19What is Cross Validation?
  • 06.20How Cross Validation Works
  • 06.21Prepare for Cross Validation
  • 06.22Parameter and implementation of Cross Validation
  • 06.23Understand the results of Cross Validation)
  • 06.24Hands On - Analyse the Result
07Chapter-7 Hypertunning Model and Clustering
  • 07.01What is Hyper parameter Tuning?
  • 07.02Grid Search and Randomized Search Approach
  • 07.03GridSearchCV Parameters Explained
  • 07.04Create GirdSearchCV Object
  • 07.05Fit data to GridSearchCV
  • 07.06Understand GridSearchCV Results
  • 07.07GridSearchCV using Logistic Regression
  • 07.08GridSearchCV using Support Vector
  • 07.09Randomized Search using random forest
  • 07.10Select Best Model
  • 07.11Randomized Search
  • 07.12Model Selection Summary
  • 07.13What is Clustering?
  • 07.14How the clusters are formed?
  • 07.15Problem Understanding: Customer Segmentation
  • 07.16Get, Visualize and Normalize the data
  • 07.17Import KMeans and Understand Parameters
  • 07.18Understanding KMeans++ Initialization Method
  • 07.19Create Clusters
  • 07.20Visualize and create different number of clusters
  • 07.21Understand Elbow Method to Decide number of Cluster
  • 07.22Implement Elbow Method
08Chapter-8 NLP and Recommendation System
  • 08.01What is NLP
  • 08.02Application of NLP
  • 08.03Remove punctuation
  • 08.04Tokenize
  • 08.05Remove stop words
  • 08.06Stem words
  • 08.07Lemmatize
  • 08.08Padding
  • 08.09Part of Speech Tagging
  • 08.10Name Entity Relationship
  • 08.11Sentiment Analysis
  • 08.12Read and preprocess data
  • 08.13NLP techniques
  • 08.14Tokenization
  • 08.15Tokens to vectors
  • 08.16Naive Bayes
  • 08.17Logistic Regression
  • 08.18Sentiment Analysis
  • 08.19Evaluate Metrics
  • 08.20What is Recommendation System?
  • 08.21How Do Recommendation Works?
  • 08.22Types of Recommendation
  • 08.23What is Content based recommendation?
  • 08.24Advantages and drawbacks
  • 08.25Hands on Content based recommendation code
  • 08.26What is Collaborative filtering?
  • 08.27Advantages and drawbacks
  • 08.28Kinds of collaborative filtering
  • 08.29Hands on collaborative filtering code
  • 08.30What is hybrid recommendation?
  • 08.31Advantages and Drawbacks
09Chapter-9 Deep Learning
  • 09.01What is Neuron and Artificial Neural Network?
  • 09.02How Artificial Neural Network works?
  • 09.03What is Keras and Tensorflow?
  • 09.04What is a Tensor in Tensorflow?
  • 09.05Installing Keras, backend and Tensorflow
  • 09.06Keras Model Building and Steps
  • 09.07Layers - Overview and Parameters
  • 09.08Activation Functions
  • 09.09Layers - Softmax Activation Function
  • 09.10What is a Loss Function?
  • 09.11Cross Entropy Loss Functions
  • 09.12Optimization - What is it?
  • 09.13Optimization - Gradient Descent
  • 09.14Optimization - Stochastic Gradient Descent
  • 09.15Optimization - SGD with Momentum
  • 09.16Optimization - SGD with Exponential Moving Average
  • 09.17Optimization - Adagrad and RMSProp for learning rate decay m. Optimization - Adam
  • 09.18Initializers - Vanishing and Exploding Gradient Problem
  • 09.19Layers - Initializers explained
  • 09.20Problem Understanding: Disease Prediction
  • 09.21Read and process the data
  • 09.22Define the Keras Neural Network Model
  • 09.23Compile the Keras Neural Network Model
  • 09.24Evaluate the result
10Chapter-10 Projects
  • 10.01Problem understanding: Loan Approval Prediction
  • 10.02Read and preprocess data
  • 10.03Data splitting
  • 10.04Classification - Naïve Bayes
  • 10.05Classification – Support Vector Machine
  • 10.06Classification - Random Forest
  • 10.07Classification - Logistic Regression
  • 10.08Train the model
  • 10.09Evaluate performance metrics
  • 10.10Visualization using matplotlib and seaborn
  • 10.11Problem Understanding : Zomato Restaurant Review
  • 10.12Read and preprocess data
  • 10.13Data Splitting
  • 10.14Classification - Naïve Bayes (Multinomial)
  • 10.15Classification - Support Vector Machine
  • 10.16Classification - Random Forest
  • 10.17Classification – Decision Tree
  • 10.18Classification - Logistic Regression
  • 10.19Train the model
  • 10.20Evaluate performance metrics
  • 10.21Visualization using matplotlib and seaborn
  • 10.22Problem Understanding : Product Recommendation
  • 10.23Read and preprocess data
  • 10.24Collaborative filtering
  • 10.25Content based filtering
  • 10.26Recommend the product
  • 10.27Evaluate metrics

What is the Python for Data Scientist Course - Machine Learning offered by DevLustro Academy?

The Python for Data Scientist Course - Machine Learning at DevLustro Academy covers the essentials of data science and machine learning, including Python programming, data visualization, and model building using popular libraries like TensorFlow and scikit-learn.

What makes DevLustro Academy the best Python for Data Scientist training center near me?

DevLustro Academy is the top choice for Python for Data Scientist training due to its comprehensive curriculum, practical projects, and expert faculty who ensure you gain the skills needed to excel in data science and machine learning.

What are the prerequisites for enrolling in the Python for Data Scientist - Machine Learning course?

Basic knowledge of programming and statistics is recommended. Familiarity with Python is helpful but not required

How will this course benefit my career in data science?

You will gain hands-on experience with Python programming, machine learning algorithms, and data analysis techniques, which are essential skills for data scientist roles.

What kind of projects will I work on during the course?

You will work on industry-relevant projects that involve data cleaning, analysis, visualization, and implementing machine learning models using real-world datasets.

Will I receive a certificate upon completion of the course?

Yes, you will receive a certificate of completion that verifies your proficiency in Python for Data Science and Machine Learning.

Are there opportunities for hands-on practice and assignments?

Yes, the course includes hands-on assignments and projects designed to reinforce your learning and practical application of Python and machine learning concepts.

Is this course suitable for beginners with no prior experience in Python?

Yes, this course is designed for beginners. It covers Python programming from basics to advanced topics specifically tailored for data science and machine learning.

Who are the instructors for this course?

Pandas has built-in methods likefillna(),dropna(), andinterpolate()to handle missing data by replacing, removing, or interpolating missing values, respectively.

What tools and libraries will I learn as part of the course?

You will learn popular Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and TensorFlow for data manipulation, analysis, visualization, and machine learning.

Will I have access to the course materials after completion?

Yes, you will have access to the course materials, including videos, slides, and code samples, even after completing the course.

Can I get support from instructors after completing the course?

Yes, you will have access to post-session support from instructors to help you with any questions or clarifications related to the course content.

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Python for Data Scientist Training Course – Machine Learning

Duration: 90 Hours

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Python for Data Scientist Training Course – Machine Learning

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