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Learn Python, NumPy, Pandas, Scikit-learn, HDFS, ZooKeeper, Hive, HBase, NoSQL, Oozie, Flume, Sqoop, Spark, Spark RDD, Spark Streaming, Kafka, SparkR, SparkSQL, MLlib, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow, Convolutional & Recurrent Neural Networks, Autoencoders, Reinforcement and More
This Data Science Certification Program is a self-paced online course. This gives you complete freedom about your schedule and convenience.
This course has over 200 hours of video content. This consists of 5 courses (Big Data with Hadoop, Big Data with Spark, Python, Machine Learning, and Deep Learning).
Additionally, this course comes with our exclusive lab access to gain the much needed hands-on experience to solve the real-world problems.
Upon successfully completing the course, you will get the certificate from CloudxLab which you can use for progressing in your career and finding better opportunities.
1. Introduction to Linux
2. Introduction to Python
3. Hands-on using Jupyter on CloudxLab
4. Overview of Linear Algebra
5. Introduction to NumPy & Pandas
6. Quizzes, gamified assessments & projects
Statistical Inference, Types of Variables, Probability Distribution, Normality, Measures of Central Tendencies, Normal Distribution
Introduction to Machine Learning, Machine Learning Application, Introduction to AI, Different types of Machine Learning - Supervised, Unsupervised, Reinforcement
Machine Learning Projects Checklist, Frame the problem and look at the big picture, Get the data, Explore the data to gain insights, Prepare the data for Machine Learning algorithms, Explore many different models and short-list the best ones, Fine-tune model, Present the solution, Launch, monitor, and maintain the system
Training a Binary classification, Performance Measures, Confusion Matrix, Precision and Recall, Precision/Recall Tradeoff, The ROC Curve, Multiclass Classification, Multilabel Classification, Multioutput Classification
Linear Regression, Gradient Descent, Polynomial Regression, Learning Curves, Regularized Linear Models, Logistic Regression
Linear SVM Classification, Nonlinear SVM Classification, SVM Regression
Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Gini Impurity or Entropy, Regularization Hyperparameters, Regression, Instability
Voting Classifiers, Bagging and Pasting, Random Patches and Random Subspaces, Random Forests, Boosting, Stacking
The Curse of Dimensionality, Main Approaches for Dimensionality Reduction, PCA, Kernel PCA, LLE, Other Dimensionality Reduction Techniques
Deep Learning Applications, Artificial Neural Network, TensorFlow Demo, Deep Learning Frameworks
Installation, Creating Your First Graph and Running It in a Session, Managing Graphs, Lifecycle of a Node Value, Linear Regression with TensorFlow, Implementing Gradient Descent, Feeding Data to the Training Algorithm, Saving and Restoring Models, Visualizing the Graph and Training Curves Using TensorBoard, Name Scopes, Modularity, Sharing Variables
From Biological to Artificial Neurons, Training an MLP with TensorFlow’s High-Level API, Training a DNN Using Plain TensorFlow, Fine-Tuning Neural Network Hyperparameters
Vanishing / Exploding Gradients Problems, Reusing Pretrained Layers, Faster Optimizers, Avoiding Overfitting Through Regularization, Practical Guidelines
The Architecture of the Visual Cortex, Convolutional Layer, Pooling Layer, CNN Architectures
Recurrent Neurons, Basic RNNs in TensorFlow, Training RNNs, Deep RNNs, LSTM Cell, GRU Cell, Natural Language Processing
Efficient Data Representations, Performing PCA with an Undercomplete Linear Autoencoder, Stacked Autoencoders, Unsupervised Pretraining Using Stacked Autoencoders, Denoising Autoencoders, Sparse Autoencoders, Variational Autoencoders
Learning to Optimize Rewards, Policy Search, Introduction to OpenAI Gym, Neural Network Policies, Evaluating Actions: The Credit Assignment Problem, Policy Gradients, Markov Decision Processes, Temporal Difference Learning and Q-Learning, Learning to Play Ms. Pac-Man Using Deep Q-Learning
Churn the mail activity from various individuals in an open source project development team.
We start Machine Learning course with this end-to-end project. Learn various data manipulation, visualization and cleaning techniques using various libraries of Python like Pandas, Scikit-Learn and Matplotlib.
The MNIST dataset is considered as "Hello World!" of Machine Learning. Write your first classification logic. Starting with Binary Classification learn Multiclass, Multilabel, Multi-output classification and different error analysis techniques.
Build a model that takes a noisy image as an input and outputs the clean image.
IRIS dataset contains 3 classes of 50 instances each, where each class refers to a type of iris plant. The three classes in this dataset are Setosa, Versicolor, and Verginica. Learn Decision Trees, CART algorithm and Ensemble method. Then use Random Forest classifier to make predictions.
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. In this project, you build a model to predict which passengers survived the tragedy.
Build a model to predict the bikes demand given the past data.
Build a model to classify email as spam or ham. First, download examples of spam and ham from Apache SpamAssassin’s public datasets and then train a model to classify email.
In this project, you will build a basic neural network to classify if a given image is of cat or not.
Download images of various animals and then download the latest pretrained Inception v3 model. Run the model to classify downloaded images and display the top five predictions for each image, along with the estimated probability.
Build a model to classify clothes into various categories in Fashion MNIST dataset.
This is a time series prediction task: you are given snapshots of polarimetric radar values and asked to predict the hourly rain gauge total.
Sentiment analysis of "Iron Man 3" movie using Hive and visualizing the sentiment data using BI tools such as Tableau
Process the NSE (National Stock Exchange) data using Hive for various insights
Analyze MovieLens data using Hive
Generate movie recommendations using Spark MLlib
Derive the importance of various handles at Twitter using Spark GraphX
Churn the logs of NASA Kennedy Space Center WWW server using Spark to find out useful business and devops metrics
Write end-to-end Spark application starting from writing code on your local machine to deploying to the cluster
Real-time analytics dashboard for an e-commerce company using Apache Spark, Kafka, Spark Streaming, Node.js, Socket.IO and Highcharts
Our course is exhaustive and the certificate rewarded by us is proof that you have taken a big leap in Python.
The knowledge you have gained from working on projects, videos, quizzes, hands-on assessments and case studies gives you a competitive edge.
Highlight your skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.
If you are unhappy with the product for any reason, let us know within 7 days of purchasing or upgrading your account, and we'll cancel your account and issue a full refund. Please contact us at email@example.com to request a refund within the stipulated time. We will be sorry to see you go though!
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