Machine Learning by Examples using Scikit-Learn, Keras, TensorFlow, PyTorch, and OpenCV.

1. Google Colab Notebooks (Free Nvidia Tesla K80 GPU)
2. Sketcher using Keras/TensorFlow and QuickDraw-Dataset
3. Disease-Prediction using Machine Learning (Scikit-Learn)
4. Recruitment Matching using Machine Learning (Keras & Tesorflow)

• Step 1. Creating Folder on Google Drive or choose the default Colab Notebooks folder
• Step 2. Opening or Creating a `Colab Notebook
  • Openning
  • Google Drive: upload onto My Drive/machine-learning
  • Google Drive: My Drive/machine-learning/disease-diagnostic-from-symptoms/disease_symptoms_data_analysis_DecisionTree.ipynb > Open with > Colaboratory
  • Creating new Colab Notebook via Right click > More > Colaboratory
• Step 3. Setting Free GPU:
  • Google Colab: Edit > Notebook settings:
    • Runtime type: Python 3
    • Hardware accelerator: GPU
• Running or Importing Files with Google Colab

      from google.colab import drive
      drive.mount("/content/gdrive", force_remount=True)
  

  Note: Click the link, copy verification code and paste it to text box; then we can use /content/gdrive/My Drive/
• Install Python Module/Package

      # Install Excel/GoogleSheet Python module
      !pip3 install --upgrade -q gspread
      !pip3 install --upgrade -q xlrd
      
      # Install Keras
      !pip3 install -q keras
      !pip3 install torch torchvision
  

• Google Colab Notebooks

      # RAM & CPU
      !cat /proc/meminfo
      !cat /proc/cpuinfo
      # Restart Google Colab
      !kill -9 -1
  

• Note: 12-hour GPU limit is for a continuous assignment of VM.

• Upload file using gsutil command to GCS(Google Cloud Storage)

  # First, we need to set our project. Replace the assignment below
  #with your project ID.
  project_id = 'chatbotdemo-ai'
  !gcloud config set project {project_id}
  import uuid
  # Make a unique bucket to which we'll upload the file.
  # (GCS buckets are part of a single global namespace.)
  bucket_name = 'sample-bucket-' + str(uuid.uuid1())
  
  # Full reference: https://cloud.google.com/storage/docs/gsutil/commands/mb
  !gsutil mb gs://{bucket_name}
  # Copy the file to our new bucket.
  # Full reference: https://cloud.google.com/storage/docs/gsutil/commands/cp
  !gsutil cp trained_model.pkl gs://{bucket_name}/
  

• Upload file from google drive to GCS(Google Cloud Storage)

      This section demonstrates how to upload files using the native Python API rather than gsutil.
      This snippet is based on a larger example with additional uses of the API
      # The first step is to create a bucket in your cloud project.
      # Replace the assignment below with your cloud project ID.
      # For details on cloud projects, see:
      project_id = 'chatbotdemo-ai'
      # Authenticate to GCS.
      from google.colab import auth
      auth.authenticate_user()
  
      # Create the service client.
      from googleapiclient.discovery import build
      gcs_service = build('storage', 'v1')
  
      # Generate a random bucket name to which we'll upload the file.
      import uuid
      bucket_name = 'sample-bucket-' + str(uuid.uuid1())
  
      body = {
      'name': bucket_name,
      # For a full list of locations, see:
      # https://cloud.google.com/storage/docs/bucket-locations
      'location': 'us',
      }
      gcs_service.buckets().insert(project=project_id, body=body).execute()   
  

• Download file using gsutil command on GCS(Google Cloud Storage)

      # Download the file.
      !gsutil cp gs://{bucket_name}/trained_model.pkl /tmp/trained_model.pkl
      
      # Print the result to make sure the transfer worked.
      !cat /tmp/trained_model.pkl
  

• Download file from google drive to GCS(Google Cloud Storage)

      We repeat the download example above using the native Python API.
      # Authenticate to GCS.
      from google.colab import auth
      auth.authenticate_user()
  
      # Create the service client.
      from googleapiclient.discovery import build
      gcs_service = build('storage', 'v1')
  
      from apiclient.http import MediaIoBaseDownload
  
      with open('/content/gdrive/My Drive/trained_model.pkl', 'wb') as f:
      request = gcs_service.objects().get_media(bucket=bucket_name,
                                                  object='trained_model.pkl')
      media = MediaIoBaseDownload(f, request)
  
      done = False
      while not done:
          # _ is a placeholder for a progress object that we ignore.
          # (Our file is small, so we skip reporting progress.)
          _, done = media.next_chunk()
  
  

A simple tool that recognizes drawings and outputs the names of the current drawing. We will use Google Colab for training the model, and we will deploy & run directly on the browser using TensorFlow.js.

We will use a CNN to recognize drawings of different types. The CNN will be trained on the Quick-Draw Dataset.

• FastAI